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

The ultimate data processing framework for the AI era

Build applications for LLM's, streaming, and batch.
Develop interactively with Python. Run in memory at scale.
Connect live data sources and run advanced algorithms on the fly.
Check it out
import pathway as pw

log_table = pw.connector(input_log_stream)
ts_table = log_table.reduce(ts = pw.reducers.max(pw.this.ts))
def sliding_window(log_table, ts_table, length):
	t_sliding_window = log_table.filter(pw.this.ts >= ts_table.ix_ref().ts - length)

return t_sliding_window

t_sliding_window = sliding_window(log_table, ts_table, 5*60)
t_alert = t_sliding_window.reduce(count=pw.reducers.count())
t_alert =
alert=pw.this.count >= alert_threshold

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The AI era is transforming data teams into software teams.

Today’s data applications blur the distinction between data scientists and data engineers. Leading data teams handle complex code logic: machine learning, time series processing, and graph algorithms. All this when working with real-time data sources and changing data.

If you are struggling to design, run, and monitor data applications, you are not alone.

Traditional data processing frameworks were not designed for the challenges we face today. They force developers to compromise on performance, scalability, or ease of use.

With Pathway you can focus on delivering value, not infrastructure.

Pathway is a Python-first framework that lets developers and ML engineers easily implement production-grade applications involving Machine Learning, LLM's, or advanced algorithms.

To provide a smooth and scalable experience with live data sources, Pathway takes care of data updates, consistency, and versioning. Developers can focus on the logic and not the plumbing.

Pathway is based on the fastest Rust runtime on the market. Its engine performs incremental computing as input data changes, leveraging the best features of Timely and Differential Dataflow, along with proprietary research.

Use it for time series analysis, alerting and anomaly detection, graph analysis, LLM applications, real-time ML, and any other data pipelines you like.

Check out our developer hub

What you get with Pathway


Dramatic ease of prototyping

Pathway provides a dramatic ease of prototyping with interactive code logic. You can easily work in notebooks, prototype your data processing pipeline, and connect or simulate live data sources.

All this with a standard Python environment. Forget the pains of setting up a local cluster just to test your application with a tuned model setting. Pathway is a pip-installable Python library.

Use the same production code and runtime engine for data science experiments, scaled production deployment in containers, and CI/CD unit tests.


Production-grade code from Day 1

Your Pathway code is production-grade already as you create it in your IDE or notebook. You don’t have to worry about scalability issues when you put it in production.

And, as a Python developer, you will never again have to worry about gluttonous JVM’s running out of resources. Promise.

What users say

Pathway’s ability to process real-time data is truly impressive. Thanks to Pathway we implemented our data pipelines for applied observability in production. If you’re looking for a top-notch data processing solutions, be sure to check out Pathway!
Priyam Kumar
Kafka and Flink are based on outdated tech which results in underperformance. Their paradigm simply is not suited to AI/ML use cases. Pathway is fast and built for the AI era.
Leading Streaming author
Enterprise Kafka Engineer at major retail chain
Going from streaming to batch and vice-versa is cumbersome for Flink or Spark. You usually end up designing and build from scratch a hybrid infra supporting both streaming & real-time. Pathway is built to resolve this pain.
Member of Apache Pulsar core team
I’m really into Pathway! It has a way lower learning curve than Flink
Flink developer
Staff Data Engineer at a leading mobility scaleup
Pathway corrects the answers of LLM’s in real time! Incrementally as well!
Hubert Dulay
Author of “Streaming Databases”
Here comes an update on the input data source, and we get alerted that the language model has changed its answer to my question. Just set a web hook, create a reactive application in Pathway, and never need to care about synchronizing data again ❤️
Pau Labarto Bajo
Creator of Real World ML
Want to create an #LLM app that can fetch real-time data? Without the need for vector databases or a complex stack? You should try Pathway.
Streamlit (Snowflake) Team

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

Put Pathway at the heart of Operational Analytics

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!

Pathway for Enterprise brings you horizontal scalability, Machine Learning toolboxes, support with SLA, and more.

See full features list

Vertical Solutions

AI Applications and data pipelines crafted for the needs of your industry. With turn-key solutions that can be owned and modified by your data team, Pathway reduces the time to build a production-grade system from long months to days.


Postal Services, 3PL, Container Shipping

Read more
AI-enabled vehicles & eSports

Telemetry tracking, Augmented reality, Realtime monitoring

Read more

Live data pipelines for AI apps, LLM-powered analytics

Read more
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