OpenRAG vs Pathway Live Data Framework
OpenRAG
open-rag.aiPathway Live Data Framework
pathway.comSide by side
| Figure | OpenRAG | Pathway Live Data Framework |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 3 |
| Spec score · LLM Application Development Frameworks | 8.3 (#5) | 7.5 (#13) |
| Spec score · Retrieval-Augmented Generation Tools | 8.4 (#8) | 7.6 (#18) |
| Integrations | The product documents compatibility with OpenAI API clients and names Open WebUI, LangChain, n8n and Twake.ai as integrations. | The documentation says Pathway includes 350+ connectors, including SharePoint, and lets users implement their own connectors. |
| Purpose | OpenRAG is a modular framework for building document-grounded retrieval-augmented generation systems. | Pathway is a Python data processing framework for analytics and AI pipelines over data streams, including streaming ETL and RAG pipelines. |
Plans
OpenRAG
- OpenRAG (AGPL-3.0 self-hosted)Free
- Managed OpenRAG servicePriced on request
Pathway Live Data Framework
- CommunityFree
- ScaleFree
- EnterprisePriced on request
In their own words
OpenRAG
OpenRAG is a modular framework for building systems that answer questions using documents as their grounding. Its retrieval combines semantic search with BM25 keyword matching and multilingual reranking; answers cite a source document and page, with links to the cited location. Document processing covers listed text, office, email, audio, video, and image formats, with PDF layout awareness, OCR, image captioning, and audio transcription. The product includes an admin console, chat interface, and OpenAI-compatible API, and can connect to user-provided models or a hosted provider. Partition isolation, owner, editor, and viewer roles, plus token or OpenID Connect authentication, are among its access controls. It can run on infrastructure controlled by the deployer, keeping documents, embeddings, and queries within that environment. The self-hosted AGPL-3.0 edition is listed at free. Deployment prerequisites are Docker, Docker Compose, and at least 16 GB of RAM; a CPU-only profile is optional. CSV, ODT, and HTML support, tool calling, agentic RAG, MCP, and encryption in transit and at rest are listed as coming soon.
Pathway Live Data Framework
Pathway Live Data Framework is a Python framework for building analytics and AI pipelines across stored data and live streams. One pipeline can handle batch processing or streaming ETL, while Differential Dataflow updates results when events arrive out of order. Its Rust processing engine runs Python with multithreading and multiprocessing. AI tooling covers online machine learning, retrieval-augmented generation pipelines, and vector indexes for current data. Documentation lists more than 350 connectors, including SharePoint, and allows custom connectors. Python and SQL programming APIs are available, along with a REST endpoint for serving queries, answers, and real-time features. Deployments can use Docker or Kubernetes and work with OpenTelemetry. Pathway can preserve computation state for recovery. Community is free, self-hosted, and limited to 8 GB RAM and four cores; Scale is free or paid with a license key.