Selecting a cloud model today goes beyond just technical considerations; it becomes a genuine strategic lever. Whether you choose a public, private, or hybrid offering, each option impacts data security, cost control, governance, and the scalability of your IT systems.

For Swiss organizations operating in regulated or multi-site sectors, this decision determines operational performance and compliance. This article offers a pragmatic overview of the three cloud architectures, illustrated with real-life examples from Swiss companies. You will gain the insights you need to align your cloud strategy with your business goals, with complete peace of mind.

Public Cloud: Flexibility, Agility, and Cost Optimization

The public cloud provides exceptional flexibility with ready-to-use managed services. This approach enables you to launch projects quickly while significantly reducing infrastructure expenses.

Elasticity and Instant Scalability

Thanks to the native elasticity of the public cloud, you can adjust compute, storage, and network capacity in just a few clicks. This agility is essential for handling traffic spikes or seasonal marketing campaigns without hardware procurement delays.

Major providers’ multi-tenant partnerships guarantee virtually limitless scaling, without physical intervention, leveraging CloudOps best practices. IT teams can thus focus on application architecture rather than server management.

For a startup in its launch phase or an innovation project, this responsiveness allows rapid validation of business hypotheses and immediate resource deallocation once the need disappears. Consumption aligns precisely with demand.

Pay-As-You-Go Pricing Model

Usage-based billing eliminates any upfront hardware investment by turning infrastructure into a flexible operational expense and facilitating migration to the cloud. You pay only for the capacity you actually use, with reservation options or per-second billing.

Example: A Swiss e-commerce SME migrated its front end to a public provider to handle year-end peaks. This transition showed that real-time capacity adjustment reduced its monthly costs by 40% compared to static on-site hosting.

This model encourages experimenting with new cloud services, such as artificial intelligence or analytics, without committing heavy upfront budgets. Expense control becomes more predictable and manageable.

Vendor Lock-In Risks and Compliance Requirements

Standardized public cloud environments can limit customization or integration of specific proprietary components. Switching providers often requires rethinking certain architectures, increasing dependency risk.

Moreover, the physical location of data centers directly affects compliance with local regulations (Swiss Federal Act on Data Protection – FADP, General Data Protection Regulation – GDPR). It is essential to verify precisely where your data is hosted and which certifications each region holds.

Highly regulated sectors may also require advanced encryption mechanisms and proof of residence. Without complete control of the infrastructure, ensuring auditability and traceability can become complex.

Private Cloud: Control, Compliance, and Customization

The private cloud provides full control over the infrastructure, ensuring strict isolation of sensitive data. This architecture is custom-designed to meet the most stringent security and performance requirements.

Total Control and Data Isolation

In a private environment, each instance is dedicated and isolated, eliminating multi-tenancy risks. You define network rules, encryption mechanisms, and data segmentation policies with precision.

Example: A Swiss university hospital deployed an on-premises private cloud to host its patient records. This solution demonstrated that complete isolation can fully comply with FADP and HIPAA standards while maintaining consistent performance for critical applications.

This granular control reassures executive management and compliance teams, providing full traceability of access and modifications made to the infrastructure.

Investments and Maintenance

Implementing a private cloud requires an initial budget for server and storage acquisition and virtualization tools, as detailed in cloud hosting vs. on-premises. Maintenance, hardware refresh, and internal monitoring costs must also be anticipated.

Specialized skills—whether in DevOps, security, or networking—are often required. This internal expertise, however, ensures rapid incident response and fine-tuned environment customization.

Advanced Customization

Private clouds enable you to configure the environment according to very specific business requirements, whether advanced network QoS policies, hyperconverged architectures, or tailored containerization solutions.

Companies can deploy proprietary tools, optimized database engines, or analytics solutions tailored to their processes without compromise.

This design freedom facilitates legacy system integration and avoids functional compromises often imposed by public cloud standards.

{CTA_BANNER_BLOG_POST}

Hybrid Cloud: Balancing Agility and Control

The hybrid cloud combines private and public environments to intelligently distribute workloads based on criticality. This approach offers the flexibility of the public cloud while preserving control over sensitive data on-premises.

Optimal Application Placement

With a hybrid cloud, each application resides in the most suitable infrastructure. High-variability services operate in the public cloud, while critical systems remain private.

Example: A Swiss financial institution uses a private cloud for sensitive transaction processing and a public cloud for near real-time reporting and analytics. This setup ensures back-office performance while optimizing the costs of analytical workloads.

This distribution also allows rapid testing of new services without impacting day-to-day operations or compromising strategic data security.

Resilience Strategies and Business Continuity

Multi-environment redundancy enhances fault tolerance. If an internal data center fails, services can failover to the public cloud within minutes using automated replication mechanisms.

Disaster recovery plans leverage distributed infrastructures, reducing recovery time objectives (RTOs) and ensuring service continuity, as described in our change management guide.

For organizations with high-availability requirements, this hybrid approach provides a structured response to risks associated with unexpected outages or security incidents.

Integration Challenges and Multi-Environment Governance

Managing identities, security policies, and billing across multiple clouds requires advanced governance tools. Orchestrating workflows and unified monitoring is essential to avoid operational fragmentation.

IT teams must develop multi-cloud skills to manage distributed architectures, automate deployments, and ensure configuration consistency.

Implementing consolidated dashboards and centralized alerting rules remains a prerequisite for controlling costs and maintaining a global performance overview.

How to Choose the Right Cloud Model for Your Organization

The right choice depends on your business requirements, regulatory obligations, and internal capabilities. An informed decision balances security, cost, scalability, customization, and available skills.

Security and Compliance

The nature of the data—personal, financial, or sensitive—often dictates the required level of isolation. Regulated industries enforce strict standards for encryption, data residency, and auditability.

Based on your FADP, GDPR, or sector-specific obligations, integrate the necessary technical and organizational measures from the design phase.

Cost Model and Financial Optimization

The CAPEX-to-OPEX ratio varies by model. Public cloud emphasizes OPEX and flexibility, while private cloud demands significant upfront investment but offers stable billing.

For hybrid cloud, analysis involves placing critical workloads on a fixed-cost foundation while varying operational expenses according to scaling needs.

Accurate financial flow modeling and forecasting are essential for selecting the most cost-effective option over your infrastructure’s lifecycle.

Scalability and Performance Needs

Stable, predictable workloads may suit a private cloud, while highly variable services require public cloud elasticity. Identify traffic peaks and anticipate activity surges.

For web and mobile applications with fluctuating traffic, public cloud remains the benchmark. Critical transactional systems demand consistent performance, often best served by private or hybrid environments.

Also evaluate latency and bandwidth requirements to determine the model that ensures optimal response times for your users.

Customization and Control Level

When complex network configurations, hardware optimizations, or specific development are necessary, private cloud proves most suitable. On-premises or dedicated-hosting offers complete design freedom.

Public cloud nevertheless provides advanced configuration options within a standardized framework. The choice depends on the balance between deployment speed and business adaptation needs.

In a hybrid setup, you can dedicate a private segment for bespoke components and offload the rest to the public cloud, leveraging the best of both worlds.

Technological Maturity and Internal Skills

Project success relies on your teams’ ability to design, deploy, and operate the chosen infrastructure. DevOps, security, and cloud governance skills are critical.

If your organization is new to the cloud, structured support will facilitate best practice adoption and gradual skill building. Conversely, an experienced IT department can leverage open-source tools and avoid vendor lock-in.

Assess your maturity in these areas to select a model that is both ambitious and realistic, ensuring a controlled transition.

Adopt the Cloud Strategy That Drives Your Business Growth

Public, private, or hybrid—each model carries its advantages and constraints. Public cloud stands out for rapid deployment and elasticity, private cloud for full control and compliance, and hybrid for combining the strengths of both.

Your decision should rest on a detailed analysis of security requirements, budget, scalability needs, customization level, and internal maturity. This approach ensures an infrastructure aligned with your operational and strategic objectives.

Our experts are available to guide you through this process, craft a tailored cloud roadmap, and deploy a robust, scalable, and compliant architecture that meets your business challenges.

Discuss your challenges with an Edana expert

In digital environments where performance and latency make all the difference, AWS DynamoDB remains a preferred choice for Swiss companies. However, when read request volumes rise, even DynamoDB can exhibit latencies that fall short of near-real-time expectations.

It is in this context that DynamoDB Accelerator (DAX) comes into play—a managed, distributed, in-memory cache by AWS capable of reducing the latency of simple operations. This article details the key mechanisms of DAX, its benefits and constraints, before comparing it with open-source and cloud-native alternatives. It also offers criteria to balance latency, consistency, technological openness, and total cost of ownership.

When to Use AWS DAX

DAX significantly speeds up simple read operations on DynamoDB by leveraging a multi-Availability Zone distributed in-memory cache. These performance gains are optimal for read-heavy workloads such as e-commerce or real-time personalization.

Understanding the three caching strategies built into DAX enables you to quickly determine if this managed service meets your project’s latency and consistency requirements.

How DAX Works and Its Multi-AZ Architecture

The DAX cluster is deployed across multiple Availability Zones to ensure high availability and fault tolerance. Each node keeps data in RAM, enabling millisecond response times. This architecture eliminates disk storage for reads, offering speed unmatched by direct DynamoDB queries.

Communications between the application and the DAX cluster occur through the standard DynamoDB API, with no major code changes required. The client extension integrates easily into Java, .NET, or Python environments, while maintaining compatibility with GetItem, Query, and Scan requests. This approach simplifies adding a cache without overhauling the existing architecture.

In case of a node failure, DAX automatically reroutes requests to the remaining instances, ensuring continued service. The cluster can be scaled up or down on the fly to adapt to traffic changes, while AWS manages maintenance and security updates, relieving the operations team from cache administration tasks.

Built-in Caching Strategies

The read-through strategy queries the DAX cache first for every read operation. If the data is missing, DAX fetches it from DynamoDB, stores it in memory, and returns it to the application. This drastically reduces the number of direct database calls, lightening the load on DynamoDB.

The write-through strategy ensures consistency between the cache and the database. On each write, DAX simultaneously propagates the update to DynamoDB and updates its local cache. This real-time synchronization prevents divergence, at the cost of a slight increase in write latency.

The write-back strategy, on the other hand, allows a delay before data is persisted in DynamoDB. Writes are held in the cache for a configurable period, then batch-replicated to the database. This mode reduces write pressure on DynamoDB but must be used cautiously to avoid data loss in case of disaster.

Typical Read-Intensive Use Cases

E-commerce sites with large product catalogs benefit from an in-memory cache to speed up product page loading, even during traffic spikes. Similarly, real-time personalization platforms leverage DAX to display recommendations or promotions without introducing visible latency for the user.

Example: A mid-sized e-commerce company integrated DAX for its product recommendation flows. Before DAX, response times for dynamic queries exceeded 25 milliseconds, affecting the customer journey. After enabling the cache, average latency dropped to 4 milliseconds, while cutting DynamoDB read capacity unit costs by 60%. This example shows that a managed service can quickly boost performance without a complete infrastructure overhaul.

In practice, DAX is especially relevant when serving a high volume of GetItem or Query requests on partitioned tables. In these scenarios, the cache acts as a memory-powered turbocharger, freeing the direct query pool to DynamoDB and optimizing overall infrastructure cost.

Constraints and Limitations of DAX to Consider

Despite its efficiency for simple reads, DAX has functional limitations and technical incompatibilities that restrict its universal adoption. Some advanced operations and secondary indexes are not supported, leading to complex workarounds.

Moreover, using DAX can introduce consistency risks and increased operational complexity, while adding recurring costs for an additional managed service.

Unsupported Operations and Incompatibilities

DAX does not support UpdateItem, BatchWriteItem, BatchGetItem, or scans with complex filters. Developers often have to implement additional application logic to work around these restrictions, which increases code maintenance overhead.

Similarly, certain local or global secondary indexes do not work with DAX, forcing table design revisions or multiple direct queries to DynamoDB. This can result in a hybrid pattern where some queries bypass the cache, complicating the read-write management scheme.

Example: A Swiss public organization had counted on DAX for its event logs with TTL on items. Since DAX does not support automatic in-memory TTL deletions, the team had to deploy an external purge process. This implementation highlighted that the native DAX ecosystem does not cover all needs and sometimes requires additional components to ensure data compliance and freshness.

Consistency Risks and Architectural Complexity

Although attractive for reducing write load, the write-back strategy can introduce a temporary delta between the cache and the source of truth. In the event of a cluster reboot or extended failover, some data may be lost if it has not been synchronized. This fragility necessitates monitoring and recovery mechanisms.

Adding a third-party managed service also requires revisiting network topology, managing IAM authentication or security groups, and setting up specific metrics to monitor cache health. The infrastructure becomes heavier and demands advanced DevOps skills to operate continuously without service disruption.

Overall, DAX remains a specialized component that must be integrated carefully into already complex architectures. Teams spend time documenting where the cache is used, orchestrating autoscaling, and controlling consistency during simultaneous data updates.

Additional Costs and Vendor Lock-In

Using DAX incurs additional costs proportional to the number of nodes and instance types chosen. For a 4-node, multi-AZ cluster, monthly fees can add up quickly, not to mention the impact on network bills in a private VPC. To estimate total cost of ownership, see our article on Capex vs Opex in Digital Projects: What It Means for Swiss Companies.

Relying on DAX strengthens a company’s dependency on a specific AWS service that is less flexible than an open-source cache deployed on EC2 or Kubernetes. Migrating later to an alternative solution involves complex changes at both code and infrastructure levels, representing a non-negligible transition cost.

Therefore, financial trade-offs must include Total Cost of Ownership, taking into account managed service fees, associated operational costs, and vendor lock-in risks. In some scenarios, a self-hosted solution or a hybrid approach may be more attractive in the medium to long term.

{CTA_BANNER_BLOG_POST}

Scalable, Less Locked-In Alternatives to Consider

To maintain technological flexibility and avoid severe vendor lock-in, other open-source and cloud-native solutions offer comparable or superior performance depending on the context. Redis or KeyDB, ElastiCache, and more scalable databases allow architecture adaptation to business requirements.

Architectural patterns like CQRS with event sourcing or distributed application caches also help separate read and write concerns, optimizing both scalability and maintainability.

Redis, KeyDB, and ElastiCache for a Flexible In-Memory Cache

Redis and its fork KeyDB provide a versatile in-memory solution capable of storing complex data structures and handling high concurrency. Their active communities ensure frequent updates, enhanced security, and compatibility with various languages and frameworks. For an overview of database systems, see our Guide to the Best Database Systems for Swiss Companies.

ElastiCache, AWS’s managed version of Redis, strikes a balance between reduced maintenance and optimization freedom. Snapshots, read scaling, cluster modes, and Redis Streams support are all features that allow fine-tuning based on business needs.

Unlike DAX, Redis natively supports disk persistence, TTL management, transactions, and Lua scripting, offering either strong or eventual consistency depending on configuration. This flexibility lets you tailor the cache to varied use patterns and minimize application workarounds.

Implementing CQRS and Event Sourcing Patterns

The CQRS (Command Query Responsibility Segregation) pattern separates read and write paths, allowing each to be optimized independently. Leveraging an event-driven architecture, commands feed a persistent event stream that can be replicated to a read-optimized datastore, such as Redis, ScyllaDB, or a relational database with read replicas.

Combining CQRS with event sourcing, state changes are stored as events. This approach facilitates auditing, replaying, and reconstructing historical states. The read system can then supply ultra-fast materialized views without directly impacting the transactional database.

Companies can handle millions of events per second while maintaining excellent read responsiveness. The clear separation of responsibilities simplifies schema evolution and horizontal scalability, avoiding overloading transactional tables with analytical queries or wide scans.

Cloud-Native Databases for Global Scalability

PostgreSQL with read replicas, offered by RDS or Aurora, provides a robust relational foundation while offloading part of the read workload. Combined with sharding and partitioning, it can handle large data volumes without resorting to a separate cache for every simple operation.

For massively distributed workloads, NoSQL databases like ScyllaDB or Cassandra ensure uniform latency and fast writes thanks to their decentralized architecture. These open-source solutions can be deployed on Kubernetes or in managed cloud mode, minimizing lock-in risks.

Adopting these complementary databases requires adjusting application logic and data workflows but offers a broader innovation path for companies seeking cost control and autonomy over their tech stack.

Criteria for Balancing Latency, Consistency, and Technological Openness

Every project must define its priorities in terms of response time, consistency guarantees, and degree of technological freedom. This trade-off phase determines the architecture’s longevity and total cost of ownership.

Partnering with a strategic advisor capable of proposing a contextual approach and integrating open-source components, managed services, and custom development makes all the difference.

Defining Key Indicators for the Trade-Off

The analysis should focus on the target latency in milliseconds, the volume of concurrent requests to support, and the required consistency level (strong, eventual, or configurable). These parameters drive the choice between an in-memory cache, a distributed database, or a mix of both.

Total Cost of Ownership should include the direct cost of managed services or licenses, operational maintenance costs, and long-term migration expenses. Additionally, indirect costs related to architectural complexity and vendor dependency risk must be considered.

Finally, technological flexibility—the ability to switch solutions without a major overhaul—is an essential factor for organizations looking to control their roadmaps and anticipate future market evolution.

Hybrid Architecture and Modularity

A modular approach combines an in-memory cache for critical reads and a distributed database for persistence. Microservices or serverless functions can query the most appropriate component based on the transactional context and performance objectives.

Clearly defined responsibilities promote reuse of open-source modules, integration of managed services, and custom development of specific modules. This hybrid architecture limits change propagation and simplifies scaling by adding targeted nodes.

With this modularity, teams can test various technology combinations, compare results, and adjust cache or database configurations without impacting the entire system.

Contextual Approach and Strategic Support

Defining an optimal solution relies on a precise assessment of business context, data volume, traffic peaks, and security requirements. This audit phase enables recommending a mix of DAX, Redis, CQRS patterns, or distributed databases according to identified priorities.

Example: A Swiss company in financial services sought ultra-fast response for near-real-time dashboards. After evaluation, the team favored a managed Redis cluster paired with a CQRS pattern over a DAX cluster. This choice ensured strong consistency while guaranteeing scalability and controlled total cost of ownership. This example demonstrates the importance of thorough contextual analysis and strategic partnership in guiding the decision.

Choosing the Right Caching Strategy for DynamoDB

AWS DAX is a high-performance accelerator for read-intensive use cases, but its limited feature coverage and additional cost reserve it for specific scenarios. Open-source alternatives like Redis or KeyDB, more open managed services, and CQRS patterns offer greater flexibility and better control over Total Cost of Ownership. The trade-off between latency, consistency, and technological openness should be based on precise indicators and contextual analysis.

Discuss your challenges with an Edana expert

The rise in cyberattacks in Switzerland is redefining the selection criteria for a cloud ERP. Beyond a simple functional evaluation, the decision now hinges on solution architecture, governance, and resilience. SMEs and mid-sized enterprises must question the provider’s cyber maturity, data location and sovereignty, the shared responsibility model, and the degree of integration with the existing ecosystem.

An expert systems integrator can audit these risks, design a secure architecture (IAM, MFA, encryption, DRP/BCP) and manage a migration without compromising control or continuity. This insight helps both executive and IT teams align digital transformation with long-term structural security.

Assess the Cyber Maturity of the Cloud Provider

The robustness of a cloud ERP is measured by the provider’s ability to prevent and remediate vulnerabilities. Verifying certifications, internal processes, and penetration testing gives a clear view of its cyber maturity.

Certification and Standards Audit

Reviewing certifications (ISO 27001, SOC 2, Swiss IT Security Label – LSTI) provides a concrete indicator of the controls in place. These frameworks formalize risk management, access control, and data protection practices.

A manufacturing SME commissioned an audit of its three potential cloud providers. The exercise revealed that only one maintained an annual penetration-testing program, demonstrating an ability to quickly identify and patch vulnerabilities.

This approach highlighted the importance of choosing a partner whose security governance relies on regular external audits.

Vulnerability Management Process

Each provider should document a clear cycle for detecting, prioritizing, and remediating vulnerabilities. Best DevSecOps Practices strengthen the effectiveness of these processes.

This responsiveness shows that rapid patching and transparent vulnerability reporting are essential for ongoing resilience.

Provider’s Internal Governance and Responsibilities

The presence of a dedicated cybersecurity steering committee and a Chief Security Officer ensures strategic oversight of cyber matters. Formal links between IT, risk, and compliance must be established.

This underscores the importance of confirming that security is not just a technical department but a forward-looking pillar embedded in governance.

Ensuring Data Sovereignty and Localization

Choosing the right data centers and encryption mechanisms determines both legal and technical resilience. Swiss and EU legal requirements mandate full control over hosted data.

Choosing Data Centers in Switzerland

Physically locating servers in Swiss data centers ensures compliance with the Federal Act on Data Protection (FADP). It avoids foreign jurisdiction risks and reassures oversight authorities.

This choice shows that a nationally based, geographically redundant infrastructure strengthens service continuity and the confidentiality of sensitive information.

Regulatory Compliance and Data Protection Act

The upcoming Revised Federal Act on Data Protection (rFADP) strengthens transparency, notification, and security obligations. Cloud ERP vendors must demonstrate comprehensive reporting and traceability capabilities.

This highlights the need to favor a provider offering automated reports to respond quickly to authorities and auditors.

Encryption and Key Management

Encrypting data at rest and in transit, coupled with secure key management (HSM or KMS), protects information from unauthorized access. Allowing clients to hold and control their own keys increases sovereignty.

A financial services SME required an encryption scheme where it held the master keys in a local HSM. This configuration met extreme confidentiality requirements and retained full control over the key lifecycle.

This real-world example shows that partial delegation of key management can satisfy the highest standards of sovereignty and security.

{CTA_BANNER_BLOG_POST}

Understanding the Shared Responsibility Model and Ensuring Resilience

Migrating to a cloud ERP implies a clear division of responsibilities between provider and client. Implementing a Disaster Recovery Plan (DRP), a Business Continuity Plan (BCP), and a Zero Trust approach strengthens continuity and defense in depth.

Clarifying Cloud vs. User Responsibilities

The Shared Responsibility Model defines who manages what—from physical infrastructure, hypervisors, and networking, to data and access. This clarification prevents grey areas in the event of an incident.

During an audit, a mid-sized healthcare enterprise misinterpreted its administrative scope and left inactive accounts unprotected. Redefining the responsibility framework explicitly assigned account management, updates, and backups.

This shows that a clear understanding of roles and processes prevents security gaps during a cloud migration.

Implementing DRP/BCP

A Disaster Recovery Plan (DRP) and a Business Continuity Plan (BCP) must be tested regularly and updated after each major change. They ensure rapid recovery after an incident while minimizing data loss.

This underlines the importance of practical exercises to validate the relevance of resilience procedures.

Adopting a Zero Trust Approach

The Zero Trust principle mandates that no component—internal or external to the network—is trusted by default. Every access request must be verified, authenticated, and authorized according to a granular policy.

This demonstrates that segmentation and continuous access control are major levers for strengthening cloud security.

Verifying Integration and Operational Security

The security perimeter encompasses all interfaces, from IAM to proactive alerting. Smooth, secure integration with the existing information system (IS) ensures performance and continuity.

Integration with IAM and MFA

Consolidating identities through a centralized IAM solution prevents account silos and duplicates. Adding MFA significantly raises the access barrier.

This case shows that unified identity management and strict MFA enforcement are indispensable for controlling critical access.

Secure Interfaces and Data Flows

APIs and web services must adhere to secure standards (OAuth2, TLS 1.3) and be protected by API gateways. Implementing middleware and IDS/IPS strengthens malicious traffic detection and filtering.

This approach demonstrates the necessity of segmenting and protecting each flow to prevent compromise risks.

Proactive Monitoring and Alerting

A centralized monitoring system (SIEM) with real-time alerts enables detection of abnormal behavior before it becomes critical. Operations should be supervised 24/7.

Implementing KPIs to Govern Your IS illustrates the importance of continuous monitoring and immediate response capability to contain incidents.

Secure Your ERP Cloud Migration by Ensuring Continuity and Performance

This overview has highlighted the need to assess provider cyber maturity, data sovereignty, responsibility allocation, operational resilience, and secure integration. Each of these dimensions ensures that your ERP migration becomes a structuring project aligned with risk and continuity objectives.

Faced with these challenges, support from cybersecurity and cloud architecture experts—capable of auditing, designing, and orchestrating each step—is a guarantee of control and sustainability. Our team assists organizations in defining, implementing, and validating best practices for data protection and governance.

Discuss your challenges with an Edana expert

The migration of your ERP to the Cloud transforms this management tool into a critical pillar of your overall security. With centralized financial, HR, production, and supply chain data, the attack surface expands significantly.

To protect the integrity and confidentiality of your information system, it is imperative to rethink access governance, Zero Trust segmentation, encryption, monitoring, and business continuity. In this article, discover the essential best practices for securing a Cloud ERP—whether off the shelf or custom-built—and understand why collaborating with an expert systems integrator makes all the difference.

Access Governance and Zero Trust for Cloud ERP

Implementing fine-grained access governance ensures that only legitimate users interact with your ERP. Zero Trust segmentation limits the spread of any potential intrusion by compartmentalizing each service.

Developing a Granular Identity and Access Management Policy

Defining an Identity and Access Management (IAM) policy starts with an accurate inventory of every role and user profile associated with the ERP. This involves mapping access rights to all critical functions, from payroll modules to financial reporting.

An approach based on the principle of least privilege reduces the risk of excessive permissions and makes action traceability easier. Each role should have only the authorizations necessary for its tasks, with no ability to perform unauthorized sensitive operations.

Moreover, integrating an open-source solution that meets your standards avoids vendor lock-in while offering flexibility for future evolution. This adaptability is essential to quickly adjust access during organizational changes or digital transformation projects.

MFA and Adaptive Authentication

Enabling Multi-Factor Authentication (MFA) adds a robust barrier against phishing and identity-theft attempts. By combining multiple authentication factors, you ensure that the user truly owns the account.

Adaptive authentication adjusts the verification level based on context—location, time, device type, or typical behavior. Access from an unknown device or outside normal hours triggers a stronger authentication step.

This reactive, context-based approach fits perfectly within a Zero Trust strategy: each access request is dynamically evaluated, reducing the risks associated with stolen passwords or sessions compromised by an attacker.

Privilege Management and Zero Trust Segmentation

At the heart of Zero Trust strategy, network segmentation isolates access to different ERP modules. This containment prevents an intrusion in one service from spreading to the entire Cloud environment.

Each segment must be protected by strict firewall rules and undergo regular integrity checks. Deploying micro-segments restricts communications between components, thereby shrinking the attack surface.

One manufacturing company recently implemented Zero Trust segmentation for its Cloud ERP. After the audit, it discovered obsolete administrator accounts and reduced inter-service exposure by 70%, demonstrating the effectiveness of this approach in limiting lateral threat movement.

Encryption and Hardening of Cloud Environments

Systematic encryption protects your data at every stage, whether at rest or in transit. Hardening virtual machines and containers strengthens resistance against attacks targeting operating systems and libraries.

Encrypting Data at Rest and in Transit

Using AES-256 to encrypt data at rest on virtual disks ensures a robust level of protection against physical or software breaches. Keys should be managed via an external Key Management System (KMS) to avoid internal exposure.

For exchanges between the ERP and other applications (CRM, BI, supply chain), TLS 1.3 ensures confidentiality and integrity of the data streams. End-to-end encryption should be activated on APIs and real-time synchronization channels.

Encryption keys must be regularly rotated and stored in a dedicated Hardware Security Module (HSM). This practice limits the risk of key theft and complies with the Swiss Federal Act on Data Protection (FADP) and the EU General Data Protection Regulation (GDPR).

Hardening Operating Systems and Containers

Hardening starts by reducing the attack surface: removing unnecessary services, applying a minimal kernel configuration, and promptly installing security updates. Each container image should be built from packages verified by a vulnerability scanner.

Enforce strong security policies for Docker or Kubernetes (Pod Security Policies, AppArmor, SELinux) to prevent unauthorized code execution. Controlling read/write permissions and forbidding privileged containers are essential to avoid privilege escalation.

A Swiss logistics company faced multiple attack attempts on its test containers. After hardening the images and implementing a CI/CD pipeline with automated vulnerability checks, it cut critical alerts by 90% and secured its entire production environment.

Securing Mobile and Bring Your Own Device (BYOD) Environments

The rise of BYOD means treating mobile endpoints as potential attack vectors. The Cloud ERP should be accessible only through applications managed by Mobile Device Management (MDM).

Local data encryption, screen-lock policies, and remote wipe capabilities in case of loss or theft ensure sensitive information remains safe. Anonymous or non-compliant access must be blocked via conditional access policies.

Combining MDM and IAM allows delegation of certificate and access-profile management, ensuring that no ERP data is permanently stored on an unsecured device.

{CTA_BANNER_BLOG_POST}

Continuous Monitoring and API Security

Implementing 24/7 monitoring with SIEM and XDR enables early detection and correlation of incidents before they escalate. Securing APIs, the junction points of your applications, is crucial to prevent abuse and code injection.

SIEM and XDR Integration

Aggregating logs from your Cloud ERP, network, and endpoints into a Security Information and Event Management (SIEM) solution facilitates correlated event analysis. Alerts should be tailored to the functional specifics of each ERP module. For guidance, see our cybersecurity for SMEs guide.

API Call Monitoring and Anomaly Detection

Every API call must be authenticated, encrypted, and subject to rate limits to prevent denial-of-service attacks or mass data extraction. API access logs provide a valuable history to trace actions and identify malicious patterns.

Behavioral analysis, based on normalized usage models, reveals abnormal calls or injection attempts. Learn how API-first integration strengthens your data flows.

DevSecOps Automation for Application Security

Integrating security tests into the CI/CD pipeline (SAST, DAST scans, automated penetration tests) ensures every ERP code change is validated against vulnerabilities. Read our article on the enhanced software development lifecycle (SDLC) to secure your pipeline.

GitOps workflows combined with mandatory pull-request policies allow for code reviews and automated attack simulations on each change. This process prevents misconfigurations, the primary source of Cloud ERP incidents.

This DevOps-security synergy reduces delivery times while raising reliability. Teams operate in a mature environment where secure automation is the norm, not an added burden.

Redundancy, DRP/BCP, and Regulatory Compliance

Implementing a redundant architecture and recovery plans ensures business continuity in the event of an incident. Compliance with the FADP and GDPR builds trust and avoids penalties.

Redundant Architecture and Resilience

A distributed infrastructure across multiple Cloud regions or availability zones guarantees high availability of the ERP. Data is replicated in real time, minimizing potential information loss if a data center fails.

Automated failover, orchestrated by an infrastructure controller, maintains service without noticeable interruption to users. This mechanism should be regularly tested through simulated failure drills to verify its effectiveness.

Using stateless containers also promotes scalability and resilience: each instance can be routed and recreated on the fly, with no dependence on local state that could become a failure point.

Disaster Recovery and Business Continuity Planning (DRP/BCP)

The Disaster Recovery Plan (DRP) outlines technical procedures to restore the ERP after a disaster, while the Business Continuity Plan (BCP) organizes the human and organizational resources to maintain a minimum service level.

These plans must align with the criticality of business processes: financial transactions, inventory management, or payroll. For more details, consult our guide to designing an effective DRP/BCP step by step.

Periodic updates to the DRP/BCP incorporate ERP evolutions, architectural changes, and lessons learned. This exercise prevents surprises and secures the company’s operational resilience.

FADP, GDPR Compliance, and Audits

Centralizing data in a Cloud ERP requires enhanced protection of personal data. The Swiss Federal Act on Data Protection (FADP) and the EU General Data Protection Regulation (GDPR) impose proportionate security measures: encryption, access traceability, and retention policies.

A periodic audit by an independent third party validates procedure adherence and identifies gaps. Audit reports provide tangible proof of compliance for regulators and clients.

Documenting approaches and recording security tests facilitate responses to regulatory inquiries and reinforce stakeholder confidence. Effective document governance is an asset in preventing sanctions.

Strengthen Your Cloud ERP Security as a Competitive Advantage

Securing a Cloud ERP requires a combination of Cloud architecture, DevSecOps, automation, encryption, and continuous monitoring. Each domain—access governance, hardening, APIs, redundancy, and compliance—contributes to building a resilient and compliant foundation.

In the face of increasingly complex threats, partnering with an experienced provider enables you to audit your environment, remediate vulnerabilities, adopt secure practices, and train your teams. This comprehensive approach ensures business continuity and stakeholder trust.

Discuss your challenges with an Edana expert

In today’s manufacturing environment, an ERP is no longer just a repository for financial and logistical data. It has become the technological heart of a connected value chain, driving production, maintenance and the supply chain in real time. By combining modular cloud architectures, microservices and open APIs, companies build a scalable foundation that hosts predictive AI services, real-time analytics and industrial IoT. This digital transformation delivers agility, transparency and continuous optimization.

For industrial small and medium-sized enterprises (SMEs) and mid-tier companies, the challenge is to build a data-driven cloud ERP platform capable of integrating with the Manufacturing Execution System (MES), Product Lifecycle Management (PLM), Customer Relationship Management (CRM) and Business Intelligence (BI) ecosystems, while supporting the ongoing innovation of Industry 4.0.

Cloud Architecture and Microservices: The Foundation of ERP 4.0

Hybrid cloud architectures and microservices form the basis of a scalable, resilient ERP. They ensure elasticity, fault tolerance and independence from evolving technologies.

Public, Private and Hybrid Cloud

Manufacturers adopt hybrid models that combine public cloud for peak workloads and private cloud for sensitive data. This dual approach ensures regulatory compliance while offering unprecedented elasticity.

Operationally, hybrid cloud lets you distribute workloads: critical, legacy processes reside in a controlled environment, while innovation or AI developments run on public environments on demand.

Such a setup reduces the risk of vendor lock-in by enabling gradual service migration and abstracting infrastructure through open-source multi-cloud management tools.

Modularity and Microservices

Breaking down functionality into microservices isolates domains—inventory, production, finance, maintenance—into independent services. Each microservice can be updated, redeployed or scaled on its own.

Thanks to orchestrators and containers, these microservices deploy rapidly under centralized monitoring, ensuring performance and availability to Industry 4.0 standards.

Implementation Example

An electronics component SME migrated its ERP to a hybrid cloud to host operational data on-premises and AI services in a public environment. This architecture reduced downtime by 30% and enabled automatic scaling during new product launches, validating the benefits of a modular, cloud-native ERP platform.

Security and Compliance

In a hybrid model, security relies on next-generation firewalls, encryption of data at rest and in transit, and granular identity management via open-source solutions.

Zero-trust architectures reinforce protection of ERP-API interfaces, reducing attack surfaces while maintaining business-critical data access for IoT and analytics applications.

By adopting DevSecOps practices, teams embed security into microservice design and automate vulnerability testing before each deployment.

Data Orchestration and Industrial IoT

Integrating IoT sensors and real-time streams turns the ERP into a continuous automation platform. Instant collection and processing of operational data optimize production and maintenance.

IoT Connectivity and Edge Computing

Industrial sensors record temperature, vibration or flow continuously. With edge computing, this data is filtered and preprocessed locally, reducing latency and bandwidth usage.

IoT streams are then sent to the cloud ERP via secure gateways, ensuring consistency of production data and archiving of critical metrics.

This distributed infrastructure automatically triggers restocking workflows, machine calibrations or maintenance alerts based on predefined thresholds.

Real-Time Ingestion and Processing

Event platforms (Kafka, MQTT) capture IoT messages and publish them to processing pipelines. Real-time ETL microservices feed the ERP and analytical modules instantly.

This orchestration provides live KPIs on overall equipment effectiveness, quality variances and production cycles, all displayed on dashboards accessible from the ERP.

Correlating IoT data with work orders and maintenance history optimizes scheduling and reduces scrap.

Predictive Maintenance

From collected time series, predictive AI models assess equipment failure probabilities. Alerts are generated directly in the ERP, triggering work orders and real-time procurement of spare parts.

This approach significantly reduces unplanned downtime and improves line availability, while optimizing maintenance costs by focusing only on necessary interventions.

Feedback loops continually refine the algorithms, improving forecast accuracy and adapting tolerance thresholds to real-world operating conditions.

Industrial Case Example

A machine-tool production unit deployed vibration and current sensors on its spindles. IoT-edge processing detected misalignment before any machine stoppage, cutting maintenance costs by 25% and extending equipment lifespan by 15%. This case illustrates the power of an IoT-connected ERP to secure production.

{CTA_BANNER_BLOG_POST}

AI and Real-Time Analytics in the ERP

Embedded predictive and generative AI in the ERP enhances decision-making and automates high-value tasks. Real-time analytics deliver clear insights into operational and strategic performance.

Predictive AI for the Supply Chain

Machine learning algorithms forecast product demand from order history, market trends and external variables (seasonality, economic conditions).

These forecasts feed procurement planning functions, reducing stockouts and minimizing overstock.

The cloud ERP incorporates these predictions into purchasing workflows, automatically placing supplier orders based on adaptive rules and providing real-time KPI dashboards.

Generative AI for Design and Documentation

Natural Language Processing (NLP) models automatically generate technical datasheets, training materials and compliance reports from product and process data stored in the ERP.

This accelerates documentation updates after each configuration change, ensuring consistency and traceability of information.

An integrated virtual assistant within the ERP allows users to ask questions in natural language and instantly access procedures or key metrics.

Intelligent Reporting and Dynamic Dashboards

The ERP’s built-in analytics engines provide custom dashboards for each function—production, finance, supply chain. Visualizations update by the second via real-time streams.

Proactive alerts flag critical deviations, such as delivery delays or energy spikes, enabling teams to act before performance is impacted.

These dashboards use configurable, exportable widgets accessible on desktop or mobile, fostering cross-disciplinary collaboration.

Process Optimization Example

A medical device manufacturer integrated a predictive AI engine into its ERP to adjust assembly lines based on demand forecasts. Service levels rose by 12% and logistics costs fell by 8%, demonstrating the direct impact of real-time AI on operational performance.

Integration and Interoperability via APIs and Ecosystems

Open, secure APIs enable the cloud ERP to interface with MES, PLM, CRM and e-commerce platforms. Removing silos ensures a continuous information flow and a unified view of the product lifecycle.

API-First and Security

An API-first strategy exposes every ERP function as a RESTful web service or GraphQL endpoint. Business developers can consume or extend these services without modifying the core system.

Implementing API gateways and OAuth 2.0 policies secures data access while providing monitoring and traceability of exchanges between systems.

This approach avoids bottlenecks and vendor lock-in by relying on open, non-proprietary standards.

Interoperability with MES, PLM, CRM and E-Commerce

The PLM supplies product data (BOM, specifications) to the ERP and receives production feedback to enrich future releases. The MES synchronizes work orders and reports shop-floor metrics in real time.

The CRM feeds customer and order information into the ERP for automated invoicing and optimized contract management. E-commerce platforms connect to manage inventory, dynamic pricing and promotions.

This multi-system orchestration eliminates duplicate entries, reduces errors and ensures data consistency at every step of the value chain.

Transform Your ERP into an Industry 4.0 Innovation Engine

Combining a modular cloud ERP, microservices architecture, IoT streams and real-time AI creates a continuous automation and innovation platform. By connecting the ERP to the MES, PLM, CRM and BI ecosystems through secure APIs, manufacturers gain agility, performance and predictability.

Projects must remain contextual, avoid vendor lock-in and favor open source to ensure long-term scalability and security. A hybrid, data-driven approach delivers fast ROI and a foundation ready to absorb future technological and business evolutions.

Our experts are available to design, integrate or modernize your cloud ERP and orchestrate your Industry 4.0 architecture. Together, let’s turn your information systems into growth and competitiveness levers.

Discuss your challenges with an Edana expert

Serverless edge computing is redefining the way modern applications are designed by combining serverless execution with data proximity. This approach pushes application logic as close as possible to end users—whether in browsers, connected devices, or remote sites. In contexts where every millisecond counts, ultra-responsive architectures become essential for streaming, gaming, massive IoT, industrial operations, and embedded AI.

Serverless Edge for Ultra-Responsiveness

Serverless edge computing delivers minimal latency by moving code execution nearer to end users. Edge functions eliminate the need for a permanent server infrastructure.

This convergence removes bottlenecks and accelerates real-time interactions while simplifying scaling without compromising performance.

An Ultra-Responsive Paradigm

The serverless edge model is built on functions deployed at global points of presence. Each request is handled locally, dramatically reducing network latency. Response times often drop from hundreds of milliseconds to a few dozen—or even under five milliseconds when deployment is optimized, particularly for massive Industrial IoT.

By removing the need to route through a centralized server, this architecture is ideally suited for applications requiring instantaneous feedback. It also accommodates event-driven use cases and frequent interactions, such as recommendation engines or embedded conversational agents.

A video streaming platform migrated its personalization functions to a local edge network. Average latency was quartered, significantly enhancing perceived quality for users.

Instant Scalability Without Infrastructure Management

Serverless removes server management and static resource allocation. Each function activates on demand, responding to events generated by users or systems.

This mechanism supports unexpected traffic spikes without the cost of idle infrastructure. New instances spin up in milliseconds and terminate as soon as processing completes.

IT teams can focus on business logic rather than server capacity planning. Operational costs become directly proportional to actual usage, avoiding expenses tied to inactive resources.

Use Case: Real-Time Streaming

In media and entertainment, any interruption or buffering frustrates audiences. Serverless edge provides a critical advantage by refreshing metadata and adjusting delivery profiles locally.

A media company implemented edge functions to dynamically recalculate resolution and content recommendations close to viewing areas. This local distribution reduced rebuffering by 70%, markedly improving retention and satisfaction.

Latency Reduction and Data Sovereignty

Edge computing brings processing power close to data collection points and end users. Critical applications benefit from near-source processing.

Additionally, localizing processing ensures regulatory compliance and data sovereignty. Each region can adhere to its legal requirements.

Proximity of Computation to End Users

Deploying functions on an edge network mechanically shortens packet journeys. Real-time tasks, such as embedded analytics and anomaly detection, execute locally without routing to a central data center.

Industrial scenarios illustrate this need perfectly: sensor data analysis must be instantaneous to trigger critical alerts. Reaction times often remain below thresholds that determine safety and operational performance.

A machine-tool manufacturer deployed on-site microfunctions to filter and preprocess data streams from sensors. This edge filtering reduced data volume sent to the cloud by 85%, while guaranteeing reaction times below 10 ms.

Local Compliance and Regulatory Adherence

Data privacy and localization requirements are tightening worldwide. By processing certain operations at the edge, only aggregated data leaves the local infrastructure, fulfilling legal obligations and ensuring compliance.

For international organizations, this approach standardizes architecture while adapting information flows to each country’s regulatory framework. Edge processing strengthens data governance without proliferating silos.

The modularity offered by serverless edge allows encryption and masking rules to be deployed directly at the entry point, ensuring continuous, centralized compliance across distributed workflows.

Practical Case: Industrial Operations

In an automated production environment, failures must be detected as close to the equipment as possible to avoid line stoppages. Edge functions run predictive maintenance algorithms locally, continuously analyzing noise, vibration, and temperature.

A major manufacturing firm deployed serverless extensions on IoT gateways to run diagnostics without cloud roundtrips. Maintenance alerts were generated in under 5 ms, reducing unplanned incidents by 30%.

An energy operator implemented a smart meter monitoring system across a wide territory. Readings were concentrated at certain times, causing significant traffic peaks.

{CTA_BANNER_BLOG_POST}

Flexibility, Performance, and Cost Optimization

Serverless edge computing enables pay-as-you-go pricing that optimizes IT spending. Costs are controlled through millisecond billing and automatic idle mode.

Performance remains consistent even under peak loads, as each point of presence scales automatically without manual configuration.

Transit Cost Optimization

By processing some requests locally, load on inter-regional links and central data centers decreases. Cloud ingress and egress charges are thus significantly reduced.

For organizations with massive data volumes, this reduction directly impacts the monthly bill. Heavy or repetitive computations can run at the edge, sending only essential results to the cloud core.

Serverless billing granularity ensures every millisecond of compute is valued, with no fees for idle or inactive resources. This encourages a highly optimized event-driven architecture.

Elasticity for Variable Workloads

Applications facing seasonal fluctuations or event-driven spikes fully benefit from instant scaling. Edge functions replicate automatically where demand is highest.

No predictive capacity setup is needed: the system adapts in real time, ensuring service continuity during marketing campaigns or special events.

This also applies to mobile use cases: geolocation and real-time tracking apps remain performant in crowded areas without manual infrastructure adjustments.

Example: IoT Application with Variable Traffic

An energy operator deployed a smart meter monitoring system across a wide territory. Readings peaked at certain hours, generating significant traffic.

By deploying edge functions on regional routers, each reading is aggregated and analyzed locally before being forwarded to the cloud. Transfer costs dropped by 60%, and the platform remained responsive even during daily peak readings.

This example demonstrates how combining serverless and edge simultaneously meets performance requirements and budgetary control in a massive IoT environment.

Strategic Impact and Hybrid Ecosystems

Serverless edge computing reshapes how application distribution is envisioned, promoting a distributed and resilient architecture. Native redundancy increases fault tolerance.

By harmoniously integrating cloud, edge, and serverless, organizations gain strategic agility. Hybrid environments become a catalyst for continuous innovation.

Distributed Architecture and Global Resilience

A distributed topology balances load and minimizes risk surface. If one point of presence fails, functions reroute automatically to another node, ensuring frictionless service continuity.

Updates can be deployed section by section, validated locally before wider propagation, reducing regression risks. Serverless deployment granularity enables rapid, secure iteration.

Combining a multi-regional edge with a central cloud backbone orchestrates workloads by criticality and sensitivity to latency or local regulations.

Hybrid Cloud + Edge + Serverless Integration

Hybrid architectures unify development and operations around APIs and events. Cloud services handle heavy processing, storage, and orchestration, while the edge executes real-time logic.

This functional segmentation reduces vendor lock-in risk while leveraging cloud offerings for non-latency-sensitive tasks. Developers can reuse the same code across different environments.

The CI/CD pipeline spans from source code to edge points of presence, ensuring end-to-end consistency and traceability of deliveries.

Embrace Serverless Edge Computing for Competitive Advantage

Serverless edge computing marks a turning point in modern application design and deployment. By eliminating infrastructure management, bringing processing closer to users, and adopting pay-as-you-go pricing, this model delivers ultra-responsive, resilient experiences.

Organizations are encouraged to reassess traditional cloud architectures and progressively adopt a hybrid model combining cloud, edge, and serverless. This transition ensures optimized performance, local compliance, and strategic agility—vital for staying competitive in a world where real-time and operational efficiency are key differentiators.

Our experts are ready to explore your use cases, define a tailored roadmap, and support your journey toward serverless edge maturity.

Discuss your challenges with an Edana expert

In a context where innovation must combine agility and robustness, Swiss companies face unprecedented challenges. Business requirements evolve faster than internal resources can keep pace, technical complexity is growing, and the pressure to launch new services in record time is intensifying.

Rather than a mere cost-saving measure, IT outsourcing proves to be a strategic catalyst for accelerating digital transformation, accessing specialized expertise, and strengthening resilience against cyber threats. By relying on an external partner, it becomes possible to turn internal constraints into sustainable competitive advantages and optimize the organization’s overall performance.

Accelerating Innovation and Scaling Up

IT outsourcing allows you to immediately integrate specialized expertise to drastically reduce development cycles. It offers progressive scaling capacity without impacting internal resources.

Optimizing Time-to-Market

Thanks to outsourcing, the development cycle enjoys a turbo boost. Dedicated, agile teams rely on proven practices such as continuous integration and automated deployment to deliver features in weeks or even days. This surge in speed requires no immediate increase in internal staff or a heavy budget allocation.

This gain in responsiveness is even more significant when the partner favors a modular, open-source architecture. The absence of vendor lock-in ensures controlled scalability, while leveraging existing components speeds up implementation without sacrificing quality or performance.

By breaking down silos across the value chain, this rapid approach promotes collaboration among product, design, and engineering teams, enabling earlier validation of business assumptions and adjustments to the roadmap based on concrete user feedback.

Scalability on Demand

By entrusting the management of cloud environments and microservices to an external provider, the company can increase or decrease server capacity in real time. This flexibility is essential to absorb traffic spikes or handle high-traffic marketing campaigns without risk of saturation.

The use of serverless solutions or container orchestrators, implemented in a hybrid model, ensures precise resource sizing and billing aligned with actual usage. Operating costs are thus optimized, while maintaining high availability and controlled response times.

Internal teams can then focus on optimizing business code and enhancing user experience rather than on the operational management of infrastructure. This refocusing helps free up time and strengthen the organization’s adaptability to strategic challenges.

Example of a Swiss Industrial Company

A mid-sized industrial SME enlisted an external partner to support the deployment of a new production-monitoring platform. In less than three months, the external team set up a Kubernetes cluster, CI/CD pipelines, and microservices operating in flexible mode.

This project demonstrated that an expert partner can reduce time-to-production by 60% compared to a fully in-house approach. End-of-month load peaks are now handled automatically and without manual intervention, ensuring optimal operational continuity.

The initiative also highlighted the value of a hybrid collaboration: advanced technical expertise complemented the in-house team’s business knowledge, facilitating a skills transfer and a progressive increase in autonomy for the IT department.

Accessing Rare and Specialized Expertise

Outsourcing opens the door to specialized skills in cloud, AI, and cybersecurity, which are often scarce in the local market. It enables deploying expert profiles without the constraints of lengthy and costly recruitment processes.

Cloud and AI Specialists at Your Fingertips

The rise of cloud and artificial intelligence technologies demands highly focused knowledge. An internal resource may require several months to become operational, or even a year depending on the complexity of architectures and algorithms.

By partnering with a specialized provider, access to data scientists, cloud architects, or DevOps engineers is immediate. These experts are often available in nearshore mode, ensuring cultural and time-zone proximity to streamline team collaboration.

This approach facilitates rapid implementation of proof of concept (PoC), optimization of machine learning models, and integration of robust data pipelines, while maintaining control over technology choices.

Leveraging open source combined with tailored know-how ensures full transparency and maximum adaptability, without vendor dependency.

Strengthening Cybersecurity

Threats evolve constantly, and vulnerability detection requires advanced skills in pentesting, forensics, and real-time monitoring. Such profiles are often lacking internally, as they are highly sought after across many sectors.

An outsourced team brings proven processes and tools: automated scans, penetration tests, centralized log management, and 24/7 alerting. They anticipate risks and deliver clear reports to guide strategic decisions.

Flexibility of Nearshore and Onshore Contracts

Outsourcing contracts often offer hybrid models combining local and nearshore resources. This flexibility allows for quick adjustment of team size according to the project phase.

Nearshore services offer competitive hourly rates without sacrificing quality, thanks to experts trained in agile methods and open-source technologies. Onshore engagement, on the other hand, ensures direct coordination for critical phases and governance checkpoints.

A customized partnership typically includes progressive ramp-up clauses, jointly defined performance indicators, and a light governance framework to ensure continuous alignment.

This balance between proximity and cost optimization contributes to faster project delivery while maintaining strategic control over mobilized skills.

{CTA_BANNER_BLOG_POST}

Creating an Agile Model Focused on Business Value

Entrusting part of your IT to an expert partner frees internal teams from operational constraints, enabling them to refocus on strategy, innovation, and value creation.

Refocusing Teams on Core Business

By delegating repetitive technical tasks—such as environment maintenance or update management—IT leadership can concentrate on strategic objectives. Dedicated teams handle day-to-day operations, while the IT department steers the roadmap and innovation, adopting an agile model.

This reduces turnover, as profiles develop business skills rather than being absorbed by operational routine. Project managers can devote more time to designing new services and improving internal processes.

Implementing an agile model where the provider acts as a natural extension of the organization promotes tight alignment between business priorities and technical deliverables, without coordination overload.

This refocusing fully aligns with the Edana approach, which values scalable, modular solutions designed to last and adapt to specific business challenges.

Agile Collaboration and Lightweight Governance

Agile methods applied with an external provider are based on short sprint cadences, regular reviews, and dynamic feature prioritization. This lightweight governance avoids long validation cycles and bottlenecks.

Agile rituals, such as daily stand-ups and sprint demos, ensure constant visibility into progress and facilitate decision-making. Teams remain engaged and can make rapid adjustments.

This optimal setup enables delivering MVPs (Minimum Viable Products) in just a few iterations, quickly testing business hypotheses, thereby limiting risks and ensuring optimal time-to-market.

Strengthening Resilience and Business Continuity

IT outsourcing includes 24/7 monitoring, proactive incident management, and disaster recovery plans. It ensures an always-available, protected infrastructure.

Proactive Monitoring and Detection

Implementing continuous monitoring with open-source or cloud-native tools allows immediate detection of performance or security anomalies. Alerts configured on critical thresholds trigger automatic or manual actions depending on urgency.

Business Continuity and Disaster Recovery Plans

Outsourcing contracts often include business continuity plans (BCPs) and disaster recovery plans (DRPs), validated through regular drills. These simulations ensure that critical processes can be restored within hours—or even minutes—after a major incident.

The partner handles data replication, geographic redundancy, and failover orchestration. Automated, encrypted backups provide a high level of security without overburdening internal teams.

This discipline also involves periodic plan reviews based on evolving risks and technologies, ensuring ongoing compliance with international standards and regulatory requirements.

Infrastructure Governance and Compliance

Strong infrastructure governance relies on clear role definitions, documented procedures, and regular configuration reviews. An outsourced partner brings templates and best practices to structure this operating model.

Security updates, access controls, and compliance audits are planned and executed without disrupting operations, thanks to rigorous segmentation of test and production environments.

This approach helps reduce vulnerability exposure, maintain high robustness, and facilitate internal or external audits required by regulators.

Example of a Swiss Financial Services Company

A medium-sized private bank entrusted the monitoring and maintenance of its infrastructure to an external provider. The partner deployed a shared Security Operations Center (SOC), a cluster of redundant servers, and an automatic datacenter failover protocol.

During a partial outage of the primary network, the failover occurred in under three minutes, with no service interruption for customers. Semi-annual recovery tests validated the system’s reliability.

The exercise demonstrated that outsourcing can achieve a level of resilience that is hard to attain solely in-house, especially for smaller organizations.

Internal teams gained peace of mind and were able to refocus on the continuous improvement of business applications instead of incident management.

Turn Your IT Challenges into Competitive Advantage

IT outsourcing is no longer just a way to cut budgets—it’s a strategy to gain agility, expertise, and resilience. Companies can accelerate time-to-market, access rare skills, adopt agile models, and ensure uninterrupted business continuity.

By collaborating with a partner able to deliver open-source, modular solutions without vendor lock-in, the IT department can focus on strategic priorities and creating business value. Performance indicators are met, security is reinforced, and risks are managed.

Our experts are at your disposal to analyze your situation, define a contextual approach, and set up an outsourcing model tailored to your objectives.

Discuss your challenges with an Edana expert

In an environment where logistics and the supply chain are under constant pressure for efficiency and responsiveness, a well-designed transport management system (TMS) becomes a strategic asset. Custom-built and cloud-native, it centralizes data, automates processes, and provides a real-time view of operations. From route optimization and AI-driven predictive management to intelligent billing, a bespoke TMS sustainably reduces costs and enhances customer satisfaction. This article outlines best practices for designing a high-performing, secure, and scalable TMS, as well as the tangible benefits it delivers to both IT and operational decision-makers.

Scalable, Cloud-Native Architecture for a High-Performance TMS

A cloud-native architecture ensures the elasticity needed to handle variable volumes and seasonal peaks in the supply chain. A modular structure facilitates enhancements and minimizes vendor lock-in.

Microservices and Modularity

Breaking the TMS into microservices decouples core functionalities—route planning, shipment tracking, and inventory management—into independent services. Each module can evolve, be updated, or replaced without affecting the entire platform. This approach reduces deployment complexity and increases resilience during incidents.

By relying on proven open source technologies, you avoid proprietary lock-in and benefit from an active community to quickly address vulnerabilities. Standardized REST or gRPC interfaces ensure long-term interoperability with other software solutions.

For example, a Swiss SME in the pharmaceutical sector migrated its legacy monolithic TMS to a Kubernetes-orchestrated microservices architecture. This transition enabled them to deploy new features in a few hours instead of several days, while improving availability to over 99.5%.

Cloud Scalability and High Availability

Choosing a cloud-native infrastructure—containers, Kubernetes, and serverless functions—provides automatic elasticity based on business demand and guarantees scalability for your application during peak periods.

Redundant availability zones ensure service continuity even in case of a major datacenter incident. Automatic failover mechanisms and load shifting minimize downtime and protect the SLAs established with carriers and end customers.

Infrastructure-as-code provisioning using Terraform or Ansible strengthens the reproducibility of test and production environments, reducing the risk of human error during updates or multi-region deployments.

Custom Development and Open Source

A bespoke TMS precisely addresses business-specific requirements—transport types, regulatory constraints, and internal processes—without unnecessary feature bloat. Developing from scratch enables direct integration of existing workflows and optimization of the application core for performance.

By leveraging open source building blocks (frameworks, databases, monitoring tools), you combine flexibility with controlled costs. The absence of proprietary licensing reduces recurring expenses and ensures independence from a single vendor.

With this approach, the TMS evolves organically with the business. IT teams can add targeted modules—cold chain management, customs tracking, carrier scoring—without disrupting the common foundation and while benefiting from shared documentation.

Seamless Integration with ERP, CRM, and WMS

Transparent integration between the TMS and existing systems optimizes processes and reduces information silos. API-first interfaces and data buses ensure real-time consistency.

API-First Strategy and Data Bus

Adopting an API-first strategy ensures that every TMS feature is accessible through secure, documented, and versioned endpoints. REST or GraphQL APIs facilitate exchanges with the ERP for orders, the CRM for customer information, and the WMS for inventory status.

An event bus (Kafka, RabbitMQ) ensures real-time distribution of notifications—order creation, route changes, delivery point arrival—to various actors within the information system. This approach decouples data producers from consumers.

Asynchronous flow management smooths out load spikes and provides a backlog in case of maintenance or incidents, thereby minimizing impact on daily operations.

Real-Time Synchronization and Event Management

Bi-directional data synchronization between the TMS and ERP prevents duplicates and planning inconsistencies. Any modification to an order or schedule is instantly reflected in both systems, ensuring information reliability.

Processing business events—shipment delays, delivery slot changes—triggers automated workflows: customer alerts, route adjustments, and resource reallocation. These automated actions reduce errors and response times.

For example, a Swiss industrial manufacturing company implemented an alert management mechanism triggered by a WMS connected in real time to its TMS. The main outcome was a 20% reduction in loading delays, demonstrating the agility gained through this synchronization.

Monitoring and Traceability

A centralized dashboard displays key performance indicators—fill rates, on-time slot adherence, CO₂ emissions—and alerts for any deviations. This operational transparency facilitates decision-making and continuous improvement.

Structured logs and event traces ensure a complete audit trail, essential for post-mortem analyses and compliance with regulatory requirements. Incident resolution times drop thanks to enhanced visibility into data flows.

A configurable notification system—email, SMS, webhook—automatically alerts relevant teams in case of incidents or anomalies, optimizing operational responsiveness.

{CTA_BANNER_BLOG_POST}

Data Security and Compliance in a TMS

Securing transport data requires a multi-layered security strategy aligned with OWASP standards and GDPR/NLPD regulations. A secure TMS builds partner trust and protects the integrity of data flows.

OWASP Standards and Regular Audits

Implementing OWASP Top 10 recommendations guides code security: strict input validation, CSRF protection, session management, and access control. An independent penetration test identifies vulnerabilities before production deployment.

Automated code reviews using SAST scanners and dynamic analyses (DAST) integrate into the CI/CD pipeline to block builds that do not meet security objectives. This continuous process prevents regressions.

Targeted bug bounty campaigns on the TMS encourage proactive vulnerability discovery and demonstrate the company’s commitment to security.

Encryption and Access Management

Encryption of data at rest (AES-256) and in transit (TLS 1.2 or higher) ensures the confidentiality of sensitive information—delivery addresses, customer data, carrier references. Encryption keys are managed by an HSM or a cloud KMS service.

Implementing a centralized Identity and Access Management system, with strong authentication (MFA) and role-based access control (RBAC), restricts privileges to only necessary operations. Access is tracked and audited to meet both internal and external requirements.

GDPR and NLPD Compliance

Mapping personal data within the TMS identifies processing activities, purposes, and retention periods. A dedicated register simplifies responding to data subject rights—access, rectification, and deletion requests.

Pseudonymization or anonymization processes are integrated upstream to minimize exposure of personal data during statistical analyses or when training predictive AI models.

An incident response plan includes mandatory notifications to competent authorities in the event of a data breach, ensuring swift and transparent communication with stakeholders.

Predictive AI and Automation to Anticipate Disruptions

Predictive AI transforms route planning and optimization into proactive actions. Automation of repetitive tasks empowers teams to focus on value-added activities and real-time decision-making.

Route Optimization through Machine Learning

Analysis of transport history, geographical constraints, and traffic forecasts feeds machine learning models that estimate travel times and risk areas in advance. These predictions dynamically adjust routes.

The algorithm incorporates external data—weather, roadworks, events—to refine forecasts and propose robust route plans.

A regional transport company deployed an ML engine for its TMS, reducing average trip durations by 15% and demonstrating that intelligent forecasting improves delivery punctuality.

Dynamic Load Planning

Automatic allocation of parcels and pallets within vehicles relies on combinatorial optimization, considering volume, weight, and handling constraints. The planner receives an optimal load proposal in seconds.

In case of last-minute reassignments, the system instantly adapts loads and generates transport documents, avoiding manual adjustments and data entry errors. Overall efficiency increases while ensuring vehicle compliance.

Models can simulate scenarios—likely delays, temporary overloads—to anticipate resource needs and trigger automatic booking of supplementary assets.

Automated Billing and Tracking

Automatic extraction of transport data—actual distances, wait times, tolls—feeds the intelligent billing module. Tariff rules and negotiated contracts are applied without manual intervention, accelerating the billing cycle.

Customer tracking notifications are generated in real time by the TMS: pickup confirmation, transit milestones, and estimated delivery date. Call volumes to customer service drop while satisfaction rises.

Detailed financial metrics—cost per kilometer, margin per trip—appear in automated reports, facilitating profitability analysis by transport segment.

Optimize Your Transport Management with a Bespoke TMS

A custom-built, cloud-native, and secure TMS provides a sustainable competitive advantage for logistics and supply chain companies. By focusing on a scalable architecture, seamless integration, regulatory compliance, and predictive AI, organizations reduce costs, improve delivery reliability, and gain operational agility. These best practices accelerate time-to-market and simplify adaptation to evolving business needs.

Our experts are at your disposal to analyze your challenges, design a tailored TMS solution, and support you through implementation—from architecture to team training.

Discuss your challenges with an Edana expert

The CRM is no longer just a customer file but an intelligent orchestration platform capable of coordinating interactions in real time. To be effective, this transformation relies on a robust architecture: a unified data layer (Customer Data Platform, Enterprise Resource Planning, marketing automation), standardized APIs and an event-driven pattern. Added to this are MLOps practices for model reliability and strict governance (GDPR, AI Act, the revised Swiss Federal Data Protection Act, Explainable AI, consent management). Only these pillars guarantee an intelligent, scalable and secure CRM capable of delivering a personalized and measurable customer experience.

Real-Time Unified Data Layer

An intelligent CRM relies on a real-time unified data layer combining a Customer Data Platform, ERP and marketing automation to provide up-to-date customer data. Event-driven architectures and standardized APIs ensure smooth, scalable integration.

Customer Data Platform for Dynamic Segmentation

The Customer Data Platform centralizes data from various customer touchpoints and makes it actionable in real time. This approach feeds the intelligent CRM with behavioral and intent data to create dynamic segments. With a real-time data layer, marketing and sales teams can act instantly on customer signals with zero latency.

Data quality in the CDP is critical: every event must be cleansed, enriched and aligned with a unified data model. The data pipeline ensures consistency of customer identifiers, behavioral attributes and transactional histories.

Integrating multiple sources, such as an ERP or a marketing automation solution enriches the data layer and enables cross-channel analytics. The intelligent CRM thus becomes the customer orchestration platform, capable of reacting to interactions within milliseconds. This creates a personalized, coherent experience across every channel.

Standardized APIs for Continuous Integration

Standardized APIs facilitate API integration between the intelligent CRM, the CDP and third-party systems—such as e-commerce platforms or customer service tools. By adopting open standards, organizations avoid vendor lock-in and retain the flexibility to change providers without major rework.

A well-defined API contract includes clear data schemas, robust authentication mechanisms and documented service-level agreements. These specifications guarantee that streaming and batch calls are handled reliably and securely. IT teams can plan incremental enhancements without risking disruption to critical data flows.

API integration also connects the intelligent CRM to AI services and MLOps modules for real-time scoring. Churn prediction, next-best-action and segmentation models update continuously via automated data pipelines. This orchestration makes the customer platform truly flexible and responsive.

Event-Driven Architecture and Responsiveness

In an event-driven architecture, every customer interaction generates an event processed by real-time stream processors. This pattern ensures minimal latency between event capture and decision-making by the intelligent CRM. Event buses, built on scalable open-source technologies, ensure resilience and durability of the streams.

They support high volume and topic partitioning, enabling granular tracking of interactions. Errors are isolated and routed to dead-letter queues, preserving the stability of the real-time data layer.

Event-consumer microservices implement clear Service Level Indicators (SLIs) and Service Level Objectives (SLOs), with defined latency and error-rate thresholds for each business domain. Detailed operational runbooks outline restore and debug procedures, ensuring agile support in case of incidents. This observability strengthens team confidence in the customer orchestration platform.

For example, a Swiss logistics SME implemented an event-driven solution for its intelligent CRM, synchronizing order statuses with its ERP and call center in under 500 milliseconds. This demonstrates how a robust event-driven architecture improves responsiveness and customer satisfaction, while maintaining seamless integration across systems.

Governance, Transparency and Consent

Solid governance and respectful consent management under regulations (GDPR, AI Act, revised Swiss Federal Data Protection Act) are essential for a trustworthy intelligent CRM. Explainable AI ensures model transparency and reinforces stakeholder confidence.

GDPR, AI Act and Revised Swiss Data Protection Act Compliance

The GDPR and AI Act require rigorous handling of personal data and full traceability of processing. In Switzerland, the revised Federal Act on Data Protection strengthens documentation and data-flow security obligations. Governance by design from the architecture and impact-assessment phase is paramount.

European directives mandate Data Protection Impact Assessments (DPIAs) for automated and AI-based processing. Implementing transversal governance involves uniting IT, legal and business teams in steering committees. This collaboration manages risks, establishes retention policies and defines consent request and revocation processes.

Explainable AI for Transparency

Explainable AI (XAI) aims to make machine learning model outputs understandable for decision-makers and regulators. In an intelligent CRM, every recommendation or score (e.g., churn prediction, next best action) must be justifiable with variable-weight indicators and decision rules.

Interpretability techniques such as SHAP or LIME analyze each feature’s influence on a prediction. Integrated into the CRM MLOps pipeline, they produce automated reports accessible via a governance console. This ensures continuous model transparency and simplifies validation by business experts.

Consent Management and Traceability

Consent management is a cornerstone of personal data governance, especially in a multichannel context. Every customer interaction must follow a process for capturing, storing and updating consent levels. An intelligent CRM integrates both the GDPR and the revised Swiss data protection requirements.

The consent management platform feeds the real-time data layer, enabling campaigns to be activated or suspended instantly according to each visitor’s individual status. Consent logs and update histories are retained for the regulatory period, ensuring full traceability.

{CTA_BANNER_BLOG_POST}

MLOps and Observability

Deploying AI models in an intelligent CRM demands robust MLOps practices for versioning, retraining and drift detection. Clear SLIs/SLOs and operational runbooks guarantee solution reliability and performance.

Versioning and Drift Detection

Model versioning preserves a complete history of iterations, hyperparameters and datasets used for each training. This traceability is essential to quickly identify changes that might cause drift. CI/CD pipelines and a model registry ensure consistent deployment.

Drift detection combines statistical metrics (Population Stability Index, Kolmogorov-Smirnov) and performance thresholds to flag significant divergence between production data and training data. Automating these checks at each prediction is crucial to maintain the integrity of churn, segmentation and next-best-action scores.

Model Monitoring and Alerting

Model observability covers prediction quality and the operational health of AI services. Latency, error-rate and processed-volume metrics are centralized in tools like Prometheus and Grafana. Dedicated dashboards provide real-time visibility into AI endpoint status.

SLOs define performance and availability commitments for AI microservices, while SLIs continuously measure adherence. If latency thresholds are exceeded or error rates spike, automatic alerts notify IT and data teams. Runbooks prescribe actions—whether redeployment or rollback—based on the incident.

Automated Retraining and MLOps Pipelines

Automating model retraining keeps them relevant amid evolving customer behavior. A CRM-focused MLOps pipeline triggers new training when drift criteria exceed defined thresholds, leveraging AI-adapted CI/CD workflows. Notebooks, Docker containers and training scripts are versioned to guarantee full reproducibility.

The pipeline includes automated data validation, performance testing and test-set scoring. Results are compared against historical benchmarks to decide on deployment. This approach prevents regressions and ensures continuous improvement.

Scalable and Measurable Use Cases

Three use cases demonstrate the business value of a real-time orchestrated intelligent CRM. From behavioral segmentation to omnichannel generative messaging and churn prediction with next-best-action, these solutions are fully scalable and measurable.

Behavioral Segmentation and Dynamic Intent Data

Behavioral segmentation uses real-time signals—web interactions, email opens and intent data from search engines. Enriching the intelligent CRM with these streams creates evolving segments reflecting customers’ mindset and purchase intent. This granularity boosts campaign efficiency and lowers targeting costs.

Clustering and supervised classification models deployed via CRM MLOps re-evaluate and adjust segments upon each interaction. Event-driven pipelines trigger automated recalculations without manual intervention, ensuring always up-to-date segmentation. This agility amplifies marketing and sales impact.

A real-time CDP unifies data so every segmentation criterion is available across channels. Analytical dashboards continuously measure segment size and performance, supporting KPI-driven decisions. This scalable approach handles millions of profiles without performance loss.

Coherent Omnichannel Generative Messaging

Integrating omnichannel large-language models (LLMs) into the intelligent CRM enables personalized message generation across email, WhatsApp and chat. Contextual prompts from the data layer keep content coherent and aligned with customer history. This approach increases engagement rates and smooths the multichannel experience.

LLM service APIs are orchestrated by a message composition module that enforces compliance rules and consent preferences. Response times and tonal consistency are measured with dedicated SLIs, ensuring smooth service even during peak load. Runbooks define fallback procedures in case of overload or excessive latency.

Churn Prediction and Next Best Action

Churn prediction relies on supervised models trained on behavioral and transactional histories. Deployed in the intelligent CRM, they feed a next-best-action workflow that delivers personalized retention offers or reengagement tactics in real time. Effectiveness is measured by retention rate and incremental customer value.

Automated pipelines trigger churn scoring on every new event for maximum responsiveness. Runbooks detail treatment scenarios for at-risk customers, recommending proactive contact, promotional offers or informative content. Each action is tracked to measure strategy impact on churn rate.

Business dashboards regularly compare churn forecasts with actual outcomes to calibrate models and strategies. Model precision and recall SLIs are monitored continuously, and retraining is automated when performance dips below thresholds. This feedback loop guarantees continuous improvement.

Transform Your CRM into a Real-Time Orchestration Platform

Moving from a static customer file to an intelligent CRM rests on four pillars: a unified, event-driven data layer; strict governance with Explainable AI and consent management; MLOps practices for observability and hyperautomation; and scalable use cases in segmentation, omnichannel generation and churn prediction. Together, they deliver a personalized, responsive and reliable customer experience.

Whether you are a CIO, CTO, IT Director, Head of Digital Transformation or COO, implementing a customer orchestration platform requires modular architecture, standardized APIs and agile governance. Our experts master these challenges and support you from audit to execution in designing, running and governing your intelligent CRM.

Discuss your challenges with an Edana expert

Migration to AWS is a key step to modernize your infrastructure, reduce IT costs, and improve operational agility.

By carefully planning each phase—from workflow mapping to post-migration optimization—you can anticipate downtime and control your overall total cost of ownership (TCO). This article presents ten strategic, technical, and financial best practices to ensure a successful transition to the AWS cloud while guaranteeing security and scalability. We’ll also discuss European and Swiss solutions to limit vendor lock-in and strengthen data sovereignty, providing a comprehensive overview tailored to IT directors, CIOs, CTOs, CEOs, and IT project managers.

Strategic Preparation and Workflow Mapping

The success of an AWS migration depends on a thorough understanding of your application ecosystem. Precise workflow and dependency mapping prevents critical oversights.

Workflow Mapping and Dependency Identification

Before any operation, it is essential to inventory all applications, services, and data streams. This mapping allows you to visualize component interactions and identify friction points that could cause disruptions.

By identifying cross-service dependencies, you minimize the risk of service outages. Each service is evaluated based on its business criticality, data volume, and latency requirements to prioritize actions.

For example, a Swiss SME in the industrial sector conducted a comprehensive audit of its production applications. This analysis uncovered outdated interfaces between two key modules that would have caused over 48 hours of downtime without prior remediation.

Clear Definition of Roles and Access (RBAC)

A precise definition of roles and permissions (Role-Based Access Control, RBAC) ensures that each team or application has only the necessary rights. This limits the risk of misconfiguration and reduces the attack surface.

Implementing IAM (Identity and Access Management) groups structured by function centralizes access management and automates updates during organizational changes.

To strengthen security, associate each role with least-privilege policies and regularly audit assignments to detect any unnecessary permissions.

Realistic Total Cost of Ownership (TCO) Assessment

Beyond migration costs, the TCO should include team training, ongoing maintenance, and partial system redesign. This long-term perspective helps avoid budgetary surprises.

You should account for variable costs (instances, storage, data transfers) and fixed costs (licenses, support, DevOps tooling). A basic FinOps model from the planning stage helps manage these expenses.

Finally, include project management fees, external consulting, and documentation to accurately estimate the total investment compared to an on-premises model.

Cloud Governance, Security, and Cost Control

Strong governance and a structured FinOps approach are essential to prevent cost overruns and ensure compliance. Incorporating AWS advanced security services strengthens your posture from the outset.

Security Integrated from the Start

In addition to RBAC, deploy AWS Security Hub to centralize vulnerability analysis and non-compliant configurations. AWS Shield protects your applications against DDoS (Distributed Denial of Service) attacks, while GuardDuty continuously detects suspicious activity.

Adopting an Infrastructure as Code security strategy using AWS CloudFormation or Terraform ensures that every configuration is versioned, traceable, and reusable across dev, test, and production environments.

Development and security teams collaborate upfront to define standards and automated controls, thereby reducing the risk of human error and privilege escalation.

FinOps Optimization and Cost Tracking

FinOps involves establishing a culture of financial accountability in the cloud. It combines cost metrics, regular audits, and transparent reporting to align spending with business value.

Using AWS Cost Explorer and rigorously applied tags, you assign each resource to a project, cost center, or team, facilitating internal billing and budget allocation.

Scheduled alerts help detect consumption anomalies before they significantly impact the budget, enabling real-time control.

European and Swiss Alternatives to Limit Vendor Lock-In

To enhance data sovereignty, combine AWS with local providers such as Infomaniak or certified European sovereign clouds like OVHcloud and Scaleway. This hybrid approach reduces reliance on a single vendor.

Compatible APIs and Kubernetes abstractions facilitate cross-cloud deployments while maintaining workload flexibility and portability.

For example, a Swiss cantonal administration implemented a hybrid infrastructure by distributing its databases between AWS and a Swiss-certified cloud. This setup met Swiss regulatory requirements while leveraging AWS scalability.

{CTA_BANNER_BLOG_POST}

Progressive Cloud Migration Approach

A gradual migration (lift & shift, refactor, replatform) reduces risks and ensures operational continuity. Each application follows the most suitable plan based on its criticality and architecture.

Lift & Shift for a Quick Start

The lift & shift method involves replicating your on-premises infrastructure to AWS without major redesign. This approach speeds up the initial migration and frees up local resources.

Although it does not fully leverage managed services, it serves as a first step to offload servers and validate network and IAM configurations.

A Swiss financial services company completed a lift & shift of its reporting servers in 48 hours. This move reduced local data center usage by 30% and laid the groundwork for further modernization.

Refactoring and Replatforming to Modernize Applications

After lift & shift, identify critical components to refactor in order to leverage managed services (RDS, Elastic Beanstalk, ECS). Replatforming aims to adjust the architecture without rewriting all the code.

This phase helps reduce technical debt and improve resilience through clustered databases, distributed file systems, and managed caches like ElastiCache.

Progressive refactoring is organized by functional batches, with performance and security validations before each production cutover.

Ongoing Training and Change Management

To ensure internal adoption, regularly train your teams on AWS services through hands-on workshops and AWS Certified Solutions Architect or DevOps Engineer certifications.

An internal communication plan, coupled with feedback sessions, fosters ownership of new processes and the spread of best practices.

Establishing a Cloud Center of Excellence facilitates experience sharing and the industrialization of validated architecture patterns.

Post-Migration Optimization, DevOps Automation, and FinOps

After migration, continuous improvement ensures optimal performance and strict financial control. DevOps integration accelerates deliveries and enhances reliability.

Performance Monitoring and Optimization

Use Amazon CloudWatch and AWS X-Ray to monitor latency, CPU usage, and error rate metrics. This allows you to detect bottlenecks in real time.

Automated reports identify underutilized instances, recommend rightsizing, and enable hibernation modes for non-critical environments.

A Swiss medical sector company implemented CloudWatch dashboards for its patient record APIs. Automated alerts reduced production performance incidents by 40%.

CI/CD Automation and DevOps Culture

Deploy CI/CD pipelines with AWS CodePipeline, CodeBuild, and CodeDeploy to automate testing, builds, and deployments. Each change is validated in a secure environment.

Integrating unit, integration, and end-to-end tests ensures each release meets your quality and security requirements.

GitOps practices, combining Git and Kubernetes operators, enable declarative and traceable management of your cloud deployments.

FinOps Governance to Control Spending

Monthly budget reviews align IT directors, finance teams, and business units on actual cloud consumption. Overrun alerts are addressed in a dedicated backlog.

Allocating dedicated budgets for each project, combined with granular tracking via AWS Budgets, promotes team accountability.

Thanks to these practices, budget overruns are minimized, and each expense is justified by measurable business value.

Benefits of a Well-Managed AWS Migration

A well-managed AWS migration relies on rigorous planning, secure governance, and a progressive approach tailored to each application. Initial mapping, RBAC, TCO assessment, and post-migration optimization work together to ensure continuity and scalability. By combining DevOps, FinOps, and managed services, you mitigate risks, control costs, and free up time for innovation. Hybrid solutions with Swiss or European clouds enhance data sovereignty and prevent vendor lock-in. Our Edana experts support every step of your project, from strategic analysis to continuous optimization, to transform your infrastructure into a genuine business performance engine.

Discuss your challenges with an Edana expert