How Website Search Breaks at Scale (10k+ Pages)

Techniques that work for a few hundred pages don't hold up when a site grows into the tens or hundreds of thousands of pages. At that point, search becomes an engineering problem as much as a product problem.

Full crawls become prohibitively expensive

Reindexing the whole site on every change wastes bandwidth and compute. Implement incremental indexing: detect content changes, enqueue only changed documents, and use a change feed or webhooks from your CMS to trigger updates. This keeps the index fresh without the cost of repeated full crawls.

Vector search needs to be efficient

Semantic search at scale relies on approximate nearest neighbor (ANN) algorithms that trade a tiny amount of accuracy for large gains in speed and storage efficiency. Use proven libraries and indexes (HNSW, IVF, or FAISS variants) and shard your index thoughtfully. Monitor recall-to-latency tradeoffs and tune parameters (efConstruction, nlist, etc.) to match your latency SLAs.

Hybrid ranking is mandatory

Pure semantic relevance can surface thematically related but operationally incorrect pages. Combine vector similarity with classic signals: title matches, exact-term boosts, freshness, CTR, and business rules (promoted docs). A hybrid scorer that blends these signals yields results that are both relevant and reliable.

Storage and retrieval costs add up

Large indexes consume storage and I/O. Use chunking strategies to split long documents into meaningful passages, compress embeddings where possible, and apply lifecycle rules that archive cold content. Consider a multi-tiered index: a fast, hot index for recent and high-traffic content plus slower archival storage for older material.

Operational visibility and SLOs

At scale, search must be monitored like any other critical service. Track index freshness, query latency percentiles, error rates, and worst-case tail latencies. Define SLOs for both freshness and latency, and surface alerts when indexing lags or query-level errors spike.

Scaling search requires changes across architecture, data, and operations. Adopt incremental indexing, efficient vector indexes, hybrid ranking, and strong monitoring to keep search fast and useful as your content library grows.

How Website Search Breaks at Scale (10k+ Pages)

Full crawls become prohibitively expensive

Vector search needs to be efficient

Hybrid ranking is mandatory

Storage and retrieval costs add up

Operational visibility and SLOs

Built to handle large content libraries with low latency.

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