pgvector vs Weaviate
Side-by-side — pricing, strengths, tradeoffs, and fit.
pgvector
Open-source vector similarity search for PostgreSQL
Weaviate
The AI-native vector database with built-in vectorization
At a glance
Open SourceFree Tier
● Established·From Free / Open Source
Open SourceFree Tier
● Established·From Freemium
Overview
One-liner
One-liner
Open-source vector similarity search for PostgreSQL
One-liner
The AI-native vector database with built-in vectorization
Maturity
Maturity
EstablishedMaturity
EstablishedOpen source
Open source
✓ YesOpen source
✓ YesPricing
Free tier
Free tier
✓ AvailableFree tier
✓ AvailableStarts at
Starts at
Free / Open Source
Starts at
Freemium
Model
Model
Flat rate
Model
Hybrid
Enterprise
Enterprise
✕ NoneEnterprise
✕ NoneBest for
✓ Best for
Teams already using PostgreSQL who want to add vector search without adopting a separate vector database
✓ Best for
Developers building semantic search or RAG pipelines who want automatic vectorization without managing embedding models separately
Enterprise teams who need a compliant vector database with self-hosted or managed options
Startups prototyping AI features who want built-in integrations with OpenAI and Cohere
Not ideal for
✕ Not for
Applications requiring billion-scale vector search with low latency — use a dedicated vector DB instead
✕ Not for
Teams who already manage their own embedding pipeline and need only raw ANN search
Developers needing maximum raw query performance who prefer a leaner Rust-based engine
Strengths
↑ Strengths
L2 and cosine similarity
HNSW and IVFFlat indexing
Postgres-native
Supabase/Neon support
↑ Strengths
Built-in vectorization modules
GraphQL and REST API
Multi-tenancy
Hybrid BM25 and vector search
Weaknesses
↓ Weaknesses
—
↓ Weaknesses
No native support for Windows operating systems or binaries.
Inability to reindex data smoothly, particularly for updating embeddings or inverted indexes.
No support for cross-tenant queries, requiring multiple separate queries to search across tenants.