HelixDB vs Pathway

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

	HelixDB RAG	Pathway RAG
Tagline	Unified graph-and-vector database built for AI agent memory and GraphRAG.	Live data framework for production RAG and streaming ETL pipelines in Python.
Category	RAG	RAG
Pricing	Freemium· GW-10: $86.87 · GW-20: $173.74 · GW-40: $348.21 · GW-80: $696.42 · GW-160: $1,392.84	Freemium· Community free (BSL 1.1, 8GB/4 cores); Scale and Enterprise tiers with license key
Model	—	Multi-model
Editorial score	7.0 / 10	7.3 / 10
Use cases	agent-memorygraphragvector-searchknowledge-graphenterprise-knowledge	live-ragstreaming-etldocument-indexingmultimodal-raganomaly-detection
Pros	Unifies graph, vector, and full-text search in one query layer Object-storage backend keeps costs and ops overhead lower than hot-memory stores Open source with SDKs in Rust, Go, TypeScript, and Python Temporal awareness for facts that change over time, useful for agent memory	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	Younger project than Pinecone/Weaviate/Neo4j; smaller ecosystem and tooling Pricing for managed tier not transparent on the marketing site Object-storage tradeoffs may add latency vs in-memory vector DBs for hot paths	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	helix-db.com	pathway.com

Pick HelixDB if

✅ Unifies graph, vector, and full-text search in one query layer
✅ Object-storage backend keeps costs and ops overhead lower than hot-memory stores
✅ Open source with SDKs in Rust, Go, TypeScript, and Python
✅ Temporal awareness for facts that change over time, useful for agent memory

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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