Feast vs Pathway

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

	Feast RAG	Pathway RAG
Tagline	Open-source feature store that serves consistent features to ML training and online inference, with RAG vector search built in.	Live data framework for production RAG and streaming ETL pipelines in Python.
Category	RAG	RAG
Pricing	Free· Free, open source (Apache 2.0); self-hosted	Freemium· Community free (BSL 1.1, 8GB/4 cores); Scale and Enterprise tiers with license key
Model	—	Multi-model
Editorial score	8.2 / 10	7.3 / 10
Use cases	feature-storerag-retrievalonline-inferencetraining-datavector-searchmlops	live-ragstreaming-etldocument-indexingmultimodal-raganomaly-detection
Pros	Solves train/serve skew with point-in-time-correct historical retrieval Broad adapter ecosystem across warehouses, KV stores, and vector DBs Production-proven at Robinhood, NVIDIA, Shopify, Walmart Vector similarity search makes it usable as a RAG feature layer Permissive Apache 2.0 license with active community	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
Cons	You operate the underlying stores yourself; Feast is orchestration, not storage Steeper learning curve than a hosted vector DB for simple RAG demos No first-party managed cloud; SaaS is via third parties like Tecton	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
Website	feast.dev	pathway.com

Pick Feast if

✅ Solves train/serve skew with point-in-time-correct historical retrieval
✅ Broad adapter ecosystem across warehouses, KV stores, and vector DBs
✅ Production-proven at Robinhood, NVIDIA, Shopify, Walmart
✅ Vector similarity search makes it usable as a RAG feature layer

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

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