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.
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
Total
Total embeddings stored.
- 2
Embedding
Embedding dimensions — 1536 for OpenAI text-embedding-3-small, 768 for many open models.
- 3
Daily
Daily nearest-neighbour searches.
- 4
Daily
Daily new vector writes.
- 5
How
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.
Sources & References
- Pinecone pricing — Pinecone
- Cloudflare R2 pricing — Cloudflare
- Turbopuffer pricing — Turbopuffer
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