Vector Database Sizing Calculator
Enter your document count and embedding dimensions to get RAM, storage, and monthly cost estimates across pgvector, Pinecone, Weaviate, Qdrant and Chroma.
Try the Calculator↓Free · No login required · Results in seconds
Your Vector Store
Each chunk typically 200–500 words
Storage Needed
4.0 GB
with HNSW index
Recommended RAM
6.1 GB
for in-memory index
Estimated CPU
2 cores
for 100,000 queries/mo
Recommended: Qdrant Cloud
Mid-scale workload — Qdrant offers the best price/performance balance at this vector count.
Estimated: $9.06/month
All Database Options
Weaviate Cloud
managed$0.8571/mo
Managed Weaviate with hybrid search (vector + BM25).
Best for: Hybrid search, graph traversal, rich filtering needs
1GB storage + 1M queries/month (sandbox)
Pinecone (Serverless)
managed$3.14/mo
Fully managed, serverless vector DB. Pay per use.
Best for: Production scale, variable traffic, no ops overhead
2GB storage + 100K queries/month
Qdrant Cloud
managed$9.06/mo
High-performance vector DB with rich filtering. Very affordable.
Best for: High-volume queries, rich payload filtering, cost-sensitive teams
1GB free cluster
Chroma (self-hosted)
self-hosted$20.20/mo
Python-native open-source vector DB. Great for prototyping.
Best for: Prototyping, Python stacks, RAG proof-of-concepts
Free to self-host
pgvector (PostgreSQL)
self-hosted$50.47/mo
Open-source extension for PostgreSQL. Best for <5M vectors.
Best for: Under 5M vectors, existing Postgres users, budget-conscious
Free on own server
* Costs are estimates based on published pricing as of March 2026. Initial write costs are not included. Verify with provider before production use.
Need help sizing your RAG infrastructure?
Digiqt designs and deploys production RAG systems with the right vector database for your scale and budget.
Vector Database Sizing — FAQ
Not always. For under 100,000 chunks, pgvector on a standard PostgreSQL instance handles similarity search efficiently. Dedicated vector databases like Pinecone and Qdrant pull ahead at millions of vectors or high query rates.
Hierarchical Navigable Small World (HNSW) is the algorithm most vector databases use for approximate nearest neighbor search. It trades a small amount of RAM overhead (20–40% extra storage for the graph index) for dramatically faster query times versus brute-force search.
More dimensions generally means better semantic accuracy but higher storage and compute cost. OpenAI's text-embedding-3-small (1536 dims) balances accuracy and cost well for most use cases. For budget-sensitive applications, 768-dim embeddings (Google, BERT) offer good quality at half the storage.
Yes. pgvector is used in production by many companies for datasets up to 10–50 million vectors. It runs inside standard PostgreSQL, benefits from all Postgres tooling, and requires no additional infrastructure. Supabase and Neon both offer pgvector as a managed service.
Consider migrating when: query latency exceeds 200ms, you have over 10 million vectors, you need multi-tenancy at scale, or you require features like hybrid search or payload filtering that pgvector doesn't support natively.
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