Announcing: Pathway is the fastest data processing engine on the market - 2023 benchmarks

Make your
AI applicationdata pipelinelog monitoringAI application
run in real-time
Check it out
index = KNNIndex(enriched_documents, d=embedding_dimension)
query_context = index.query(query, k=3).select(
    pw.this.query, documents_list=pw.this.result
prompt =
    prompt=build_prompt(pw.this.documents_list, pw.this.query)
model = OpenAIChatGPTModel(api_key=api_key)
responses =,

The fastest data processing engine supporting unified workflows for batch, streaming data, and LLM applications

What do people typically use Pathway for?

  • I want to fix my latency
    I need latency to be milliseconds or seconds, but my batch job is taking minutes or hours to run each time.
  • I want to reduce my compute bill quickly.
    My cloud bill is too high because of Spark and other jobs taking lots of compute resources.
  • I want real-time to work from day one.
    I am launching a new data project and I want it to integrate with real-time data sources easily.

How others use it

Build your LLM App in 30 lines of code, no vector database requiredBuild your LLM App in 30 lines of code, no vector database required
Get actionable insights on top of data for logistics assets

Trusted by

gartner logoFeatured in Gartner's Market Guide

Market Guide for Event Stream Processing

gartner logoGartner's Representative Vendor

Market Guide for Data Analytics and Intelligence Platforms in Supply Chain

Why adopt Pathway?

It has soft fur.

Pathway is used by data scientists and engineers alike. You can program in Python or SQL.

Launch your data project quickly with static data sources. Then, connect live data (Kafka, S3/, PostgreSQL,...) and run your code in Pathway's streaming mode.

You can call HTTP API's and integrate with your existing ML models or build your entire LLM stack using Pathway.

It has sharp teeth.

Pathway is powered by a distributed pure-Rust streaming engine. It achieves high throughput with astonishingly low latency.

Pathway works with event stream data and time series, data tables, graph data, and external blobs (video, text).

Pathway supports backfilling with a mix of streaming and static data sources. It allows for late data points and corrections to data.

It is deployed across enterprise.

Pathway is currently used for real-time anomaly detection, predictive analytics, IoT and logs data observability, recommender systems, and alerting.

You can also use Pathway simply to reduce cloud costs, when frequent batch recomputation is eating away at your data budgets.

The data processing layer of Pathway is free and source available.

Use Pathway to create value with your data platform

Live data
Data tables
Live events data
Live transaction records
Live sales data
IoT data
Logistics & moving asset data
Supply chain plans
User inputs
and more!
Value Delivered in Real Time
AI-powered insights from LLM
Anomalies detected
Actionable insights
Data harmonization and enrichment
Interactive scenario simulations
and more!

Your data product built with Pathway

Pathway architecture diagram

It's time to add Pathway to your workflow

Build your LLM App in 30 lines of code

using Pathway, no vector database required

Transform your data in motion

using Pathway on top of a Kafka real-time data pipeline

Enrich your data inside ElasticSearch

to unlock Pathway-powered analytics for your ELK stack for free

Start a new project

and transform static and live data sources with Pathway