COMPARE:
Pinecone or Postgres pgvector for embeddings
Pinecone and Supabase pgvector both do storing embeddings and searching them, and this page puts them on the same seven questions. Mamba Labs builds Apify actors. We have nothing of our own in this category, so this page is two products and no pitch.
The verdict
If your app already has a Postgres database, pgvector is a column and Pinecone is a second service. Start with the column and move when it stops keeping up.
The same questions, both tools
| Pinecone | Supabase pgvector | |
|---|---|---|
| What it actually does | Stores embeddings and returns the nearest ones fast, with filtering and namespaces on top. | Stores embeddings in a Postgres column and returns the nearest ones with an index on that column. |
| Coverage and hit rate | Serverless indexes that scale a long way, with hybrid search and metadata filtering. | Good to a few million vectors on sensible hardware, and it slows before a dedicated engine does. |
| What it costs | By storage and by read and write units on the serverless tier, with a free tier for a small index. | Nothing beyond the database you were already paying for, which is the whole argument. |
| How it fits a workflow | A client library and an API. It is one more service in the stack, with its own keys and its own bill. | It is a column. The similarity search joins to your users and your documents in one query. |
| Where the data comes from | Whatever you write into it. | Whatever you write into it. |
| What it takes to set up | Minutes to a working index. | One extension and one index. There is no second service and no second key. |
| Where it stops | Your vectors live away from your relational data, so every join is something you write yourself. | At real scale the tuning becomes your problem, and that is when a dedicated engine starts to pay. |
The same seven questions are asked on every compare page here, in this order, so two of these pages can be read against each other.
What each is actually better at
- Pinecone wins on coverage and hit rate, what it takes to set up. A managed vector database that stays fast when the index gets large.
- Supabase pgvector wins on what it actually does, what it costs, how it fits a workflow, where it stops. Vectors as a column in the Postgres database the rest of the app already uses.
- Nobody wins on where the data comes from. Pinecone and Supabase pgvector give the same answer there, and we are not going to invent a difference.
Try them yourself
What we would actually do
Almost every retrieval feature that ships is under a million vectors. At that size pgvector on hardware you already pay for is faster to build and easier to reason about. The similarity search joins straight to your users and your permissions. Pinecone earns its place at scale and when relevance quality is the product rather than a feature of it. Building on it first is the common mistake. You take on a second bill, a second set of keys and a consistency problem, for a scaling issue you may never reach. Move when a query gets slow, and you will know.