AI ECONOMICS · VECTOR DB
Embeddings DB Cost
Cheapest vector DB for your workload? Compare Pinecone, Postgres+pgvector, LanceDB, and Turbopuffer side-by-side.
Result
Cheapest: LanceDB · 7.15 GB index size
Vendor Cost Comparison
Monthly cost per vendor for this workload — cheapest highlighted.
Transparent by design — computed in your browser from a published formula and sourced rates, not a black box. Data verified 2026-05-25. Sources: Pinecone ↗ · Cloudflare R2 ↗ · DigitalOcean Managed Databases ↗ · Turbopuffer ↗ . Full methodology →
How to use it
- Enter your vector count, your embedding dimension, your queries per day, and your ingest (new vectors) per day. The tool estimates storage from vector count and dimension, then projects monthly cost across four vendors, so the two inputs that matter most are how many vectors you store and how heavily you query them, since one drives storage cost and the other drives operation cost. If you evict old vectors on a retention schedule, enter the vector count you actually keep, because storage is sized from that number.
- Read the per-vendor monthly cost for Pinecone, Postgres+pgvector, LanceDB, and Turbopuffer, the cheapest vendor, the cheapest monthly cost, and your estimated storage in gigabytes. Each vendor's pricing notes spell out the assumptions (plan minimums, per-gigabyte storage, per-million-operation rates) so you can see why the ranking comes out the way it does for your specific workload rather than a generic one.
- Use the ranking to understand which vendor wins at your scale, because the answer changes dramatically with workload shape. Managed services like Pinecone carry plan minimums that dominate at small scale, self-hosted Postgres+pgvector is cheap until storage grows large, and object-storage options like LanceDB shine for large-but-cold datasets. The cheapest vendor for a hobby project is rarely the cheapest at production query volume.
- Treat the dollar figures as infrastructure cost only, not total cost. The notes flag that some options exclude self-hosted compute or operational effort, so a vendor that looks cheapest on storage may cost more once you account for the engineering time to run it. Weigh the savings against your team's capacity to operate a database, especially for the self-hosted options.
- Re-run as your vector count grows, your query volume scales, or vendor pricing updates, since the crossover point where one vendor overtakes another shifts with workload. For a RAG or AI product, pair this with the AI stack cost tool so the vector database cost sits inside the full infrastructure picture rather than being optimised in isolation.
Questions people usually ask
What decision is Embeddings DB Cost designed for?
Embeddings DB Cost helps teams pinecone, postgres+pgvector, lancedb, or turbopuffer — cheapest for your workload. before committing budget, pricing, or operating changes.
How can I get decision-grade output quality?
Use validated baseline numbers, run downside and upside scenarios, and align assumptions with your real cadence and constraints.
Is this legal, tax, or accounting advice?
No. Outputs are business planning estimates and should be reviewed with qualified professionals when required.
Is this free and private?
Yes. Tools run client-side in your browser with no signup.
Related Resources
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Precomputed reference table
Skip the inputs: this tool's engine also powers a full reference grid you can cite or download as CSV.
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