Benchmarking Vector Database Performance for RAG Systems

Vector database benchmarks are easy to get wrong because the headline number most marketing emphasizes (raw query latency) is only meaningful alongside a second number almost everyone undersells: recall, the accuracy of the approximate nearest-neighbor search itself.

Recall vs latency: the fundamental trade-off

Most production vector databases use approximate nearest neighbor (ANN) search (HNSW, IVF, and similar algorithms) rather than exact search, because exact search doesn’t scale to large datasets at acceptable latency. ANN algorithms expose tunable parameters that trade recall (how often the true nearest neighbors are actually returned) against latency and resource usage — a benchmark reporting only latency, without stating the recall achieved at that latency, is reporting half the story. A system returning answers in 5ms with 70% recall is not necessarily better than one returning answers in 20ms with 98% recall — it depends entirely on how much your downstream RAG quality depends on retrieval accuracy.

Why recall matters disproportionately for RAG specifically

In a RAG pipeline, a missed relevant document due to poor retrieval recall isn’t just a minor accuracy hit — it can mean the LLM generates a confidently wrong answer because the actually-relevant context was never retrieved at all, with no model-level signal that anything went wrong. This makes recall a correctness issue, not merely a quality-of-service metric, distinguishing vector database benchmarking from many other latency-focused database benchmarks.

Indexing time and update latency

Beyond query performance, indexing throughput (how fast can you ingest and index new vectors) and update/delete latency matter heavily for applications with frequently changing data — a vector database that queries fast but takes hours to reindex after a bulk update may be unsuitable for a use case needing near-real-time freshness, even if query benchmarks look excellent.

Filtering performance: a common blind spot

Real RAG applications frequently need filtered search (find similar vectors and matching a metadata condition, like “only documents from this tenant” or “only documents updated in the last 30 days”) — pure ANN benchmark numbers without filtering often look very different from filtered-query performance, since filtering can substantially change which ANN optimization paths are usable. Benchmark with realistic filter patterns, not just unfiltered nearest-neighbor search, if your application needs filtering (most do).

Memory and cost scaling with dataset size

Some ANN index structures (particularly HNSW) require keeping a substantial graph structure in memory for fast queries — benchmark at the dataset scale you actually expect to operate at, since memory and cost characteristics often don’t scale linearly, and a benchmark run against a small sample dataset can be a poor predictor of behavior at your real production scale.

Embedding dimensionality’s effect on everything

Higher-dimensional embeddings (common with newer, larger embedding models) increase memory footprint, indexing time, and query latency roughly proportionally — when comparing vector database performance across different embedding models, make sure you’re not confounding “this database is faster” with “this benchmark happened to use a lower-dimensional embedding model.”

A practical benchmarking checklist

1. Report recall alongside latency, not latency alone.
2. Benchmark with realistic filter patterns if your application uses them.
3. Test at production-representative data scale, not a small sample.
4. Measure indexing/update throughput if your data changes frequently.
5. Hold embedding dimensionality constant when comparing databases against each other.

Takeaway: a vector database benchmark that doesn’t report recall alongside latency is incomplete — for RAG specifically, recall is closer to a correctness property than a performance nice-to-have, since missed retrieval directly causes confidently wrong generated answers.