Pathway vs Supabase

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

	Pathway RAG	Supabase RAG
Tagline	Live data framework for production RAG and streaming ETL pipelines in Python.	Open-source Firebase alternative built on Postgres with a first-class pgvector AI toolkit.
Category	RAG	RAG
Pricing	Freemium· Community free (BSL 1.1, 8GB/4 cores); Scale and Enterprise tiers with license key	Freemium· Free / Pro $25 per month / Team $599 per month / Enterprise custom. Compute add-ons from $10/mo (Micro) to $3,730/mo (16XL). Database overage $0.125/GB. PITR $100/mo per 7-day retention.
Model	Multi-model	—
Editorial score	7.3 / 10	—
Use cases	live-ragstreaming-etldocument-indexingmultimodal-raganomaly-detection	RAG chatbot backendsemantic document searchhybrid keyword and vector searchagent long-term memoryembedding storage for product catalogsAI-powered mobile app backenduser authentication for LLM appsfile storage for RAG source documentsrealtime AI chat interfacesself-hosted vector database
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	pgvector is deeply integrated so embeddings live in the same Postgres schema as your business data, enabling SQL joins between rows and vectors. Fully open source and self-hostable via Docker, so you can move off the hosted platform without rewriting your app. Auto-generated REST and GraphQL APIs plus row-level security remove huge amounts of backend boilerplate for AI app prototypes. Realtime subscriptions and Edge Functions let you stream RAG results and run inference glue code close to the database. Generous free tier with unlimited API requests and 2 projects makes it easy to prototype AI features without a credit card. First-party guides and starters for LangChain, LlamaIndex, OpenAI, and Hugging Face reduce the time to a working RAG stack.
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	It is a Postgres backend platform, not an AI product per se, so you still need to bring your own embedding model, LLM, and orchestration layer. pgvector at very high dimensionality or billion-scale corpora can lag purpose-built vector databases like Pinecone or Milvus on latency and index build time. Free-tier projects pause after a week of inactivity, which surprises hobbyists running demo RAG bots. The jump from $25 Pro to $599 Team is steep for small teams that need SOC2 or SLAs. Self-hosting the full stack (Studio, GoTrue, Storage, Realtime, Kong, PostgREST) is doable but operationally heavier than a managed vector service.
Website	pathway.com	supabase.com

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 Supabase if

✅ pgvector is deeply integrated so embeddings live in the same Postgres schema as your business data, enabling SQL joins between rows and vectors.
✅ Fully open source and self-hostable via Docker, so you can move off the hosted platform without rewriting your app.
✅ Auto-generated REST and GraphQL APIs plus row-level security remove huge amounts of backend boilerplate for AI app prototypes.
✅ Realtime subscriptions and Edge Functions let you stream RAG results and run inference glue code close to the database.

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