Vector DB Showdown: Pinecone vs Weaviate vs Chroma
Tested three vector databases for a RAG system. The conclusion was surprisingly straightforward.
Building RAG means you need a vector DB
We had to build an internal document search system. The existing keyword search couldn't find "March performance marketing report" when someone searched for "this month's marketing results." We needed semantic search, which means converting text to vectors and running similarity searches.
Too many vector DB options out there. Pinecone, Weaviate, Chroma, Milvus, Qdrant, pgvector... Couldn't test them all, so I picked three to compare.
Test conditions
12,340 documents. Each embedded as a 768-dimension vector (using OpenAI ada-002). Ran 100 search queries to compare response time and search accuracy.
Pinecone: easiest, but expensive
Setup was dead simple. Create an index in the dashboard, grab an API key, done. No server management. Three lines of code to insert vectors and search.
Search speed: 34ms average. Accuracy was good too. But the cost was a problem. At our usage level on the Serverless plan, it came out to about $73/month. For 12,340 documents and ~500 searches per day, that felt steep.
(There's a free tier, but the 1-index limit makes it sketchy for production.)
Weaviate: feature-rich, but complex
Can run locally via Docker, so no cost concerns. Built-in hybrid search (vector + keyword) was a nice bonus.
But initial setup was noticeably more complex than Pinecone. You need to define a schema and configure modules. Followed the official docs and it still took 2 hours before the first search worked.
Search speed: 52ms average. Slower than Pinecone but perfectly usable. Hybrid search clearly improved accuracy compared to vector-only search. That's a big plus.
Chroma: best for prototyping
One pip install and you're running. No server needed. SQLite-based, everything stored in a single file.
For rapid development and testing, it was the best experience. But once documents exceeded 10,000, search speed dropped noticeably. Average 187ms. It was 23ms with 1,000 documents.
Stability in server mode was also a bit shaky. The process crashed once and some data was lost. You need a separate backup strategy.
The conclusion was surprisingly simple
Prototyping: Chroma. Fast to start with, switch later if needed.
Production (can manage infra): Weaviate. Free and feature-rich. Hybrid search is particularly good.
Production (don't want infra): Pinecone. Pricey, but zero ops.
We went with Weaviate. We had DevOps people, so running Docker wasn't a burden, and hybrid search was a great fit for keyword-sensitive internal document search.
Though in 6 months, if pgvector catches up, we might switch again. This field moves too fast to commit with confidence.