Vector Database Selection Guide (2026)
pgvector, Pinecone, Weaviate, Milvus, Qdrant, LanceDB — a decision matrix for enterprise RAG teams in 2026.
Start with pgvector, always
For 90% of enterprise teams with <10M vectors, pgvector on your existing Postgres is the right answer. You get transactional consistency, ACL joins for permission-aware retrieval, backup and DR you already run. Don't add a new datastore until pgvector demonstrably breaks.
When to graduate
At 50M+ vectors, sub-100ms p95 latency on rich filters, or write throughput above ~1k QPS, dedicated vector databases start to justify themselves. Pinecone for pure managed simplicity, Milvus/Qdrant for open-source at scale, Weaviate when you want built-in hybrid search and modules.
Hybrid search is table stakes
Pure dense retrieval loses on exact-match queries — proper nouns, SKU codes, dates. Every serious RAG system in 2026 combines BM25 + embeddings + a reranker. Pick a vector DB that supports it natively or pair with OpenSearch/Elastic.
The under-discussed criteria
Metadata filtering performance (matters more than raw recall), ACL-aware retrieval (critical for enterprise), backup/restore (surprisingly bad on some managed services), and observability. Benchmark on your workload, not on marketing pages.
What we deploy by default
pgvector for teams under 20M vectors and single-tenant deployments. Qdrant self-hosted for multi-tenant scale where cost matters. Pinecone when the team explicitly doesn't want to run infra. Every stack has a reranker in front and BM25 alongside — never dense-only.
Key takeaways
- pgvector wins for most enterprise RAG under 10M vectors
- Graduate to dedicated vector DBs at 50M+ vectors or heavy filter workloads
- Hybrid retrieval (BM25 + dense + rerank) beats dense-only on every real benchmark
- Metadata filter perf and ACL-aware retrieval matter more than raw recall
- Benchmark on your workload before committing
T7 Research
Enterprise AI Research Group
T7 Research is the research arm of T7 Solution, focused on benchmarking LLMs, evaluating RAG patterns, and compiling implementation playbooks for enterprise technology leaders.