AI assistants such as OpenAI’s Operator or the embedded agents in Perplexity are quietly reshaping online purchasing journeys. These autonomous systems will soon be able to search, compare, and complete transactions on behalf of customers. For e-commerce, marketing, or innovation leaders, this shift demands a rethink of visibility, product architecture, and user experience. How can we anticipate this transition and adapt our digital ecosystems to these “non-human customers”? In this article, we explore four key challenges and offer a practical framework to help prepare your e-commerce platform for the era of AI-driven purchasing.

1. AI Becomes the Customer

AI agents initiate and complete purchases without direct human interaction. These automated actors browse catalogs, evaluate offers, and execute transactions in just a few queries.

Evolution of the Customer Role

Early chatbots merely assisted users; today’s AI agents can act on their behalf. These assistants navigate sites, compare prices, and place orders on third-party platforms via dedicated programming interfaces. They rely on advanced language models to understand the business or personal need they represent. This capability paves the way for scenarios in which companies primarily interact with “buyer robots.”

This shift profoundly changes the very notion of “traffic” and “clicks.” Request volumes may drop on the traditional interface while the AI aggregates and forwards decisions directly to your API. Traditional metrics like click-through rates or average basket value lose relevance. E-commerce managers must therefore track new KPIs, such as the number of AI sessions and the machine-to-machine conversion rate.

How AI Agents Work

An AI agent uses structured prompts to search for a product, assess its attributes, and decide on a purchase. It analyzes the semantic content of your product page, compares available options, and selects the most relevant offer. Compatible platforms expose data via JSON-LD schemas optimized for machines, summarizing price, availability, and delivery terms. The purchase then completes through a payment API or secure webhook.

This automation requires precise, accessible technical documentation. Order flows must be triggerable via an authentication token without a traditional form. Companies that adopt these APIs ensure seamless, resilient integration with AI requests. Without this level of preparation, the agent may halt or misinterpret data, causing failures and drop-offs.

Example of Integrating an AI Agent into an E-commerce Purchase Journey

An industrial supplies retailer recently opened its catalog to an internal AI agent. The company structured its product data in machine-readable JSON-LD and deployed a simplified checkout API. After a few weeks of testing, the agent narrowed searches, compared warehouse rates, and placed orders according to predefined cost-optimization rules.

Result: purchasing managers saw a 30% reduction in time spent on repetitive orders. The agent now processes a batch of 200 items in under ten minutes versus two hours previously. This automation also reduced input errors and improved delivery reliability.

2. The Commerce Experience Moves Off-Site to External Chatbots

The act of sale is no longer confined to your online store. Interactions now begin in a chatbot, a third-party app, or a conversational search engine.

Conversational Commerce

Platforms like Perplexity or mobile apps equipped with AI agents offer a journey where users never visit your site. Search, comparison, and purchase all occur within a conversational channel, whether text or voice. These environments deliver instant responses, powered by your structured data and business rules.

To appear in this new sales funnel, you must index your product content on conversational engines. A simple XML feed is no longer sufficient; you need clear, contextualized snippets. Each response should trigger a secure link to your order API or redirect to the agent’s proprietary interface.

Proactive AI Recommendations

AI agents can suggest products based on purchase history or configurable business rules. They anticipate needs and drive purchases without direct user prompts. This proactive personalization boosts engagement and accelerates the conversion cycle. At the same time, it demands fine-grained segmentation of customer data and rigorous GDPR consent management.

In this context, traditional SEO optimization takes on a “machine” dimension, where keywords must appear in named entities and agent-specific tags. Conversational ranking involves semantic enrichment and alignment with industry ontologies. SEO teams must therefore collaborate closely with data managers.

Example of an Off-Site Product Purchase Experience

An online furniture retailer tested a conversational agent integrated into a third-party group-buying platform. The company provided a public API and a dynamic filter field to tailor product use. Within two months, over 15% of orders originated from this interface, without a single visitor viewing the standard product page.

Beyond volume, the average order value generated by the agent increased by 12%, thanks to complementary product suggestions based on stored preferences. This success convinced teams to extend the setup to other conversational channels.

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3. New Technical and Strategic Challenges

AI agents demand a complete reevaluation of your product architecture and user experience. Data readability, checkout integration, and personalization become critical priorities.

Product Data Readability

Language models consume microdata and ontologies to interpret your offerings. It’s no longer enough to display price or description on a webpage; you must structure all relevant details in technical schemas. Every attribute—dimensions, materials, warranty—must be machine-readable to avoid misinterpretation.

Implementing JSON-LD or appropriate microformats ensures reliable extraction. You should also document use cases, price calculation rules, and special conditions in a continuously accessible data catalog. This step requires collaboration between product, marketing, and data engineering teams.

Integrable Checkout Process

For the agent to finalize a transaction, the purchase funnel must be exposed via a secure API. This involves not only offering one-click payments but also handling authentication, authorization, and confirmation flows. Authentication should use OAuth or JWT tokens, without human intervention, while maintaining high security standards.

A Swiss online pharmaceutical company recently adapted its payment system for AI agents. It implemented a REST API with dual cryptographic validation. The operation now completes in under four seconds and integrates seamlessly into the agent’s workflow.

Personalization and Security

Agents leverage customer data to tailor offers in real time. They cross-reference purchase history, declared preferences, and behavioral data to adjust products and quantities. This personalization increases conversion rates but demands precise access and consent management. Each API call must verify that the agent has the necessary rights to view or modify user data.

Moreover, technical reliability and machine-readable UX are essential. Agents don’t handle error pages or poorly structured forms well. They require clear responses and appropriate HTTP statuses. A flawed implementation can halt the agent or produce unrecoverable errors.

4. Edana as a Tech-Strategy Partner to Evolve Your E-commerce for the AI Era

Preparing your e-commerce for AI-driven purchasing requires a holistic approach combining data, architecture, and UX. Edana supports you in structuring, ensuring AI compatibility, and designing adaptable journeys.

Product Data Structuring

We analyze your catalog and identify key attributes to expose for AI agents. Our approach includes semantic modeling, optimized JSON-LD, and the creation of automated data pipelines. These steps ensure consistency across channels and simplified maintenance.

Working in agile sprints, we adjust the data model based on initial agent feedback. This iteration refines the relevance and accuracy of responses provided by AI assistants. The result is a scalable, controlled architecture.

Compatibility with AI Ecosystems

We assess and implement the APIs needed to expose your catalog and checkout funnel to agents. Our expertise covers OAuth implementation, JWT security, and OpenAPI documentation. We ensure performance, scalability, and regulatory compliance, including GDPR.

With our hybrid approach, we integrate proven open-source solutions to orchestrate these flows and avoid vendor lock-in. You retain control of your ecosystem and can adapt components as agents and standards evolve.

Machine-Readable UX Design

We design interfaces tailored for AI agents, defining optimal endpoints and response formats. Each entry point is crafted to deliver clear, comprehensive, structured data. We systematically test with pilot agents to validate journey robustness.

Simultaneously, we advise on the classic user experience to ensure a seamless transition between human and agent journeys. This duality has become a market differentiator in automated e-commerce.

Prepare Your E-commerce for AI-Driven Purchasing

AI agents are poised to redefine shopping journeys by automating search, comparison, and payment. To remain competitive, you must structure your data, implement an API-first checkout, and ensure a machine-readable UX. This evolution impacts SEO, technical architecture, and transaction security.

Regardless of your digital maturity, it’s essential to build a scalable, modular platform aligned with emerging AI standards. Our Edana experts guide Swiss mid-sized and large SMEs through this transition, from technical audit to operational rollout.

Discuss your challenges with an Edana expert

The emergence of “AI Shoppers,” these intelligent agents capable of placing orders based on entirely objective criteria, is disrupting the established rules of e-commerce. Brands that relied on branding, storytelling, or interface design must now anticipate a new criterion: the readability and pure reliability of product data. For Swiss decision-makers, this challenge is not theoretical—it requires adapting catalogs, online reputation, and technical solutions to appeal to these automated buyers. In this landscape, AAO (AI Agent Optimization) is set to be as critical as SEO was twenty years ago. This article explores four strategic avenues to prepare your e-commerce for this revolution.

AI Agents: The New Digital Buyers Ready to Overlook Traditional Branding

These agents favor objective data (price, availability, performance) over brand identity. They analyze and compare thousands of items in real time to optimize each purchase.

The AI assistants that purchase automatically operate based on algorithms designed to select the most efficient offer according to precise criteria. They assess perceived quality from factual parameters: delivery times, customer returns, availability history, and reliability scores.

Unlike traditional consumers, these agents are not influenced by sophisticated marketing content or attractive visuals. What matters to them is clear, structured information, accessible via APIs or extracted from intelligible catalog feeds.

Objective Factors Preferred by AI Agents

AI agents run scoring functions that aggregate quantified, normalized data. The most common criteria are total cost of acquisition, logistical processing time, and return probabilities.

Each data point must be provided in a structured format (JSON-LD, microdata, XML) to be easily ingested. A missing attribute can lower a product’s score, excluding it from the agent’s generated results.

Historical consistency also plays a role: an agent values channels that have proven reliable across multiple past transactions. A site with recurring stockouts can be penalized, even if its price is low.

Impacts on the User Experience

The obsession with pure data is reshaping the structure of purchase journeys. Product pages now need to include performance indicators (availability rate, average delivery time, customer satisfaction rate) rather than narrative content.

On the UX side, the goal is no longer just to entice the end user but also to provide a technically reliable interface for bots. Rigorous semantic markup and coherent microformats become essential.

Internally, marketing and IT teams must coordinate their efforts to ensure every data feed is updated in real time and error-free, or risk seeing the AI agent favor a more agile competitor.

Repercussions on Brand Strategy

Brands will lose part of their emotional influence if they do not objectify their value. Differentiation must shift to tangible criteria: service quality, eco-design, manufacturing origin.

Narrative content and branding campaigns remain relevant for human purchases, but they no longer factor into the agents’ calculations. It is therefore necessary to strike a careful balance between technical optimization and emotional marketing.

In practice, a cross-functional collaboration between product managers, data scientists, and UX designers is required to align storytelling with AI-processable formats.

Example: A European online furniture retailer saw AI-generated sales increase by 20% after enriching its product data with sustainability indicators and real-time availability metrics.

AAO: AI Agent Optimization, the New Lever as Critical as SEO

Optimizing for AI agents (AAO) has become a strategic imperative, ensuring visibility and relevance in automated queries. Product data, reputation, and technical architecture must be reinvented.

Just as SEO forced organizations to revise their web content and site structures, AAO demands rethinking catalog structuring and the robustness of IT integrations. The correlation between data quality and business performance is now direct.

Teams must adopt agile workflows, integrating a continuous process of updating and verifying feeds destined for agents. Automated monitoring mechanisms become vital to detect anomalies before they harm a channel’s score.

Structuring Product Data for AI

The first step is ensuring completeness, consistency, and granularity that meet agents’ needs. Each product must include measurable attributes: exact dimensions, weight, standardized colorimetry, certifications.

Catalog-wide normalization allows algorithms to compare offers more efficiently. Format discrepancies between suppliers are eliminated via automated data mapping.

Maintaining this data requires a robust ETL (Extract, Transform, Load) pipeline capable of integrating continuous updates without disrupting production systems.

Managing Reputation and Reviews to Earn AI Agents’ Trust

AI agents also analyze a merchant’s reputation based on customer reviews and logistical reliability scores. A review aggregation and cleansing process ensures an image that reflects operational reality.

Transparency in returns and dispute resolutions is valued. Platforms that clearly publish return rates or resolution times receive higher ratings from assistants.

To anticipate these criteria, companies must implement webhooks and APIs to relay satisfaction and incident-handling metrics in real time.

Technical Optimization and IT Integrations for Scalable Deployment

AI Agent Optimization requires a scalable, resilient infrastructure. API endpoints must handle request peaks and guarantee response times under a few tens of milliseconds.

A modular architecture, favoring open-source microservices, limits contention boundaries and reduces the risk of vendor lock-in. Cloud orchestrations should dynamically allocate resources and bandwidth.

Proactive monitoring (distributed tracing, structured logs) and real-time alerting ensure the continuous availability of critical feeds for agents.

Example: A Swiss electronics equipment distributor doubled its AI Shoppers sales opportunities after overhauling its master data management, automating certified review publication, and deploying a Kubernetes cluster for its product APIs.

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Becoming Objectively Distinct to Withstand Copycat Brands and Appeal to AI Agents

AI agents do not differentiate products by branding if specifications are identical. Only technical and factual uniqueness creates a barrier to replication.

In the era of AI Shoppers, differentiation no longer comes through packaging or slogans but through tangible value: innovative materials, measurable performance, local certifications. These attributes must be documented in a standardized, verifiable manner.

Highlighting Swiss labels, environmental standards, or durability tests becomes a lever to stand out in automated queries. This data feeds into agent scores and enhances the appeal of Swiss offerings.

Importance of Product Differentiation by Technical Characteristics

A product must stand out through clear metrics: energy efficiency, cycle time, stress resistance, extended warranties. Each numeric value contributes to the automated comparison.

These indicators are then exposed via interoperable formats (schema.org, GS1) so agents can prioritize them. The supplier providing the most exhaustive metrics dominates the results.

Internally, this requires a close link between R&D and IT to transform technical documentation into machine-readable markup.

Role of Quality Labels and Local Certifications

Swiss certifications (Oeko-Tex, Swiss Made, eco-labels) serve as objective proof of recognized quality. AI agents consider them as reliability filters.

To be taken into account, these labels must appear in structured product attributes and be updated according to certification renewal cycles.

Compliance data, managed through a central repository, can be published in a dedicated feed that agents access with priority.

Data-Driven Narratives Based on Performance

Rather than marketing descriptions, agents favor comparative test results (benchmarks, technical rankings) documented by recognized third parties.

A self-audit protocol hosted on an accessible portal demonstrates the company’s rigor. Links to these reports, provided in metadata, reinforce algorithmic trust.

This effort turns editorial content into quantified proof, bolstering brand resilience against imitations.

Example: A Basel-based cosmetics SME rose to the top of AI queries after integrating dermatological test data and botanical origin certificates directly into its product feed.

Capitalizing on Swiss Strengths to Attract AI Shoppers

Service quality, fast delivery, local stock, and reputation are tangible competitive advantages. Making them indexable for agents can create a significant differential.

Swiss e-merchants can leverage geographical proximity and logistical reliability to achieve a higher score in automated rankings. However, every promise must be formalized through accessible data.

The information system must convey real-time stock levels, express delivery options, and customer support commitments. APIs should reflect these strengths as a priority.

Highlighting Local Logistics and Delivery Times

A “Shipped from Switzerland” badge or a “+2 working days” delivery indicator directly impacts the agent’s score. This information must appear in a dedicated product catalog field.

Logistics API integrators can synchronize this data automatically, ensuring that an out-of-stock item does not appear as available.

Order tracing, usable by assistants, reinforces confidence in meeting the promised deadlines.

Clarifying Stock Levels and SLAs

AI agencies evaluate the likelihood of transaction success based on the granularity of stock data. Swiss e-merchants must expose not only totals but also warehouse-level breakdowns.

Service level agreements (SLAs) for returns and exchanges then adjust the final score. A 30-calendar-day return period is preferred over a more restrictive process.

Implementing logistical performance indicators (OTIF, shipment accuracy rate) improves algorithmic visibility and justifies recommendation premiums.

Integrating Customer Service into AI Feeds

AI agents don’t stop at purchase: they anticipate post-sales interactions. Access to an automated chatbot or a certified call center appears in their scoring matrix.

It is therefore recommended to make first-contact resolution rates, service hours, and available channels (email, phone, chat) accessible. This information feeds trust in the merchant’s ability to handle incidents.

A structured dashboard, exposed via a dedicated API, allows continuous synchronization of these metrics with AI shopping platforms.

Position Your E-Commerce for the Age of AI Shoppers

To capitalize on this disruption, adopt a holistic approach: fine-grained data structuring, feed automation, reinforced IT reliability, and highlighting local strengths. AAO becomes a cross-functional project involving marketing, data, and IT.

By investing in open-source, scalable, and secure architectures without vendor lock-in, you ensure the longevity of your optimizations. Every enriched metadata and every optimized API contributes directly to your visibility with AI assistants.

Our Edana experts are available to define a tailored AAO roadmap and turn these challenges into growth opportunities.

Discuss your challenges with an Edana expert

The digitization of retail today demands major strategic decisions around the e-commerce platform. Salesforce Commerce Cloud (SFCC) positions itself as a robust SaaS solution, capable of unifying front office, back office, and CRM within a single ecosystem. Yet its proprietary model and recurring costs raise questions of flexibility and budget control. To meet these imperatives, it is essential to objectively analyze its strengths, limitations, and alternatives to select the solution best suited to each company’s growth ambitions and business constraints.

Understanding Salesforce Commerce Cloud and Its Strengths

Salesforce Commerce Cloud offers an integrated, highly connected platform ready to use within the Salesforce ecosystem. This cloud-native solution accelerates the deployment of global e-commerce sites.

Origins and Positioning of the Platform

Salesforce Commerce Cloud traces its roots to Salesforce’s 2016 acquisition of Demandware. This integration combined Salesforce’s CRM expertise with a proven e-commerce solution. Since then, SFCC has become a leading SaaS player in online retail. Its strength lies in natively synchronizing customer, product, and order data without the need for additional interface development.

SFCC primarily targets mid-market and large enterprises with international requirements. The platform supports multiple sites, currencies, languages, and sales channels within a single tenant. This multi-market capability simplifies managing a complex network and ensures consistency across global processes. Salesforce delivers regular updates, guaranteeing fast access to the latest innovations.

On the technology side, Commerce Cloud relies on a framework called Storefront Reference Architecture (SFRA). This layer streamlines template deployment and front-end customization. Developers can also use Commerce Cloud Digital to deploy code in Customer Experience Language (ISML). These mechanisms speed up the creation of engaging shopping journeys.

The SFCC ecosystem also includes native modules such as Einstein for AI, Order Management for order handling, and Marketing Cloud for automation. This comprehensive suite reduces the need for third-party integrations, representing a major asset for organizations aiming to unify their marketing and sales channels.

Flexibility and Speed of Implementation

The SaaS nature of Salesforce Commerce Cloud eliminates hosting and infrastructure constraints. IT teams do not have to manage servers, databases, or workloads—Salesforce handles scalability. In just a few weeks, a first e-commerce site can go live using standard templates. This rapid deployment significantly reduces time-to-market and meets sudden capacity needs, especially during seasonal promotions.

Developers can deploy changes via Git and CI/CD, following DevOps best practices. Salesforce synchronizes isolated staging and production environments. Unit and integration tests can be automated through standard pipelines, minimizing regression risks with each update.

For business teams, the Business Manager interface allows administration of the entire product catalog, promotions, editorial content, and pricing rules without IT support. This autonomy accelerates marketing campaigns by reducing developer dependency. Approved workflows and granular permissions ensure security and compliance with internal processes.

Lastly, Salesforce offers a network of certified partners and a rich AppExchange marketplace for complementary applications. These extensions cover payments, shipping, collections, and analytics. Organizations can enhance their platform without starting from scratch while benefiting from centralized maintenance.

Integrations and the Salesforce Ecosystem

One of Commerce Cloud’s key strengths is its native connection to other Salesforce clouds. CRM, Marketing, Service, and Analytics data form a single repository, eliminating information silos. Marketing teams can personalize customer journeys in real time with Einstein Recommendations, synchronized with on-site purchasing behavior.

SFCC exposes REST and SOAP APIs to interface with ERPs, PIMs, or third-party logistics solutions. This openness ensures end-to-end coherence between the front office and back-office processes without complex connector development. Transactions, inventory tracking, and billing are automatically shared.

On the analytics front, integration with Tableau CRM and Datorama consolidates KPIs in real time. Leadership teams gain cross-channel dashboards to monitor campaign ROI and operational performance. This visibility supports agile, informed decision-making.

The AppExchange marketplace offers a wide range of certified modules. Whether for localization, payments, reporting, or loyalty management, companies can accelerate their roadmap. Salesforce’s global support guarantees the compliance and security of every added extension.

Limitations and Risks of Vendor Lock-In and Recurring Costs

Salesforce Commerce Cloud can entail a long-term financial and technical commitment. Customization limits and the SaaS model may complicate adaptation to specific needs.

License Costs and the SaaS Model

Salesforce Commerce Cloud’s subscription model includes a monthly license based on generated revenue. This pricing can quickly become significant for high-volume sites. Initial implementation costs are generally included, but recurring fees remain high over time.

Beyond base licenses, complementary modules (Order Management, Einstein AI, Marketing Cloud) are billed separately. Each new use case can generate non-negligible additional costs. Forecast budgets must account for these items to avoid surprises.

Seasonal revenue fluctuations directly impact the monthly bill. During major sales events, companies may end up paying more than anticipated, with limited negotiation capacity. This budget variability requires expertise to forecast peak periods accurately.

Finally, migrating to another system or terminating the contract involves transition costs. Data and custom developments reside within Salesforce’s proprietary environment, making extraction complex and costly without specialized support.

Customization and Technical Constraints

While customization options are extensive, they rely on SFCC’s proprietary framework. Developers must master ISML and platform-specific APIs, potentially limiting the reuse of in-house skills. Ramp-up on these proprietary technologies requires learning time and training budgets.

For highly specific needs, teams often resort to tenant-specific JavaScript overlays. These can become black boxes in case of turnover, complicating maintenance and version upgrades. Platform updates then demand thorough testing of these ad hoc developments.

Developers are also bound by Salesforce’s roadmap. Platform enhancements follow a fixed schedule, with quarterly feature releases. If an urgent business need isn’t covered, teams must devise workarounds, often involving external integrations or hacks.

Debugging and incident resolution go through the Business Manager interface or specific logs. Access to production logs is limited and regulated, which can extend diagnosis times and affect service quality during critical periods.

Scalability and Performance Under Critical Load

SFCC’s scalability is managed by Salesforce’s infrastructure, ensuring automatic capacity scaling. However, real-world performance depends on code quality and server query optimization. Poorly designed code can lead to high response times despite the cloud’s power.

Mass marketing campaigns, unexpected traffic spikes, or flash sales may expose limits in session pool management. Latency can increase if resources aren’t properly profiled, impacting user experience and conversion rates.

Load tests must be conducted in advance, but staging environments don’t always mirror production configurations. Performance gaps discovered after go-live then require post-deployment optimizations, which can be costly.

Finally, asset management (images, videos) relies on the integrated CDN. Default configurations may not be optimal for every market or content type. Poor cache settings can overload servers and slow page rendering.

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Open Source and Custom-Built Alternatives

Several headless open source platforms and custom solutions offer greater freedom and avoid vendor lock-in. They allow architecture to be tailored precisely to each business’s needs.

We have conducted a comprehensive comparison of merchant solutions on the e-commerce and e-service market. For detailed information on all leading solutions and guidance on choosing the one that truly fits your company and strategic challenges, please refer to it.

Below is a brief overview of a few alternatives to SFCC to illustrate the range of possible options.

Custom-Built Solutions and Key Benefits

Custom-built involves developing an e-commerce platform from scratch, tailored to specific business processes. This approach ensures perfect alignment between the tool and requirements, without superfluous features. Every component is designed to match the defined scope, simplifying maintenance and scaling.

With a modular, microservices architecture, teams can evolve each building block independently. Deployment phases are isolated, reducing regression risk for the entire system. Performance is optimized based on actual usage.

Choosing open source technologies (such as Node.js, TypeScript, NestJS, Laravel) ensures an active community and continuous updates. This reduces licensing costs and strengthens security through rapid patches. No recurring platform fees free up budget for innovation.

Finally, custom-built solutions facilitate integration of third-party or in-house systems via standard APIs. A hybrid architecture promotes interoperability with ERP, PIM, or external CRM systems without excessive dependency on a single vendor.

Medusa.js: A Headless Open Source Platform

Medusa.js presents itself as a headless alternative based on Node.js and TypeScript. It offers a lightweight, modular e-commerce core with a store API independent of the front end. This separation guarantees total freedom over the presentation layer, whether using React, Vue, or mobile frameworks.

The Medusa community is growing rapidly, providing plugins for payments, inventory management, shipping, and more. Developers can build custom extensions and share them, enriching the framework. Updates are driven by the community rather than a single vendor.

Medusa.js enables straightforward horizontal scaling through a microservices architecture. Each service (orders, products, customers) can be deployed independently, optimizing resources for peak loads. REST and GraphQL APIs allow seamless integration with existing tools.

For example, a Swiss agrifood company migrated to Medusa.js to gain flexibility with its highly segmented product catalogs. It reduced its licensing costs by 70% while improving the responsiveness of product updates. Implementation took three months, with an estimated ROI of eight months.

Magento and commercetools: A Comparative Approach

Magento (Adobe Commerce) remains a mature solution with a strong community and extensive module ecosystem. It can be deployed on-premise or in the cloud. Open source editions help avoid certain license fees but require greater infrastructure and maintenance investment.

Commercetools, on the other hand, offers an API-first headless SaaS platform oriented toward microservices. Its billing is based on API calls and deployed modules, providing a different cost visibility compared to Salesforce Commerce Cloud.

Magento demands rigorous version and third-party module management, which can generate technical debt if updates aren’t anticipated. Commercetools delivers transparent updates but may create dependency on the vendor’s roadmap.

The choice between these platforms depends on desired autonomy, in-house skills, and scalability requirements. In all cases, favoring a modular architecture and standard APIs will ease future migrations if needed.

Selecting the Ideal Solution for Your Digital Commerce

The success of an e-commerce or e-service project relies on balancing agility, performance, cost control, and technological independence. Proprietary solutions like Salesforce Commerce Cloud offer rapid deployment and native integration, but may commit you long term to high costs and technical constraints.

Open source and custom-built alternatives—such as Medusa.js, Magento, or commercetools—enable the creation of an evolving ecosystem free from vendor lock-in. By combining proven building blocks with bespoke developments, each platform can be tailored to your business context and guarantee resilience and ROI.

Every decision should be based on a precise analysis of volumes, processes, and internal skills. Defining a modular, API-driven architecture provides a robust foundation for continuous growth and innovation.

Our experts are available to challenge your digital roadmap, formalize your requirements, and guide you in building a high-performance, sustainable custom e-commerce platform.

Discuss your challenges with an Edana expert