Evidence-first authority page
Vector Database Comparison: What to Actually Evaluate
The practical dimensions that differentiate vector databases beyond raw benchmark speed numbers.
Direct answer
When comparing vector databases, prioritize how well each integrates with your existing stack, its metadata filtering capabilities alongside vector search, and its behavior at your actual expected scale — raw benchmark speed numbers from vendor marketing rarely reflect performance on your specific data distribution and query patterns.
Last reviewed 2026-08-29
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Why vendor benchmarks are a weak signal
Vector database benchmarks are typically run on specific, favorable datasets and query patterns chosen by the vendor — actual performance on your data (its dimensionality, distribution, and update frequency) can differ meaningfully. Test candidates against a realistic sample of your own data before deciding based on published benchmarks alone.
Dimensions that matter beyond raw speed
- Metadata filtering — can you combine vector similarity search with structured filters (date ranges, categories) efficiently, which most real applications need.
- Update patterns — how well does the database handle frequent inserts/updates versus a mostly-static index.
- Operational overhead — self-hosted open source options (Qdrant, Weaviate, Milvus) versus managed services (Pinecone) trade control for operational simplicity.