Billion-vector workloads (Milvus, Vespa, or managed Pinecone better), multi-tenant SaaS with many thousands of tenants per cluster, or teams requiring sub-10ms p99 at high QPS.
Teams already on Postgres (any size) with RAG or semantic-search workloads in the under-5-to-10M-vector range who want to avoid adding a new vendor and a new datastore.
Why we say this
Editorial pulled these weaknesses from pgvector + Postgres’s product card in our Top 10 Vector Database Software for 2026:
- ! At very large scale (50M+ vectors) dedicated DBs win on latency and operations
- ! Index build time on large datasets can be long; tuning is a real skill
- ! Multi-tenancy at thousands of tenants strains a single Postgres without careful schema design
If pgvector + Postgres is wrong for you, consider these instead
Same Vector Database Software category, different best-fit buyer.
Best for
Engineering-led teams (any size) targeting billion-scale or near-billion-scale vector workloads, or organizations that need GPU-accelerated ANN and Apache 2.0 licensing.
See full profile →Best for
Mid-market product engineering teams (20-1,000 employees) wanting an OSS-licensed vector DB with built-in vectorizer modules, hybrid search, and the option to self-host or buy the managed cloud.
See full profile →Best for
EU-headquartered product teams (any size) wanting OSS-licensed vector DB with strong performance per dollar, or global teams who prioritize recall-vs-cost benchmarks and Apache 2.0 licensing.
See full profile →Related editorial
Last updated 2026-05-23. Editorial verdict based on the published Top 10 Vector Database Software for 2026 ranking. Disagree? Tell us.