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The Vector Database Cost Calculator estimates monthly hosting expenses for vector storage and similarity search across managed services (Pinecone, Weaviate Cloud, Qdrant Cloud) and self-hosted options. It factors in vector dimensions, total vectors, query volume, and storage requirements.
Formula
- V
- Number of Vectors (vectors) — Total stored vector embeddings
- D
- Dimensions (dimensions) — Dimensionality of each vector (768, 1024, 1536, 3072)
- Q
- Monthly Queries (queries/month) — Similarity search queries executed per month
- QPS
- Queries per Second (queries/sec) — Peak query throughput requirement
Guida passo passo
- 1Enter the number of vectors and their dimensionality (768, 1024, 1536, 3072)
- 2Specify your monthly query volume and required queries per second (QPS)
- 3Select the vector database provider and deployment tier
- 4Compare costs across providers and self-hosted alternatives
Esempi risolti
Errori comuni da evitare
- ✕Over-dimensioning vectors — 768 or 1024 dimensions often perform nearly as well as 1536/3072 at much lower storage cost
- ✕Not planning for metadata storage, which can be 2-5x the size of the vectors themselves
- ✕Selecting a plan based on vector count alone without considering query throughput requirements
Domande frequenti
Which vector database is cheapest?
For small workloads (<100K vectors), most providers offer free tiers (Pinecone free tier: 100K vectors, Qdrant Cloud: 1GB free). For medium workloads, self-hosted Qdrant or Chroma on a $40-80/month VM is cheapest. For large-scale production, Pinecone Serverless offers competitive pay-as-you-go pricing.
Do I need a dedicated vector database or can I use PostgreSQL with pgvector?
For under 1M vectors with moderate query loads, pgvector in PostgreSQL is a solid and cost-effective choice, especially if you already run PostgreSQL. For >1M vectors, high QPS requirements, or features like automatic scaling and replication, dedicated vector databases outperform significantly.
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