Vector Database Selection Guide

Why Vector Databases Matter

RAG systems depend on fast, accurate vector retrieval. The choice of vector database shapes latency, cost, scalability, and operational complexity. The wrong choice produces slow responses, high bills, or unreliable results.

For RAG systems, recall above 0.90 is typically sufficient; the LLM can compensate for missing context, but not for completely wrong context.

We evaluate vector databases on four dimensions: performance (latency, throughput, recall), cost (infrastructure, licensing, operational), operational complexity (self-hosted vs managed, scaling, backup), and ecosystem (integrations, community, support). No database wins on all dimensions; the choice is always a trade-off.

The Options Compared

Chroma

Chroma is an open-source embedding database designed for simplicity. It runs locally, stores embeddings in SQLite or DuckDB, and provides a Python-first API that data scientists find intuitive.

Chroma Limitations: Single-node only, no horizontal scaling, limited production monitoring. Not suitable for high-throughput applications. Best for prototypes and small datasets (under 1M vectors).

Pinecone

Pinecone is a managed vector database with serverless deployment, automatic scaling, and strong metadata filtering. It abstracts away infrastructure entirely — you send embeddings, Pinecone stores and retrieves them.

Pinecone Limitations: Vendor lock-in, cost scales with usage unpredictably, limited customisation of indexing algorithms. Best for teams that want zero operational overhead and have variable traffic patterns.

Weaviate

Weaviate is an open-source vector database with GraphQL interface, hybrid search (vector + keyword), and built-in vectorisation modules. It supports both self-hosted and managed (Weaviate Cloud) deployment.

Weaviate Strength: Hybrid search — combine vector similarity with keyword matching for better results. Best for teams needing hybrid search, complex metadata queries, or graph relationships alongside vectors.

pgvector

pgvector is a PostgreSQL extension that adds vector storage and similarity search to the world's most trusted relational database. It supports exact and approximate nearest neighbour search using IVFFlat and HNSW indexes.

pgvector Strength: If you already use PostgreSQL, pgvector adds vector capability without new infrastructure. Supports transactional consistency between vector and relational data. Best for regulated environments.

pgvector Limitation: Performance falls behind specialised databases at very large scale (over 100M vectors). Index build times can be long.

Performance Benchmarks

Based on our internal testing with 10M vectors (768-dimensional, cosine similarity):

Query latency (p95): Pinecone ~15ms, Weaviate ~25ms, pgvector (HNSW) ~40ms, Chroma ~120ms
Recall @ top-10: Pinecone 0.97, Weaviate 0.95, pgvector 0.94, Chroma 1.0 (exact search)
Index build time (10M vectors): Pinecone < 1 hour (managed), Weaviate ~2 hours, pgvector ~4 hours, Chroma ~8 hours
Throughput (queries/sec/node): Weaviate ~8,000, pgvector ~3,000, Chroma ~500, Pinecone (managed) ~unlimited

Decision Framework

Choose Chroma if: You are prototyping, have under 1M vectors, and need something running in under an hour.

Choose Pinecone if: You want zero operational burden, have variable or unpredictable scale, and cost is secondary to velocity.

Choose Weaviate if: You need hybrid search, want open-source with managed option, or have complex metadata filtering requirements.

Choose pgvector if: You already use PostgreSQL, need transactional consistency between vectors and relational data, or operate in a regulated environment.

Our Recommendation

Start with pgvector if you use PostgreSQL already. It adds vector capability without new infrastructure and handles most production workloads. Move to Weaviate when you need hybrid search or graph queries. Use Pinecone only when operational simplicity outweighs cost concerns.