Researched and Edited by Rajat Gupta
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Editor's Summary · Vector Database Software
Start with where your data already lives, because that decides more than benchmark numbers do. If you run Postgres, pgvector keeps embeddings beside the rows they describe and removes an entire system from the stack — for most applications that is the right answer and the cheapest one. If you run MongoDB, Atlas Vector Search does the same thing. Elasticsearch, Redis, ClickHouse and SingleStore all now do vectors too.
Reach for a dedicated engine when scale or recall genuinely demands it. Pinecone is the managed default, Milvus and Qdrant are the open-source options at serious scale, and Weaviate and Chroma sit closer to the developer-experience end.
The cost model deserves more attention than it usually gets. Keeping a large index in memory is expensive, which is why turbopuffer and Upstash Vector serve from object storage and bill per request — a much better fit for multi-tenant products where most indexes are idle.
And check filtering carefully. Nearly every engine claims metadata filtering; the difference is whether it filters before or after the nearest-neighbour search, which changes both recall and latency.
Quick picks for Vector Database Software
- Best if you already run Postgres — pgvector
- Best managed dedicated engine — Pinecone
- Best open-source at scale — Milvus
Who gets the most from Vector Database Software
- 1Engineers building retrieval-augmented generation over a document corpus
- 2Platform teams choosing between extending Postgres and running a dedicated engine
- 3Teams running multi-tenant search where most indexes are idle most of the time
How to choose Vector Database Software
Check whether your current database already supports vectors before adding a system to operate — for most applications pgvector or Atlas Vector Search is sufficient and removes a synchronization problem. If you do need a dedicated engine, test with your real corpus and your real filters, since pre-filter versus post-filter behavior changes results in ways a generic benchmark will not show. Weigh the cost model against your access pattern: an index that is queried rarely does not justify provisioned memory.