zenml
ZenML: MLOps for Reliable AI: from Classical AI to Agents.
Decision gist · record as of 2026-08-14
Yes. ZenML is actively maintained, has low install friction, uses a permissive Apache-2.0 license, and has no known security vulnerabilities. It solves a real problem—orchestrating ML and AI workflows across infrastructure—without requiring you to rewrite existing code. Suitable for teams moving from ad-hoc scripts to production MLOps, though the 17 runtime dependencies and learning curve around stacks warrant evaluation in your specific environment.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (up to 3.14).
- The [server] extra installs additional dependencies for running a local ZenML server; the base install provides a client-only mode.
- Low friction install with a pure Python wheel.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions—suitable for most production and proprietary settings.
last release 2026-08-07 (7 days) · last repo commit 2026-08-14 · 5,557 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 198,588 downloads/mo, #9,725 on PyPI
Alternatives
Verify before relying
pip install zenml
zenml init
zenml login
from zenml import pipeline, step
@step
def my_step():
return "result"
@pipeline
def my_pipeline():
my_step()- Whether the 17 runtime dependencies introduce significant transitive dependency bloat in production environments.
- Performance characteristics and scalability limits when orchestrating large numbers of concurrent pipeline runs.
- Specific cloud provider support coverage beyond the documented integrations (MLflow, Langfuse, Sagemaker, GCP Vertex).
What it is and what it does
ZenML is an MLOps orchestration framework designed for teams running traditional ML, LLM workflows, or agentic systems in production. It abstracts infrastructure complexity by letting you write pipelines in Python, then automatically containerizes, tracks, and deploys them across any backend—local, Kubernetes, cloud providers, or custom stacks. At its core, you wrap your existing code in @step and @pipeline decorators; ZenML then handles containerization, artifact tracking, metrics logging, and integration with tools like MLflow, Weights & Biases, and cloud platforms.
The platform is built on a client-server architecture with an integrated web dashboard. You can start locally with `pip install "zenml[local]"` for development, then deploy the server separately and connect via `zenml login` for production. It supports Python 3.10 through 3.14 and depends on well-maintained libraries (pydantic, docker, opentelemetry, click, rich) for configuration, containerization, observability, and CLI interaction. The project is actively maintained, with recent releases and thousands of companies using it.
Use it for
- Orchestrate end-to-end ML pipelines with feature engineering, model training, and batch inference across distributed infrastructure.
- Deploy and monitor LLM applications and RAG systems with built-in evaluation loops and production observability.
- Build and manage agentic workflows that integrate with existing Python tools and frameworks with automatic run tracking.
- Unify classical ML model deployment and AI agent management in a single framework without rewriting existing code.
- Iterate rapidly on experiments in development with automatic artifact versioning, then promote to production without changes.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
ZenML is actively maintained, has low install friction, uses a permissive Apache-2.0 license, and has no known security vulnerabilities. It solves a real problem—orchestrating ML and AI workflows across infrastructure—without requiring you to rewrite existing code. Suitable for teams moving from ad-hoc scripts to production MLOps, though the 17 runtime dependencies and learning curve around stacks warrant evaluation in your specific environment.
Install
zenml on PyPI
Before you install
Low friction install with a pure Python wheel. Active maintenance with a recent release (7 days ago) and 5557 GitHub stars. Depends on 17 runtime packages including docker, pydantic, and opentelemetry, which are well-established libraries.
Requires Python 3.10 or later (up to 3.14). The [server] extra installs additional dependencies for running a local ZenML server; the base install provides a client-only mode.
License in practice
Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions—suitable for most production and proprietary settings.
Quickstart
pip install zenml
zenml init
zenml login
from zenml import pipeline, step
@step
def my_step():
return "result"
@pipeline
def my_pipeline():
my_step()Verify before relying
- Whether the 17 runtime dependencies introduce significant transitive dependency bloat in production environments.
- Performance characteristics and scalability limits when orchestrating large numbers of concurrent pipeline runs.
- Specific cloud provider support coverage beyond the documented integrations (MLflow, Langfuse, Sagemaker, GCP Vertex).
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <3.15,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 17 packagesasgirefclickcloudpickledistrodockergitpythonjsonrefopentelemetry-instrumentation-loggingopentelemetry-sdkpackagingpsutilpydanticpython-dateutilpyyamlrichsetuptoolsstructlog |
| Maintenance | Actively maintained 7 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 198,588 / month, #9,725 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchIntended Audience :: System AdministratorsLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Software Development :: Libraries :: Python ModulesTopic :: System :: Distributed ComputingTyping :: Typed |
Evidence: zenml-0.96.3-py3-none-any.whl
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See also mlflow · clearml · azureml-pipeline-core · sagemaker-mlops · kfp · azureml-pipeline-steps · azureml-pipeline · azureml-mlflow · clearml-agent · tecton