Vector Database Comparison: Pinecone vs Weaviate vs Qdrant vs pgvector
Managed is faster to ship. Self-hosted is cheaper at scale and gives you compliance control.
The vector database landscape has four realistic choices for most teams. Here is the one-line version and then the detail.
| Managed (Pinecone, Weaviate Cloud) | Self-Hosted (Qdrant, Weaviate, pgvector) | |
|---|---|---|
| Pinecone | Fastest to get running. Excellent performance. No self-host option. Pricing can be high. | — |
| Weaviate | Best hybrid search out of the box. Both managed cloud and self-hosted. | Self-hosted: Docker/K8s. Active community. Multi-tenant friendly. |
| Qdrant | Rust-based, very fast. Good filtering. Cloud or self-hosted. | Lightweight Docker deployment. Great for on-prem setups. |
| pgvector (Postgres) | Already in your Postgres. Zero new service. Good for under 5M vectors. | No extra infra if you are already on Postgres. Limited to ~10M vectors at scale. |
| Hybrid search | Pinecone: yes. Weaviate: best-in-class. Qdrant: yes. | pgvector: limited (requires FTS extension). |
| Cost model | Managed: monthly subscription or compute-units. | Self-hosted: infrastructure cost only. |
Pick Managed (Pinecone, Weaviate Cloud) when
Use a managed service when: you want to move fast, do not have a DevOps team, or are under 10M vectors.
Pick Self-Hosted (Qdrant, Weaviate, pgvector) when
Use self-hosted when: compliance requires on-prem, you are at scale where managed costs are painful, or you need full control.
Bottom line
Start with pgvector if you are already on Postgres (free, zero new service). Migrate to Pinecone or Qdrant when you hit scale or need advanced filtering. Choose Weaviate when hybrid search quality is non-negotiable.
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Talk to usGlossary
- Vector DatabaseA database optimized for storing embeddings and finding the nearest matches fast.
- EmbeddingsNumerical representations of text so a computer can measure meaning by distance.
- Hybrid SearchCombining vector (semantic) search with keyword (BM25) search for better retrieval.
- Metadata FilteringNarrowing retrieval to specific document subsets using attributes like date, department, or type.
- Semantic SearchFinding documents by meaning, not just matching keywords.