Vector database cost: what powers similarity search

Quick answer

Vector database cost is storage of embeddings (vector count times dimensions) plus query compute for similarity search, whether on a managed service (Pinecone, managed pgvector, cloud vector stores) or self-hosted. Cost scales with how many vectors you store, their dimensionality, and your query volume. The levers: reduce dimensionality where quality allows, prune stale vectors, right-size the index, and cache frequent queries. Fewer, smaller vectors and fewer queries mean a smaller bill.

Vector databases store embeddings and serve similarity search, the retrieval backbone of RAG and semantic search. Their cost is storage of the vectors plus the compute to search them, and it scales with how many vectors you keep, how large each is, and how often you query. Whether managed or self-hosted, those drivers are the same.

Storage plus query compute

Driver	Effect
Vector count	Storage and index size
Dimensionality	Bytes per vector (bigger embeddings cost more)
Query volume	Search compute
Index type / replicas	Memory and compute for low-latency search

Storage scales with vector count times dimensionality, so a million 1,536-dimension embeddings take far more than a million 384-dimension ones. Query compute scales with search volume and index configuration (in-memory indexes for low latency cost more). Managed services (Pinecone, cloud vector stores) bundle this into pods or per-usage pricing; self-hosted (pgvector, open-source engines) pays the underlying compute and storage.

Managed vs self-hosted

Managed vector databases charge a premium for operations and scaling; self-hosted (pgvector on your own Postgres, or an open-source engine on your compute) is cheaper per unit but you run it. For moderate scale, pgvector on an existing database is often the cheapest start; for large scale or demanding latency, a purpose-built managed service may be worth its premium.

Controlling vector database cost

Reduce embedding dimensionality where retrieval quality allows (smaller vectors cut storage and search cost directly), prune stale or duplicate vectors so the index does not grow unbounded, right-size the index type and replica count to your latency need (not every workload needs everything in memory), cache frequent query results, and for RAG retrieve only what you need. Because the bill is vectors times size plus queries, fewer and smaller vectors and fewer queries are the levers, part of RAG cost.

FAQ

What drives vector database cost?

Storage of embeddings (vector count times dimensionality) plus query compute for similarity search, and the index configuration (in-memory indexes for low latency cost more). So the drivers are how many vectors you store, how large each is, your query volume, and your latency requirements. Managed services bundle this into pods or per-usage pricing; self-hosted pays the underlying compute and storage.

How do I reduce vector database cost?

Reduce embedding dimensionality where retrieval quality allows (smaller vectors cut storage and search cost directly), prune stale or duplicate vectors so the index does not grow unbounded, right-size the index type and replica count to your latency need, cache frequent query results, and for RAG retrieve only what you need. Fewer and smaller vectors and fewer queries are the main levers.

Is pgvector cheaper than a managed vector database?

Often, for moderate scale. Running pgvector on an existing Postgres database avoids the premium of a managed vector service and reuses infrastructure you already have, making it a cheap start. For large scale or demanding latency, a purpose-built managed service may be worth its premium for the operations and scaling it handles. The crossover is scale and latency needs.

Does embedding dimensionality affect cost?

Yes, directly. Storage scales with vector count times dimensionality, so a million 1,536-dimension embeddings take far more storage and search compute than a million 384-dimension ones. Using a smaller embedding model or reducing dimensionality where retrieval quality still meets your bar cuts both storage and query cost proportionally.

Should everything be in memory in a vector database?

Not necessarily. In-memory indexes give the lowest search latency but cost the most in memory and compute. Not every workload needs the fastest possible search, so right-sizing the index type and replica count to your actual latency requirement, rather than defaulting to everything in memory, controls cost. Reserve full in-memory indexes for latency-critical search.

Does C3X estimate vector database cost?

C3X prices the infrastructure behind a vector database (managed service resources or the compute and storage for self-hosted pgvector or open-source engines) from your Terraform, so the base cost appears before deploy. Query volume and vector count are usage inputs you model to estimate the full cost.

What to do next

Price your vector database before you build. C3X reads your Terraform and prices your resources against a live catalog. Start with the quickstart.