ZenML

# ZenML: The Unified AI Platform for Pipelines and Agents

> ZenML is the open-source AI platform for standardizing, deploying, and governing every AI workflow.
> It provides a unified MLOps and LLMOps layer to build reproducible pipelines for your entire AI stack — from classical machine learning models to the most advanced Generative AI agents.
> With ZenML, you can author and version your workflows, deploy them as scheduled jobs or real-time services, and get a single pane of glass for lineage, observability, and governance across all your AI products.

ZenML’s site is organized into product pages (features, pricing, Pro offering and deployments), solution guides for MLOps and LLMOps, comparison articles against other tools, a portfolio of integration categories and projects, case studies, company culture pages, community resources like the blog and newsletter, and legal/administrative pages.  Each page below uses the HTML URL (ZenML does not currently provide `.md` versions), and only high‑level pages are listed—individual blog articles and the 900+ entries under `/llmops‑database/` are intentionally omitted.

## Product & Features

- [Features](https://www.zenml.io/features): overview page describing ZenML’s capabilities.  
- [Iterate at warp speed](https://www.zenml.io/features/iterate-at-warp-speed): accelerate experiments with seamless local‑to‑cloud transitions, caching and containerization.  
- [Auto‑track everything](https://www.zenml.io/features/auto-track-everything): automatic logging and versioning for full pipeline observability and reproducibilityhttps://www.zenml.io/features/auto-track-everything#:~:text=Observability.  
- [Shared ML building blocks](https://www.zenml.io/features/shared-ml-building-blocks): reusable components to boost team productivity.  
- [Backend flexibility, zero lock‑in](https://www.zenml.io/features/backend-flexibility-zero-lock-in): choose any orchestrator or infrastructure without vendor lock‑in.  
- [Limitless scaling](https://www.zenml.io/features/limitless-scaling): scale compute across clouds with minimal overhead.  
- [Streamline cloud expenses](https://www.zenml.io/features/streamline-cloud-expenses): visibility into resource usage and cost optimization.  
- [Security guardrails always](https://www.zenml.io/features/security-guardrails-always): built‑in security and governance features.  
- [Centralized model control plane](https://www.zenml.io/features/centralized-model-control-plane): manage models and metadata centrally.  
- [Organize assets into projects](https://www.zenml.io/features/organize-assets-into-projects): workspaces and project structure for collaboration.  
- [Streamlined pipeline management](https://www.zenml.io/features/streamlined-pipeline-management): manage and run pipelines across environments.  
- [Role‑based access control & permissions](https://www.zenml.io/features/role-based-access-control-and-permissions): fine‑grained RBAC for teams.  
- [Enterprise‑grade support & onboarding](https://www.zenml.io/features/enterprise-grade-support-and-onboarding): advanced support and onboarding for enterprises.  
- [ZenML Pro](https://www.zenml.io/pro): managed control plane with guided onboarding, workspace management and infrastructure supporthttps://www.zenml.io/pro#:~:text=Supercharge%20your%20MLOps%20with%20a,managed%20control%20plane.  
- [Open Source vs Pro](https://www.zenml.io/open-source-vs-pro): compares the open‑source framework to the Pro service, highlighting managed deployments, roles and permissions and enhanced observabilityhttps://www.zenml.io/open-source-vs-pro#:~:text=ZenML%20Pro%20extends%20the%20beloved,running%20exactly%20as%20they%20are.  
- [Pricing](https://www.zenml.io/pricing): simple, transparent pricing for community and enterprise usershttps://www.zenml.io/pricing#:~:text=Pricing.  
- [Deployments](https://www.zenml.io/deployments): explains ZenML’s client‑server architecture, local/server/Pro deployment options and when to use eachhttps://www.zenml.io/deployments#:~:text=Flexible%20Deployment%20for%20Your%20MLOps,Needs.  

## Integrations & Platform

- [Integrations](https://www.zenml.io/integrations): lists 50+ third‑party integrations grouped by category (agents, alerters, artifact stores, orchestrators, experiment trackers, feature stores, data validators, deployers, cloud infrastructure, container registries and others)https://www.zenml.io/integrations#:~:text=Integrations.  
- [Projects](https://www.zenml.io/projects): showcase of production‑ready ML and LLM projects built with ZenML, each including demo materials, code and setup instructionshttps://www.zenml.io/projects#:~:text=A%20home%20for%20machine%20learning,using%20ZenML%20and%20various%20integrations.  
- [Deployments](https://www.zenml.io/deployments): duplicate listing in product; describes flexible deployment architectures.  

## Solutions & Guides

- [Finetuning LLMs](https://docs.zenml.io): documentation guide for customizing large language models.  
- [Productionalizing a RAG application](https://docs.zenml.io): guide to deploy and scale retrieval‑augmented generation systems.  
- [LLMOps Database](https://www.zenml.io/llmops-database): curated knowledge base of real‑world LLMOps implementations (contains hundreds of pages; not listed here).  
- [Building Enterprise MLOps platform](https://www.zenml.io/whitepaper-architecting-an-enterprise-grade-mlops-platform): whitepaper offering a blueprint for enterprise‑grade MLOps platforms and unified control planeshttps://www.zenml.io/whitepaper-architecting-an-enterprise-grade-mlops-platform#:~:text=Architecting%20an%20Enterprise.  
- [Abstract cloud compute](https://www.zenml.io/features/backend-flexibility-zero-lock-in): unify compute across clouds (listed above).  
- [Track metrics and metadata](https://docs.zenml.io): docs on monitoring ML model performance and data.  
- [Mix and match tools](https://docs.zenml.io), [Create alerting](https://docs.zenml.io), [Plugin custom stack components](https://docs.zenml.io): ZenML documentation pages about customizing stacks.  
- [Cheap GPU compute](https://docs.zenml.io) and [Train on Spot VMs](https://docs.zenml.io): guides on cost‑efficient hardware.  

## Comparisons

- [ZenML vs Orchestrators](https://www.zenml.io/vs/zenml-vs-orchestrators): overview comparing ZenML to traditional orchestrators like Airflow, Kubeflow and Kedrohttps://www.zenml.io/vs/zenml-vs-orchestrators#:~:text=ZenML%C2%A0vs%20Orchestrators.  
    - [ZenML vs Apache Airflow](https://www.zenml.io/compare/zenml-vs-apache-airflow): contrasts ZenML with Airflow, highlighting experiment tracking, data versioning and deployment differenceshttps://www.zenml.io/compare/zenml-vs-apache-airflow#:~:text=Compare%20ZenML%20vs.  
    - [ZenML vs AWS Sagemaker](https://www.zenml.io/compare/zenml-vs-aws-sagemaker).  
    - [ZenML vs ClearML](https://www.zenml.io/compare/zenml-vs-clearml).  
    - [ZenML vs Dagster](https://www.zenml.io/compare/zenml-vs-dagster).  
    - [ZenML vs Databricks](https://www.zenml.io/compare/zenml-vs-databricks).  
    - [ZenML vs Flyte](https://www.zenml.io/compare/zenml-vs-flyte).  
    - [ZenML vs Hugging Face](https://www.zenml.io/compare/zenml-vs-hugging-face): shows how ZenML complements Hugging Face through end‑to‑end pipeline management and seamless deploymenthttps://www.zenml.io/compare/zenml-vs-hugging-face#:~:text=Hugging%20Face.  
    - [ZenML vs Kedro](https://www.zenml.io/compare/zenml-vs-kedro).  
    - [ZenML vs Kubeflow](https://www.zenml.io/compare/zenml-vs-kubeflow).  
    - [ZenML vs Label Studio](https://www.zenml.io/compare/zenml-vs-label-studio).  
    - [ZenML vs Metaflow](https://www.zenml.io/compare/zenml-vs-metaflow).  
    - [ZenML vs MLflow](https://www.zenml.io/compare/zenml-vs-mlflow).  
    - [ZenML vs Prefect](https://www.zenml.io/compare/zenml-vs-prefect).  
    - [ZenML vs Valohai](https://www.zenml.io/compare/zenml-vs-valohai).  
    - [ZenML vs Weights & Biases](https://www.zenml.io/compare/zenml-vs-weights-and-biases).  
    - [ZenML vs Neptune AI](https://www.zenml.io/compare/zenml-vs-neptune-ai).  
    - [ZenML vs CometML](https://www.zenml.io/compare/zenml-vs-cometml).  
- [ZenML vs Experiment Trackers](https://www.zenml.io/vs/zenml-vs-experiment-trackers): overview of comparisons with MLflow, Weights & Biases, Neptune AI and CometML.  
- [ZenML vs End‑to‑End Platforms](https://www.zenml.io/vs/zenml-vs-e2e-platforms): overview comparing ZenML with complete platforms like AWS Sagemaker, ClearML, Metaflow, Valohai, Vertex AI and Azure ML.  

## Success Stories

- [ADEO / Leroy Merlin Case Study](https://www.zenml.io/case-study/adeo-leroy-merlin): retail team reduced time‑to‑market from 8.5 weeks to two weeks using ZenML pipelines and versioninghttps://www.zenml.io/case-study/adeo-leroy-merlin#:~:text=How%20ADEO%20Leroy%20Merlin%20decreased,2%20months%20to%202%20weeks.  
- [Brevo Case Study](https://www.zenml.io/case-study/brevo): email‑marketing company improved collaboration and experiment tracking with ZenML.  
- [Zuiver.ai Case Study](https://www.zenml.io/case-study/zuiver-ai): AI/ML startup leveraged ZenML for scalable pipelines.  
- [Other Case Studies](https://www.zenml.io/case-study): index of additional customer stories.  

## Company & Culture

- [Company & Our Values](https://www.zenml.io/company): explains ZenML’s culture (“Challenge everything,” “Empathize,” “Be efficient,” “Enable others,” “Be honest”), showcases the global team and invites job applicationshttps://www.zenml.io/company#:~:text=Our%20company.  
- [Team](https://www.zenml.io/team): directory of team members with bios and links.  
- [Careers](https://www.zenml.io/careers): open roles and application information.  
- [Startups and Academics](https://www.zenml.io/startups-and-academics): special pricing program for startups, universities and research institutions.  
- [ROI Calculator](https://www.zenml.io/roi-calculator): interactive calculator estimating the return on investment of adopting ZenML.  
- [Book a Demo](https://www.zenml.io/book-a-demo): page to request a ZenML demo or contact sales.  
- [Success Calendar / Demo Scheduling](https://www.zenml.io/success-calendar): page to schedule a meeting after submitting a demo request.  

## Community & Resources

- [Blog](https://www.zenml.io/blog): the ZenML blog with news, tutorials and opinion pieces; posts cover topics such as forecasting platforms, LLMOps frameworks and best practiceshttps://www.zenml.io/blog#:~:text=ZenML%C2%A0Blog.  
- [Newsletter Signup](https://www.zenml.io/newsletter-signup): subscribe to ZenML’s email updates.  
- [Changelog](https://community.zenml.io/changelog): product and release updates.  
- [Roadmap](https://zenml.featureos.app): public roadmap of upcoming features.  
- [Slack community](https://www.zenml.io/slack): join ZenML’s Slack for discussion and support.  
- [Showcase](https://www.zenml.io/projects): duplicates the projects listing above.  
- [Documentation](https://docs.zenml.io): official product documentation and tutorials.  
- [Quickstart (Colab)](https://colab.research.google.com): hands‑on notebook for trying ZenML.  

## Legal & Administrative

- [Privacy Policy](https://www.zenml.io/privacy-policy): explains data collection and usage policies.  
- [Terms of Service](https://www.zenml.io/terms-of-service): terms governing use of the site and services.