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Vector Database Cost Calculator
Compare Pinecone, Weaviate, Qdrant, Supabase pgvector, and Chroma. Know your vector DB costs before you build.
How vector database pricing works
Vector databases charge for two things: storage (keeping your embeddings) and queries (similarity searches at runtime). Storage is cheap at small scale but grows linearly. Queries dominate costs at high traffic.
You also pay your embedding provider once to convert documents into vectors. If you ever switch models, you pay that fee again — so picking the right model upfront matters.
Pinecone vs Weaviate vs Qdrant vs Supabase vs Chroma
- Pinecone — Fully managed, zero ops. Best for teams that want to ship fast. Serverless billing fits bursty workloads.
- Weaviate — Excels at hybrid search (BM25 + vector). Great for semantic search where keyword context matters.
- Qdrant — Best price/performance at scale. Supports scalar and product quantization to shrink storage 4–32×.
- Supabase pgvector — Best for Postgres users. Vectors live alongside your relational data. No separate infra.
- Chroma — Open-source, free to use. You own the infra. Great for prototyping or private deployments.
How to reduce vector database costs
- •Reduce dimensions — Use
text-embedding-3-smallwith 512 or 768 dimensions instead of 1536. Quality drops slightly but costs fall 3×. - •Quantization — Qdrant supports scalar (int8) and product quantization, reducing storage 4–32× with minimal accuracy loss.
- •Chunk wisely — Smaller chunks mean more vectors. Aim for 200–500 tokens per chunk with overlap rather than sentence-level splitting.
- •Cache frequent queries — If the same queries repeat, cache results in Redis to skip vector DB calls entirely.
- •Use free tiers — Pinecone has a free tier (100K vectors). Supabase free tier includes 500MB. Good for prototyping.
🔌 Building a RAG pipeline? Also check the Agent Workflow Cost Calculator to estimate your LLM costs alongside vector DB spend.