PostgresML vs Weaviate

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	PostgresML RAG	Weaviate RAG
Tagline	PostgreSQL extension that runs embeddings, vector search, and LLM inference inside your database.	Open-source vector DB with hybrid search and modules.
Category	RAG	RAG
Pricing	Freemium· Open-source self-host free; managed cloud usage-based with $100 free credits	Freemium· Free open-source; cloud from $25/mo
Model	Multi-model (Llama, Mistral, open-source embeddings)	Hosted vector DB (not an LLM)
Editorial score	—	8.4 / 10
Use cases	vector-searchragembeddingsllm-inferencefine-tuningin-database-ml	self-hosted RAGhybrid search
Pros	Embeddings, vector search, and LLM inference in one Postgres extension Eliminates network hops between app, vector DB, and inference service Open source (PGML, Korvus, PgCat) with SQL/Python/JS SDKs Self-host or managed cloud with VPC option Strong benchmarks vs Pinecone on cost and latency	Hybrid search built in Self-host or cloud Module ecosystem GraphQL + REST APIs
Cons	Couples GPU/ML workload to your primary database Requires Postgres operational expertise to self-host well Smaller model catalog than dedicated inference providers	More ops than Pinecone if self-hosted Smaller community
Website	postgresml.org	weaviate.io

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