what place do vector-native databases have in 2025? I feel using pgvector or redisearch works well and most setups will probably be using postgres or redis anyway.
Chroma supports multiple search methods - including vector, full-text, and regex search.
Four quick ways Chroma is different than pgvector: Better indexes, sharding, scaling, and object storage.
Chroma uses SPANN (Scalable Approximate Nearest Neighbor) and SPFresh (a freshness-aware ANN index). These are specialized algorithms not present in pgvector. [1].
The core issue with scaling vector database indexes is that they don't handle `WHERE` clauses efficiently like SQL. In SQL you can ask "select * from posts where organization_id=7" and the b-tree gives good performance. But, with vector databases - as the index size grows, not only does it get slower - it gets less accurate. Combining filtering with large indexes results in poor performance and accuracy.
The solution is to have many small indexes, which Chroma calls "Collections". So, instead of having all user data in one table - you shard across collections, which improves performance and accuracy.
The third issue with using SQL for vectors is that the vectors quickly become a scaling constraint for the database. Writes become slow due to consistency, disk becomes a majority vector indexes, and CPU becomes clogged by re-computing indexes constantly. I've been there and ultimately it hurts overall application performance for end-users. The solution for Chroma Cloud is a distributed system - which allows strong consistency, high-throughput of writes, and low-latency reads.
Finally, Chroma is built on object storage - vectors are stored on AWS S3. This allows cold + warm storage tiers, so that you can have minimal storage costs for cold data. This "scale to zero" property is especially important for multi-tenant applications that need to retain data for inactive users.
dedicated solutions have more advanced search features enable more accurate results. search indexing is resource intensive and can contend for resources with postgres/redis. the cost and speed benefits are naturally more pronounced as data volume scales.
for example - chroma has built in regex+trigram search and copy-on-write forking of indexes. this feature combo is killer for the code-search use case.