Pathway is enabling Real-Time Intelligence.

Real-time framework for data processing, indexing, search, analytics, and retrieval. It empowers Enterprise developers to build easy-to-deploy, secure, optimized and cost-efficient data pipelines.

Pathway is a data processing framework that handles streaming data in a way easily accessible to Python and AI developers.

Streamlined Cloud-Native Development Built in Rust with ❤ for Python developers, Pathway streamlines the entire ML/AI project lifecycle from prototype to production. It supports local, notebook, and scaled container deployments.

Fast, and Always In-Sync. Connect live data sources like SharePoint, Google Drive, S3 and Delta Tables, cloud folders, Kafka, databases, and 300+ APIs such as Salesforce and Hubspot. Pathway's high-speed Rust engine delivers real-time updates, and is scalable to hundreds of CPU cores, with cost-efficient incremental computing, persistence, and caching.

A single robust container. You own it. Deploy each of your Pathway applications from a git folder using Docker or Kubernetes, on-premises or in any cloud. Simplify your setup by avoiding multiple databases and compute engines linked by clunky APIs. Gain full control of the pathway that your data follows. It is made available for download as a Python-native package from GitHub

Chef's Choice, or à la carte? Have it your own way. Choose from ready-to-use Pathway app templates suitable for various industries and data types. Customize them using functions from the Pathway library, connectors, and integrations. Safely interact with LLMs and external asynchronous APIs.

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Pathway's LLM (Large Language Model) Apps allow you to quickly put in production AI applications which offer high-accuracy RAG at scale using the most up-to-date knowledge available in your data sources.

The apps connect and sync (all new data additions, deletions, updates) with data sources on your file system, Google Drive, Sharepoint, S3, Kafka, PostgreSQL, real-time data APIs. They come with no infrastructure dependencies that would need a separate setup. They include built-in data indexing enabling vector search, hybrid search, and full-text search - all done in-memory, with cache.

See our RAG Solutions Pricing

Enterprises work on constantly changing datasets and the problem with most Gen AI Engineering solutions is that they are designed to work on static data. Pathway believes that the Enterprise GenAI stack of the future will be built on live streaming data as opposed to batch processing. We are enabling this transition today. Pathway automatically connects and syncs with data sources from Enterprise file systems, Kafka, real-time data APIs, to Sharepoint, S3, PostgreSQL, Google Drive, etc. - with no infrastructure dependencies.

On top of the live aspect, Pathway promotes building systems in which the retriever and LLM are aware of the joint use case and jointly tuned. Current RAG+LLM solutions are built by composing several pieces built separately: embedders, retrievers (search systems, vector dbs), LLMs,... therefore leading to: limited understanding with personalized facts, hallucinations, and miscalibrated confidence. Pathway resolves these problems for use cases focused around information retrieval (search) yielding greater accuracy and differentiating it from piece-meal solutions.

The application templates provided in our public repo scale up to millions of pages of documents with consistent performance. While some solutions are optimized for simplicity, and others are optimized for accuracy - we do both. Many of our customers use Pathway out of the box, others change some steps of the pipeline: adding new data sources or changing a Vector Index into a Hybrid Index, is a one code-line change in Pathway.

The apps can be run as Docker containers, from private local installations to large-scale cloud deployments.

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