Build real-time ML systems without the MLOps migraine

The Python stream processing platform for data teams. Ingest data from any source. Transform streaming data with Python and test with Git & CI/CD. Then serve data back to production in real time. All in one modular platform.

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99.97%

Uptime (90 days)

57tb

Processed

1390

Developers

934

Organizations

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By developers for developers

Production-ready pipelines in days, not months

Imagine if data teams could work with real-time data to build features and ML models with pure Python. Then autonomously serve processed data back to production in real time with robust, scalable data pipelines. While platform teams retain full control and flexibility of the underlying infrastructure.

All this without re-engineering Python into Java, manually deploying to testing or production environments, or wasting time and money battling complex real-time infrastructure.

Quix lets you do just that.

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What can you build with Quix?

Real-time ML pipelines

Time-series data pipelines

Event-driven data pipelines

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THE PRODUCT

What makes Quix unique?

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High performance serverless platform

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Get a scalable, full-stack yet modular streaming platform

Quix is a modular platform to serve robust and scalable real-time data pipelines back to production, without losing control.

Choose your own infrastructure options across any cloud or on-prem vendor or self-hosted: we provide customers with a control plane and pre-built connectors to orchestrate any infrastructure environment they choose to provision.

Minimal configuration and easy debugging

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Develop faster with Pure Python

Ingest streaming data from anywhere and transform it with pure Python.

With Quix, you have a Python development environment for stateful stream processing that abstracts away the underlying complexity of streaming data (Kafka) and containers (Kubernetes) without needing to learn new technologies or languages.

Feature rich development tools

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Turn ML models into production ML systems with a single click

Quix leverages the best-in-class open-source solutions to build development- and production-ready pipelines where testing environments match the real world.

Reduce the time it takes to go from training ML models to production ML systems, without the hassle of re-engineering your code.

“Quix has given us an environment to handle a huge amount of real-time machine data.”
Chris Angell Cloud NC
Chris Angell

Lead Systems Engineer, CloudNC

“The lightbulb moment happened when we realized how resilient Quix is. We’ve automated a new product feature and Quix’s architecture gives us confidence it won’t fail.”
Nathan Sanders Control
Nathan Sanders

Technical Director, Control

“We built a complex real-time pipeline for a zero-trust client on Quix, it's very secure and resilient.”
Alex Chooi CK Delta
Alex Chooi

Senior Software Engineer, CKDelta

“Quix saved us from hiring a whole data engineering team to build a real-time predictive maintenance application.”
Jonathan Wilkinson Airdale
Jonathan Wilkinson

CTO, Airedale

“We're able to collect and process new data in milliseconds, giving us an advantage others cannot match.”
Baptiste Quidet Quidios
Baptiste Quidet

Founder and Lead Data Scientist, Quideos

“Instead of spending months with research and unknown costs afterwards, we use Quix as our nervous system for a real-time trading platform with high performance infrastructure and deploy engine. The tech support behind Quix is pretty awesome.”
Christoph Dietrich Proreactware
Christoph Dietrich

Founder and Lead Software Engineer, proreactware

“Quix is one of the more interesting options because it's more or less Faust 2.0 - pure Python with the annoying bits handled.”
Ben Gamble Aiven
Ben Gamble

Developer Advocate, Aiven

It’s free to get started

Sign up for free and get up and running in minutes with our library of connectors and code samples.

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Connect with the Quix Community

Get your questions answered in our community Slack channel, chat with us face-to-face at an event, or contribute to our open source connectors on GitHub.

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