Weaviate vs pgvector
A head-to-head comparison of two leading vector databases for AI-powered growth. See how they stack up on pricing, performance, and capabilities.
Weaviate
Pricing: Free sandbox, then $25/mo Serverless; open-source self-hosted
Best for: Hybrid search use cases and teams wanting built-in vectorization
pgvector
Pricing: Free (open-source PostgreSQL extension)
Best for: Teams already on PostgreSQL with under 5M vectors
Head-to-Head Comparison
| Criteria | Weaviate | pgvector |
|---|---|---|
| Setup Complexity | Moderate — schema + module config | Minimal — Postgres extension, SQL schema |
| Cost at 1M Vectors | ~$25/mo serverless; free self-hosted | Incremental Postgres cost |
| Query Latency | ~5-25ms p99 | ~10-50ms p99 (HNSW competitive up to ~1M vectors) |
| Hybrid Search | BM25 + vector, multiple search modes | tsvector + vector via SQL — flexible but requires manual setup |
| Scaling Ceiling | Billions of vectors, multi-tenancy native | Best under 5M vectors |
The Verdict
Weaviate delivers a purpose-built search platform with out-of-the-box hybrid search, auto-vectorization modules, and multi-tenancy features that would take significant effort to replicate with pgvector. However, pgvector's tight integration with Postgres means teams already managing Postgres can store, query, and join vectors alongside relational data with a single connection and no additional service. For projects where search is a core product feature, Weaviate's richer tooling justifies the added complexity; for simpler semantic search needs alongside relational data, pgvector is the pragmatic choice.
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