Pathway vs PostgresML

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

	Pathway RAG	PostgresML RAG
Tagline	Live data framework for production RAG and streaming ETL pipelines in Python.	PostgreSQL extension that runs embeddings, vector search, and LLM inference inside your database.
Category	RAG	RAG
Pricing	Freemium· Community free (BSL 1.1, 8GB/4 cores); Scale and Enterprise tiers with license key	Freemium· Serverless: From $7.50 per query hour · Dedicated: From $0.60 per instance hour · Enterprise: Custom pricing
Model	Multi-model	Multi-model (Llama, Mistral, open-source embeddings)
Editorial score	7.3 / 10	7.1 / 10
Use cases	live-ragstreaming-etldocument-indexingmultimodal-raganomaly-detection	vector-searchragembeddingsllm-inferencefine-tuningin-database-ml
Pros	Genuinely live indexing - documents update without rebuild jobs Self-hosted under BSL 1.1, no data leaves your infra Rich connector library (Kafka, S3, SharePoint, Postgres, Delta Lake) Same pipeline handles batch and streaming 20+ production-ready templates including multimodal and adaptive RAG	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
Cons	Steeper learning curve than prompt-chain frameworks BSL is not OSI-approved - commercial restrictions apply at scale Smaller community than LangChain/LlamaIndex Pricing for Scale/Enterprise tiers not transparent	Couples GPU/ML workload to your primary database Requires Postgres operational expertise to self-host well Smaller model catalog than dedicated inference providers
Website	pathway.com	postgresml.org

Pick Pathway if

✅ Genuinely live indexing - documents update without rebuild jobs
✅ Self-hosted under BSL 1.1, no data leaves your infra
✅ Rich connector library (Kafka, S3, SharePoint, Postgres, Delta Lake)
✅ Same pipeline handles batch and streaming

Pick PostgresML if

✅ 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

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