We unified our AI stack into one Postgres instance - and cut our infra and backend code by 90%!
Most teams use 3+ databases already:
- A relational / document store like mongo or postgres,
- A vector DB like Chroma or Pinecone for embeddings,
- Redis for caching, and
- Usually an analytics warehouse, like Athena
You of course have to write and maintain all the glue code & data transformations for each of them. But more importantly, you have to take care of the schema evolution which can be a nightmare when dealing with multiple databases!
Instead we run everything on TigerData (creators of TimescaleDB)’s PostgreSQL platform (TimescaleDB + pgVector + pgAI).
Most teams use 3+ databases already: - A relational / document store like mongo or postgres, - A vector DB like Chroma or Pinecone for embeddings, - Redis for caching, and - Usually an analytics warehouse, like Athena
You of course have to write and maintain all the glue code & data transformations for each of them. But more importantly, you have to take care of the schema evolution which can be a nightmare when dealing with multiple databases!
Instead we run everything on TigerData (creators of TimescaleDB)’s PostgreSQL platform (TimescaleDB + pgVector + pgAI).