Vector Database Cost Estimator
Enter your dataset size and query volume to compare monthly costs across Pinecone, Weaviate, Qdrant, Milvus, Chroma, and more.
Each vector = one embedded chunk of text
Estimated Monthly Cost by Provider
| Provider | Plan | Storage | Est. Monthly Cost | Notes |
|---|
Vector Database Cost Estimator
Vector databases store high-dimensional embeddings and enable fast approximate nearest-neighbor (ANN) search. They are a core component of Retrieval-Augmented Generation (RAG) pipelines, semantic search engines, recommendation systems, and AI-powered applications.
Key Cost Factors
- Storage — Number of vectors × dimensions × bytes per float (4 bytes for float32). 1M vectors at 1,536 dims ≈ 5.7 GB.
- Compute / Pods — Managed databases charge for compute resources (pods, replicas) separately from storage.
- Query volume — Some providers charge per query or per read unit.
- Index type — HNSW indexes are faster for queries but use more memory than flat indexes.
Self-Hosted Alternatives
- Chroma — Fully open-source, runs locally or on your own server. Zero licensing cost.
- Qdrant — Open-source with Docker; cloud option available. Best cost control at scale.
- Weaviate — Open-source with powerful filtering and hybrid search.
- Milvus — Enterprise-grade open-source vector DB from Zilliz.
Related Tools
- Embedding Cost Calculator — calculate the cost to generate embeddings for your vectors
- RAG Pipeline Cost Calculator — full RAG cost including embedding, vector DB, and LLM inference
- Multi-Model Cost Comparison — compare LLM inference costs for your RAG queries