Buying snapshot
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| Decision point | What ZenML states |
|---|---|
License | Apache 2.0; ZenML and Kitaru are free to self-host on the Open Source plan |
Deployment | Open Source is self-hosted on your infrastructure. Starter is fully hosted SaaS. Enterprise runs in your own cloud or on-prem, air-gapped if needed, with your own LLM endpoint |
Data handling | ZenML stores only metadata: data, artifacts and compute stay in your cloud. On Starter, the AI engineer runs in ZenML's cloud and reads traces through keys you can revoke; self-hosted or on Enterprise on-prem, nothing leaves your infrastructure |
Where pipelines run | Kubernetes, Vertex, SageMaker and AzureML; one pipeline, any orchestrator, no rewrites |
Compliance | SOC 2 Type II report and ISO 27001 certification, independently audited |
Support | Open Source: community. Starter: email. Enterprise: dedicated support with an SLA and a named engineer |
Our take
ZenML fits ML and platform teams that want one Python pipeline layer across the orchestrators and clouds they already run, while keeping data in their own cloud. If your team can operate the open-source framework itself, start there. Starter suits a team shipping its first agent that wants the AI engineer hosted for it.
Evaluate ZenML
Comparisons
Where it stands out
Decorators on existing code. ZenML adds decorators to existing Python code and automates containerization, so the same code runs from laptops to Kubernetes clusters. A customer quoted by ZenML started from small local pipelines and gradually built production ones.
Versioned runs. ZenML versions every step, dataset and model, records every run and tracks artifact lineage. That helps when a change breaks a model and you need to find which run, step or dataset changed.
Caching. ZenML says its automated caching and data versioning speed up iteration, cut compute costs and eliminate idle GPU time.
Agent replay included. Kitaru, from the team behind ZenML, imports the traces you already collect from Langfuse, LangSmith, Braintrust, Logfire, Arize Phoenix or MLflow, turns findings into checks and replays a fix against the sessions that failed before it ships. Your tracing tool stays the system of record, and the hosted AI engineer runs on Kitaru.
Product screenshot
Trade-offs to weigh
Free means self-run. Open Source runs on your own infrastructure, and your own coding agent drives it. On Starter and Enterprise, the AI engineer works on its own around the clock.
Governance sits at the top tier. SSO (SAML or OIDC), SCIM, custom roles, audit logs and on-prem or air-gapped deployment come with Enterprise, which ZenML positions for regulated data, so regulated teams should plan for an Enterprise conversation rather than Starter.
Starter is sized for one agent and one project. It covers 1 AI agent, 1 ML project and up to 100 pipeline runs a month, while Open Source lists unlimited runs. Paying for Starter buys the hosted AI engineer, not more capacity; more agents, projects or runs mean Enterprise, priced on what you actually run.
Pricing and plan conditions
Starter is one price with unlimited seats, so a team of fifteen pays the same as a team of two, and the AI engineer reads every session your agent sends. The 14-day trial is full access and needs no credit card; you add a card when it ends to keep going. Replays run with your own model keys, so those model costs stay on your bill. Early-stage companies, universities, research institutions and educational users can apply for special pricing on ZenML Pro features.
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| Plan | Published price | What it adds |
|---|---|---|
Open Source | Free, self-hosted | ZenML and Kitaru; pipelines, traces, evals and replay; unlimited pipeline runs and team members |
Starter | $39/month for 1 AI agent and 1 ML project, up to 100 pipeline runs a month, unlimited seats | Everything in Open Source, plus the AI engineer for your agents and ML pipelines, and schedules, triggers and alerts; fully hosted |
Enterprise | Custom, priced on what you run | Everything in Starter, plus more agents, projects and workspaces, on-prem or air-gapped deployment, your own LLM endpoint, SSO, SCIM, custom roles and audit logs |
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
519 GitHub commits 90d317 GitHub stars0 vulnerabilities across 1 package
See all signals from 6 sources
PyPI · zenml@0.98.1
Alternatives to ZenML
The reviewed substitutes for ZenML among the ML pipeline frameworks, and what would make each one the better answer.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- MLflow
- MLflow and ZenML both organize the path from ML experiment to production model. ZenML is an open-source framework that defines portable pipelines and versions their artifacts on pluggable infrastructure from a laptop to Kubernetes, while MLflow centers on tracking experiments and managing models, so the choice turns on whether pipeline orchestration belongs in the same tool.Applies to: Deciding whether run tracking and pipeline orchestration come from one tool or two.
- Flyte
- Flyte is an alternative to ZenML for Kubernetes-native ML and data pipelines requiring typed, cached, multi-tenant task execution. We recommend Flyte over ZenML for teams that need Python or Java workflows with type-safe tasks and multi-tenant Kubernetes execution.
- Kedro
- Kedro is used rather than ZenML for modular Python data-science codebases that need a standardized project structure and visual pipeline inspection. We recommend Kedro over ZenML for teams whose immediate problem is turning loosely organized Python data-science projects into maintainable software, while accepting less emphasis on ZenML’s unified AI governance layer.
- Metaflow
- Metaflow is chosen instead of ZenML for Python-first ML and data-science projects where dependency management and practitioner workflow are the primary concerns. We recommend Metaflow over ZenML when the priority is making real-world Python ML projects manageable without centering the evaluation on a cross-stack AI control plane.
- Dagster
- Dagster and ZenML can both define and run machine learning pipelines with tracked outputs. ZenML is built for ML teams, with pluggable stack components and artifact versioning that move a pipeline from a laptop to Kubernetes, while Dagster is a general data orchestrator that treats every table, file and model as an asset.Applies to: Deciding how the stack is shaped, where both products can be part of the answer.