Global commerce is complex by nature. Managing products across multiple countries, languages, and brands involves a web of localization, compliance, and operational considerations that only grows as a business scales. But one challenge that comes up consistently among enterprise organizations is that their search and discovery architecture makes it harder than it needs to be — not because the problem is unsolvable, but because the dominant approach to structuring catalog data was never designed with global scale in mind.
The Hidden Cost of Multi-Market Deployments
For organizations managing product catalogs across multiple markets, the setup has typically looked something like this: every market gets its own version of every product. A sweater sold in Canada, the US, and the UK doesn’t live once in the index, it’s duplicated across multiple indexes or environments. But country is only the first multiplier. Many markets operate across multiple languages, Canada alone runs in English and French for example, which means duplication compounds by language variant too, not just geography. A catalog across ten countries with two languages each isn’t looking at 10x the records but at 20x, before regional pricing and brand-specific descriptions are even factored in.
For a business with hundreds of thousands or even millions of SKUs across dozens of markets, this creates real, compounding operational drag that touches every phase of a deployment, from initial implementation through to day-to-day maintenance.
Take a parts distributor with two million skus operating across 33 locales. In a typical architecture, that often means maintaining 33 separate indexes (one per market), resulting in more than 60 million indexed records. But the reality is, they’re still only selling two million products.This plays out in a few consistent ways:
- Architectural complexity
Whether a platform uses a single unified index or multiple separate indexes, the duplication requirement creates overhead that increases with every market added. In a unified index model, the same catalog ballooning across dozens of country-language combinations drives index size, cost, and performance in the wrong direction. In multi-index architectures, the problem shifts: teams are managing multiple indexes, multiple ingestion endpoints, and the custom processes needed to break a unified catalog into market-specific versions and route each one correctly. The infrastructure differs, but the underlying burden is the same. The platform’s data model is working against how the business actually manages its catalog. - Maintenance overhead
Updating a single product attribute should be simple but in multi-market architecture, it rarely is. Whether a business is pushing changes through one index or many, that update needs to reach every market variant of every affected product. In practice, that means triggering updates across 16 separate ingestion endpoints, validating that each one processed correctly, and building the monitoring and reconciliation processes to catch anything that falls out of sync. The more markets a business serves, the more complex that orchestration becomes, and the more engineering time gets consumed by data plumbing rather than anything that moves the business forward. - Implementation complexity and data integrity
Architecting a multi-market search deployment requires significant upfront investment, and the ongoing maintenance burden grows with every market added. More indexes mean more surfaces where data can fall out of sync: a description updated in one market but not others, a price discrepancy, a missing translation. The result is a system that demands continuous engineering attention just to stay consistent, before any work gets done on the experience itself.
What makes this particularly frustrating is that it doesn’t match how most of these organizations actually manage their data internally. The majority of global enterprises maintain a single version of each product in their systems — with localized attributes like translated names, regional descriptions, and market-specific pricing layered on top of a shared core record. The duplication requirement asked them to break that model apart just to get search working, adding engineering overhead before they’d even begun to configure the platform itself.
That feedback came consistently enough, and from enough different customers and prospects across B2B and B2C, that it became clear this needed a proper solution rather than an accepted constraint.
The Solution: One Source, Every Storefront
Coveo’s new Multi-Market Source feature fundamentally changes how catalog data can be structured in the Coveo index. Instead of requiring a separate document and source per market, customers can now maintain a single product record with market-specific attributes, localized names, descriptions, prices, units of measure, and use that one record to power storefronts across every market they serve.
At the same time, this doesn’t force a single approach. For some organizations, maintaining segmented catalogs across markets reflects how their business is structured; separate product systems, regional ownership, or operational boundaries. Coveo supports that model as well.
The difference is that customers now have a choice. Where previously they were constrained to duplicating data across indexes, they can now centralize, segment, or combine both approaches based on what fits their business.
This aligns with how most enterprise customers already have their data structured internally, which means the architecture of the platform finally matches the architecture of the business, rather than working against it.
In practice, this means:
- One source, one document per product (when you want it). Localized values like translated names, regional descriptions, and market-specific pricing are defined at the attribute level, not the document level, so the catalog stays clean and manageable regardless of how many markets it serves.
- Flexible catalog composition. Whether operating from a unified global catalog or maintaining segmented market-specific structures, both approaches are supported. Adding a new market no longer requires rethinking your entire data model.Full commerce AI compatibility. ML models, personalization, reporting, and all commerce features work natively with this structure. There are no capability trade-offs for choosing a simpler architecture.
- Segmented merchandising control. Each market retains full autonomy over its own rules, rankings, and shopper experience. Despite drawing from a shared source, markets can be configured independently with their own relevance tuning, boosting rules, and personalization logic.
- Simpler architecture. Teams that choose a unified approach eliminate duplication and reduce operational overhead. Teams that require segmentation can maintain it without compromising capability.
The impact on index size alone can be significant. One organization in early conversations would have had roughly 178 million records under a fully duplicated model. With multi-market support, that same catalog can be structured at approximately 6 million—dramatically reducing complexity, cost, and performance overhead.
Who This Is Built For
This capability was designed with complex, global enterprises in mind — organizations managing large catalogs across many markets, where the existing architecture was creating friction at every level: implementation, maintenance, and scale.
The use cases that shaped this feature illustrate the kinds of challenges that kept coming up in conversations with global enterprises:
- A global distributor maintaining 16 regional sources faces hours of rebuild time every time product changes need to propagate across the system — operational time that simply disappears with a unified source model.
- An enterprise with 800,000 global SKUs operating across 75 countries needs an architecture that can grow with the business without multiplying implementation costs every time a new market is added.
- A manufacturer with 2 million products across 33 country-language combinations needs confidence that a modern platform can handle that scale without forcing their team to build elaborate workarounds just to get there.
That said, this isn’t only a solution for the largest deployments. Whether a customer has thousands of products or millions, the operational gains — less duplication, simpler maintenance, faster time-to-value — are real and immediate. The relevant factor isn’t catalog size; it’s the complexity of the multi-market setup and how much friction that complexity is currently creating for the teams responsible for building and maintaining it.
A Better Starting Point for Migration
There’s another dimension worth highlighting for organizations currently evaluating platforms: multi-market support meaningfully reduces migration friction.
Most enterprises running on legacy search infrastructure already have their data structured the way Coveo now supports — a single product record with localized attributes. Previously, migrating to modern SaaS search solutions meant re-engineering that structure to fit a duplication-based model before any configuration work could even begin. That added implementation time, cost, and risk at the very point in the process where businesses most need momentum.
That barrier is now gone. Organizations can bring their existing data model into Coveo without having to reshape it first, which makes onboarding faster, reduces the professional services burden, and shortens the path from deployment to realized value. For teams that have delayed a platform move partly because of migration complexity, this removes one of the most consistent friction points from the equation.
Take a Closer Look
Coveo’s multi-market capability is heading into early access soon. If your organization manages commerce across multiple countries, languages, or brands and the current architecture is generating more overhead than it should — it’s worth seeing what’s changed and what it could mean for your deployment.