Weaviatevspgvector
- ✦ Open Source (Apache 2.0)
- ✦ Hybrid Search (BM25 + Vector)
- ✦ Multimodal Support
- ✦ PostgreSQL Extension
- ✦ Exact Nearest Neighbor
- ✦ Approximate Nearest Neighbor (IVFFlat, HNSW)
Real-World Scenarios: When to Choose Which
The question that matters: “In what situation will I regret choosing A over B after 3 months?”
Weaviate's multi2vec module indexes text and image objects in the same collection, enabling cross-modal search where a text query returns images and vice versa without separate pipelines.
pgvector's HNSW index achieves sub-50ms similarity search for collections under 10M vectors, covering most product recommendation and semantic search use cases without a specialized vector database.
Weaviate's Generative Search module passes retrieved objects directly to an LLM within the same query, cutting latency by eliminating a separate LLM API call for RAG retrieval-generation pipelines.
pgvector stores embeddings as a native column type and queries them with standard SQL, avoiding the operational complexity of a separate vector database for applications already running on Postgres.
Weaviate's structured schema enforces data types on vector objects, enabling filtered vector search that combines nearest neighbor with exact property matches and reducing false positives in metadata-sensitive retrieval.
pgvector writes and deletes embeddings within standard Postgres transactions, ensuring vector index and application data never diverge in multi-step operations that require rollback.
Pricing Intelligence
Weaviate Plans
Free tier available
- • Full features
- • Community support
- • Apache 2.0
- • From $0.045/1M vectors
- • Managed cloud
- • No ops overhead
- • Custom deployment
- • SLA
- • Dedicated support
pgvector Plans
Free tier available
- • Free PostgreSQL extension
- • MIT license
- • Self-hosted
Feature Matrix
6 differences found across 14 standardized features
Pros & Cons Face-Off
Evaluative strengths and weaknesses — not feature lists
- +Open source with managed cloud option gives deployment flexibility
- +Built-in vectorizers reduce pipeline complexity
- +Knowledge graph cross-references unique in category
- +Active community and excellent documentation
- −GraphQL API has steeper learning curve
- −Performance benchmarks trail Qdrant at very high scale
- +No new infrastructure — runs inside existing PostgreSQL
- +SQL interface familiar to every developer
- +ACID transactions across vectors and relational data
- +Works on Supabase, RDS, Neon — all managed PG providers
- −Performance trails purpose-built vector DBs at 10M+ vectors
- −No distributed vector search without manual sharding