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ZenML - One AI Platform - From Pipelines to Agents

Discover the LLMOps Database, a curated knowledge base of real-world implementations

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Product

DATA SCience

Iterate at warp speed

Accelerate your ML workflow seamlessly

Auto-track everything

Automatic logging and versioning

Shared ML building blocks

Boost team productivity with reusable components

Infrastructure

Backend flexibility, zero lock-in

One framework for all your MLOps and LLMOps needs

Limitless scaling

Effortlessly deploy across clouds

Streamline cloud expenses

Gain clarity on resource usage and costs

Organization

ZenML Pro

Our managed control plane for MLOps

ZenML vs Other Tools

Compare ZenML to other ML tools

Integrations

50+ integrations to ease your workflow

Solutions

GENAI & LLMS

Finetuning LLMs

Customize large language models for specific tasks

Productionalizing a RAG application

Deploy and scale RAG systems

LLMOps Database

A curated knowledge base of real-world implementations

mlops

Building Enterprise MLOps

Platform architecture and best practices

Abstract cloud compute

Simplify management of cloud-based ML resources

Track metrics and metadata

Monitor and analyze ML model performance and data

Success Stories

Adeo Leroy Merlin

Retail

Brevo

Email Marketing

Cross Screen Media

Media

Developers

Documentation

Docs

Comprehensive guides to use ZenML

Deploying ZenML

Understanding ZenML system architecture

Tutorials

Examples showing ZenML in action

GUIDES

Quickstart

Quickly get your hands dirty

Showcase

Projects of ML use cases built with ZenML

Starter Guide

Get started with the basics

COMMUNITY

Slack

Join our Slack Community

Changelog

Discover what’s new on ZenML

Roadmap

Join us on our MLOps journey

PricingBlogShowcase

Book a demo

Get Started

# From Pipelines to Agents.
One AI Platform.

An open-source foundation to ship reliable AI products at scale - on any cloud, anywhere.

Use Open Source

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## Trusted by 1,000s of top companies to standardize their AI workflows

The ZenML Advantage

## Unified AI platform **Bridging ML and GenAI**

78%

faster time‑to‑market

65%

reduced engineering overhead

3x

more workflows
in production

5x

faster time to production

Simple, elegant AI workflows

All you need is Python decorators to supercharge your existing code. Easily migrate your existing code, bring your own preferred frameworks, and run batch and real-time flows with one beautiful SDK.

Full observability

Never lose track of your workflows. Visual pipeline graphs, automatic versioning of code and data artifacts, real-time execution monitoring, and comprehensive logging. See exactly what ran, when, and why - with full reproducibility built in.

Deploy anywhere

Your code, your infrastructure choice. Configure deployment stacks with your preferred orchestrators and cloud providers. Whether it's AWS, GCP, Azure, Kubernetes, or Modal - register once, deploy everywhere. Same paradigm, for both batch and real-time pipelines, no matter where you run.

Prototype and compare

Rapid experimentation made simple. Modify deployed pipeline artifacts like prompts and configs directly from your dashboard, trigger runs instantly, and compare performance across iterations. Test new ideas without breaking production - all with visual diff tools and side-by-side result analysis.

Made for enterprise teams

Open source meets enterprise security. Role-based access controls, encrypted secrets management, service connectors for cloud integrations, and multi-tenant workspaces. Organize teams and projects with enterprise-grade governance while maintaining the flexibility of open source innovation.

### Plug-in  your existing pipelines and agents

Keep what works, add what’s missing. ZenML takes your existing AI or Agentic workflows and supercharges it with unified orchestration, governance, and automated lineage tracking.

### From batch evals to real-time serving

Teams today glue together a batch orchestrator, serving stack, eval framework, and tracker and lose velocity in the gaps. ZenML collapses that toolchain into a single pipeline that runs locally for dev, in batch for evaluations, and as a real time endpoint.

### ZenML does not replace your existing tools

It creates an experience layer around the way you work. Centralized tracking, observability, and governance across traditional ML and model GenAI workflows.

### Open-source & easy deployment

The framework is completely OSS under Apache 2.0, and the Pro version is SOC2 and ISO 27001 compliant. Easily deploy ZenML on your infrastructure, and easily connect your cloud infrastructure.

### Ready to Unify Your AI Platform?

Join thousands of teams using ZenML to eliminate chaos and accelerate AI delivery

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Use Open Source

## Use ZenML with any framework

60+ integrations across the AI ecosystem. From sklearn to LangGraph.

Use Open Source

See All Integrations

Whitepaper

## ZenML as your Enterprise-Grade AI
Platform

We have put down our expertise around building production-ready, scalable AI platforms, building on insights from our top customers.

Get The Whitepaper

## Customer Stories

Learn how teams are using ZenML to save time and simplify their MLOps.

### How ADEO Leroy Merlin decreased their time-to-market from 2 months to 2 weeks

### How Brevo accelerated model development by 80% using ZenML

### How Cross Screen Media Trains Models for 210 Markets in Hours, Not Weeks, with ZenML

ZenML offers the capability to build end-to-end ML workflows that seamlessly integrate with various components of the ML stack. This enables teams to accelerate their time to market by bridging the gap between data scientists and engineers.

**Harold Giménez**

SVP R&D at HashiCorp

Many teams still struggle with managing models, datasets, code, and monitoring as they deploy ML models into production. ZenML provides a solid toolkit for making that easy in the Python ML world.

**Chris Manning**

Professor of Linguistics and CS at Stanford

ZenML's approach to standardization and reusability has been a game-changer for our ML teams. We've significantly reduced development time with shared components, and our cross-team collaboration has never been smoother.

**Maximillian Baluff**

Lead AI Engineer at IT4IPM

ZenML's automatic logging and containerization have transformed our MLOps pipeline. We've drastically reduced environment inconsistencies and can now reproduce any experiment with just a few clicks.

**Liza Bykhanova**

Data Scientist at Competera

ZenML allows orchestrating ML pipelines independent of any infrastructure or tooling choices. ML teams can free their minds of tooling FOMO from the fast-moving MLOps space, with the simple and extensible ZenML interface.

**Richard Socher**

Former Chief Scientist Salesforce and Founder of You.com

Thanks to ZenML we've set up a pipeline where before we had only jupyter notebooks. It helped us tremendously with data and model versioning.

**Francesco Pudda**

Machine Learning Engineer at WiseTech Global

ZenML has transformed how we manage our GPU resources. The automatic deployment and shutdown of GPU instances have significantly reduced our cloud costs. We're no longer paying for idle GPUs, and our team can focus on model development instead of infrastructure management.

**Christian Versloot**

Data Technologist at Infoplaza

ZenML allowed us a fast transition between dev to prod. It’s no longer the big fish eating the small fish – it’s the fast fish eating the slow fish.

**François Serra**

ML Engineer / ML Ops / ML Solution architect at ADEO Services

ZenML allows you to quickly and responsibly go from POC to production ML systems while enabling reproducibility, flexibitiliy, and above all, sanity.

**Goku Mohandas**

Founder of MadeWithML

With ZenML, we're no longer tied to a single cloud provider. The flexibility to switch backends between AWS and GCP has been a game-changer for our team.

**Dragos Ciupureanu**

VP of Engineering at Koble

After benchmarking several solutions, we chose ZenML for its stack flexibility and incremental process. We started from small local pipelines and gradually created more complex production ones.

**Clément Depraz**

Data Scientist at Brevo

News

## **Latest ZenML Updates**

Stay updated on the latest developments, announcements, and updates from the ZenML ecosystem.

See all news

— 2,9k

### Newsletter 18: Real-Time AI, Zero Cold Starts

### Why Pipelines Are the Right Abstraction for Real-Time AI (Agents Included)

### Newsletter 17: What Teams Need to Ship AI Agents

No compliance headaches

## Your VPC, your data

ZenML is a metadata layer on top of your existing infrastructure, meaning all data and compute stays on your side.

ZenML is SOC2 and ISO 27001 Compliant

### We Take Security Seriously

ZenML is SOC2 and ISO 27001 compliant, validating our adherence to industry-leading standards for data security, availability, and confidentiality in our ongoing commitment to protecting your ML workflows and data.

### Looking to Get Ahead in MLOps & LLMOps?

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Support

## Frequently asked questions

Everything you need to know about the product.

What is the difference between ZenML and other machine learning orchestrators?

Unlike other machine learning pipeline frameworks, ZenML does not take an opinion on the orchestration layer. You start writing locally, and then deploy your pipeline on an orchestrator defined in your MLOps stack. ZenML supports many orchestrators natively, and can be easily extended to other orchestrators. Read more about why you might want to write your machine learning pipelines in a platform agnostic way here.

Does ZenML integrate with my MLOps stack (cloud, ML libraries, other tools etc.)?

As long as you're working in Python, you can leverage the entire ecosystem. In terms of machine learning infrastructure, ZenML pipelines can already be deployed on Kubernetes, AWS Sagemaker, GCP Vertex AI, Kubeflow, Apache Airflow and many more. Artifact, secrets, and container storage is also supported for all major cloud providers.

Does ZenML help in GenAI / LLMOps use-cases?

Yes! ZenML is fully compatabile, and is intended to be used to productionalize LLM applications. There are examples on the ZenML projects repository that showcases our integrations with Llama Index, OpenAI, and Langchain. Check them out here!

How can I build my MLOps/LLMOps platform using ZenML?

The best way is to start simple. The user guides walk you through how to build a miminal cloud MLOps stack. You can then extend with the other numerous components such as experiment tracker, model deployers, model registries and more!

What is the difference between the open source and Pro product?

ZenML is and always will be open-source at its heart. The core framework is freely available on Github and you can run and manage it in-house without using the Pro product. On the other hand, ZenML Pro offers one of the best experiences to use ZenML, and includes a managed version of the OSS product, including some Pro-only features that create the best collaborative experience for many companies that are scaling their ML efforts. You can see a more detailed comparison here.

Still not clear?

Ask us on Slack

## Unify Your ML and LLM Workflows

Free, powerful MLOps open source foundation

Works with any infrastructure

Upgrade to managed Pro features

Use Open Source

Book a Demo

Simplify MLOps

Product

Features

ZenML Pro

New

OSS vs Managed

Integrations

Pricing

Resources

Newsletter

New

Blog

Docs

Roadmap

Slack

Company

Careers

About Us

Our Values

Join Us

ZenML vs Orchestrators

Apache Airflow

Dagster

Databricks

Flyte

Kedro

Kubeflow

Prefect

ZenML vs Exp Trackers

MLflow

Weights & Biases

Neptune AI

CometML

ZenML vs e2e Platforms

AWS Sagemaker

ClearML

Metaflow

Valohai

GCP Vertex AI

Azure ML

ClearML

GenAI & LLMs

LLMOps Database

Finetuning LLMs

Creating a code copilot

Cheap GPU compute

MLOps Platform

Mix and match tools

Create alerting

Plugin custom stack components

Leveraging Hyperscalers

Train on Spot VMs

Deploying Sagemaker Endpoints

Managing GCP Vertex AI

Training on Kubernetes

Local to Sagemaker Pipelines

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