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How to Use Embeddings DB Cost

Estimates float32 storage with index overhead, then applies snapshot vendor pricing for storage, queries, and ingest. Returns the cheapest option plus all four monthly costs.

Bottom Line

Compute monthly cost across four vector DBs (Pinecone Standard, Postgres+pgvector managed, LanceDB on R2, Turbopuffer) for one workload.

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Embeddings DB Cost

Pinecone, Postgres+pgvector, LanceDB, or Turbopuffer — cheapest for your workload.

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What It Does

Use the calculator with intent

Estimates float32 storage with index overhead, then applies snapshot vendor pricing for storage, queries, and ingest. Returns the cheapest option plus all four monthly costs.

Engineers and ops planning RAG infrastructure, especially when balancing cost against query volume.

Interpreting Results

LanceDB on R2 wins at small scale. Turbopuffer is competitive for cold-tier read-heavy workloads. Pinecone is the easiest operational choice but most expensive at scale.

Input Steps

Field by field

  1. 1

    Total Vectors Stored

    Total embeddings stored.

  2. 2

    Embedding Dimensions

    Embedding dimensions — 1536 for OpenAI text-embedding-3-small, 768 for many open models.

  3. 3

    Daily Query Volume

    Daily nearest-neighbour searches.

  4. 4

    Daily Write Volume

    Daily new vector writes.

  5. 5

    Retention Period

    How long vectors stay in the index. Affects storage growth.

    Quantised indices (int8, binary) cut storage 2 to 32× — adjust dim downward to approximate.

Common Scenarios

Use realistic starting points

Small RAG

Vector count

100000

LanceDB usually under $5/mo.

Large RAG

Vector count

10000000

Storage dominates; Postgres baseline becomes attractive.

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FAQ

Questions people ask next

The short answers readers usually want after the first pass.

Both have viable cloud and self-hosted offerings; the four chosen here cover the price range. Open a request issue if you want them added.

Sources & References

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