Which Vector Database Is Cheapest for Startups

Free Tier Comparison

For prototyping and early development, any of these options works. pgvector is free if you have PostgreSQL. ChromaDB is free to self-host and requires minimal setup. Pinecone's free tier is the easiest to start with because there is nothing to install or configure. Qdrant Cloud's free tier provides a production-ready managed cluster without payment details.

Scaling to 100K to 1M Vectors

This is the range where most startups operate after initial traction: enough data to need real infrastructure but not enough to need enterprise-scale solutions.

pgvector on existing PostgreSQL: $0 incremental cost. If you already pay for a PostgreSQL instance (AWS RDS, Google Cloud SQL, a VPS), adding pgvector does not change your hosting bill. You may need to increase the instance size if the HNSW index pushes memory usage above your current allocation, but for 500K vectors at 1,536 dimensions (roughly 9 GB index), a $50 to $100 per month instance handles the workload.

Self-hosted Qdrant: $20 to $50 per month. A 4 GB RAM VPS from Hetzner, DigitalOcean, or Linode runs Qdrant comfortably for 200K to 500K vectors. An 8 GB instance handles up to 1 million vectors. Qdrant runs as a single Docker container, so deployment is a single command. The trade-off is operational responsibility: you manage backups, updates, and monitoring.

Pinecone Starter: $0 to $70 per month. Pinecone's free tier covers 100K vectors. Beyond that, the Starter plan starts at roughly $70 per month for standard pod storage. Pinecone handles all operations, scaling, and maintenance. The cost per vector is higher than self-hosted options, but the time saved on operations can be worth it for small teams focused on product development.

Qdrant Cloud: $25 to $65 per month. Managed Qdrant pricing is based on cluster size rather than vector count. The smallest paid cluster handles 500K to 1M vectors at roughly $25 per month, which is significantly cheaper than Pinecone for the same capacity.

Scaling Beyond 1M Vectors

At scale, the cost differences between providers widen significantly. Pinecone's per-vector pricing model means costs grow linearly with data volume: 5M vectors costs roughly 5 times what 1M vectors costs. Self-hosted and cluster-based pricing (Qdrant, Weaviate) grows step-wise as you upgrade to larger machines or add nodes, which is more cost-efficient at scale.

For startups growing past 1M vectors, the most cost-effective path is usually Qdrant (self-hosted or managed cloud) or pgvector on a larger PostgreSQL instance. Pinecone remains the simplest option but becomes the most expensive. Weaviate offers a middle ground with rich features but moderate operational complexity.

A cost optimization that applies to any database: use scalar quantization to reduce storage by 4x. This lets a $20 VPS that normally handles 500K vectors handle 2M vectors instead, pushing the need for a larger instance or managed service further into the future. Most startups that enable quantization early find they never need to upgrade their vector infrastructure before achieving product-market fit.

The Total Cost Picture

Database hosting is only part of the total cost of running vector search. The full picture includes embedding API costs, re-embedding costs when switching models, engineering time, and operational overhead.

Embedding API costs are surprisingly low. OpenAI's text-embedding-3-small costs $0.02 per million tokens. Embedding 100K documents averaging 500 tokens each costs $1 for the initial corpus. Monthly costs for new documents and queries are typically under $5. Embedding costs rarely exceed $50 per month even for fast-growing startups.

Re-embedding costs hit when you switch models. If you start with OpenAI's small model and later want to try Cohere or Voyage for better domain-specific performance, you need to re-embed your entire corpus because different models produce incompatible vector spaces. For 500K documents, re-embedding costs $5 to $65 depending on the model. Not expensive, but the engineering time to run the migration, verify quality, and update the pipeline is the real cost.

Engineering time varies dramatically by approach. Self-hosted Qdrant requires initial setup (1 to 2 hours), ongoing monitoring (1 to 2 hours per month), and occasional troubleshooting. pgvector on existing PostgreSQL requires initial setup (30 minutes) and near-zero ongoing maintenance. Managed services require initial setup (15 minutes) and zero maintenance. For a two-person startup where every engineering hour counts, a $70 per month managed database that saves 5 to 10 hours per month of operations time is the cheapest option in practice.

The minimum viable vector search budget for a startup is effectively $0: pgvector on existing PostgreSQL for storage, OpenAI's text-embedding-3-small for embeddings ($1 to $5 per month), and your existing server for hosting. You can build and ship a working semantic search feature to users without spending a dollar more than you already spend on hosting.