While ecommerce catalogs routinely stretch into the tens of millions of SKUs, product discovery has quietly become one of the biggest strategic bottlenecks for online retailers and marketplaces. What may have started as a simple search bar suddenly reveals deep technical and experience gaps that risk conversion, loyalty, and revenue.

That’s when product discovery silently crumbles. Not from poor intent signals. Not from misconfigured search. But because most systems were engineered for a fraction of this chaos.

The scale illusion: Growth exposes discovery's limits

Small and mid-sized catalogs make product discovery feel effortless:

• Search delivers spot-on relevance
• Filters load instantly
• Merchandising rules stay simple
• Index updates hum along

Then scale hits. New products flood in. Categories explode. Variants multiply. Localization demands surge.

When product discovery turns brittle:

• latency spikes
• results grow erratic
• new products linger unseen for hours or days
• merch teams battle the system instead of crafting experiences

The truth? Product discovery doesn’t scale linearly. It fractures.

Why 10+ million SKUs mark the breaking point

Eight-figure catalogs rewrite the rules. Here’s why traditional discovery buckles:

1. Scale changes the nature of product discovery

At smaller catalog sizes, basic keyword search and manual merchandising can get results. But once a catalog crosses into millions of SKUs, traditional systems strain under sheer volume and complexity.

Search that relies on exact keyword matching or static rules often:

• fail to handle variations in product terms and synonyms,
• return irrelevant or incomplete results, and
• slow down as data volume increases.

Modern product discovery interprets intent, not just match words which is a challenge that becomes significantly harder as catalogs grow exponentially.

2. Performance and latency drive revenue

Performance, particularly search response time, is tightly linked to shopper behavior.

As catalogs scale:

• slower index refresh cycles emerge,
• complex queries take longer,
• and friction in search responses compounds across millions of users.

At volume, milliseconds matter and performance issues translate into lost money, along with slower results.

3. Relevance gets harder when catalogs are huge

Traditional relational databases and simple text search cannot keep up with relevance demands at scale. These systems often only support basic full-text matching while missing nuances like:

• synonyms,
• long-tail semantic meaning,
• misspellings or abbreviations that real shoppers enter.

Product relevance models must evolve beyond basic keyword scoring to handle evolving business environments effectively.

4. Data issues compound as catalogs grow

Large catalogs often have issues with:

• inconsistent metadata,
• missing attributes,
• duplicated SKUs,
• and unstructured product descriptions.

These data quality gaps break filters, make facets inaccurate, and cripple search relevance.

Large catalogs with incomplete or inconsistent attributes cause product filters to:

• return empty results,
• overload shoppers with irrelevant facets,
• or fail to narrow down effectively.

5. Filtering and navigation often collapse

Faceted search (filter-based refinement) is essential to navigate millions of products. But poor data and inadequate indexing make filters:

• slow,
• inconsistent,
• or completely broken under load.

When filters don’t work, shoppers can’t refine results leading to frustration and abandonment much earlier in the discovery funnel.

The real cost: Slow leaks that sink growth

Discovery failures don’t explode. They erode:

• Search conversion dips significantly
• Zero-result queries climb
• Manual merchandising dependency soars
• Long-tail and new products stay invisible

Worse, confidence evaporates. Teams halt experiments. Merchants fear tweaks. Innovation stalls. The growth those catalogs promised? It grinds to a halt.

Why legacy search stacks collapse at scale

Most platforms hail from a simpler era:

• Sub-10M catalogs
• Uniform product data
• Rule-based boosts
• Batch indexing

At true scale, they choke. Big businesses need:

• Distributed indexing for extreme loads
• AI relevance from massive behavioral datasets
• Sub-50ms queries
• Continuous, adaptive learning

This demands architectural reinvention, not tweaks.

Discovery as core infrastructure

At massive scale, product discovery isn’t a frontend add-on. It’s infrastructure like payments or logistics. It must deliver:

• Resilience under peak traffic
• Agility for endless change
• Zero-degradation scaling

Early adopters compound their edge. Others fight forever.

Scale-proof your discovery now

If your catalog nears or tops 10 million SKUs, breakdown isn’t "if"—it’s "when." Act early:

• Audit rigorously: Benchmark relevance, speed, and freshness against enterprise peers.
• Upgrade strategically: Choose platforms built for 100M+ SKUs with AI-powered Search.
• Prioritize holistically: Bake discovery into your tech stack as a growth engine.

At this level, superior product discovery unlocks sustainable scale while enhancing UX.