The best MLOps tools help data scientists and engineers track experiments, version models, build pipelines and serve models reliably in production. Many are free and open source, while managed platforms add hosting, scaling and support. Eleven options follow in no particular order, and most of them are smaller, specialist projects.
Getting a model to work in a notebook is the easy part. Keeping track of which data, code and settings produced it, then shipping and watching it in production, is where most teams struggle.
The eleven tools below are listed alphabetically and in no particular order, and every description comes from the project's or vendor's own website, read this week. For context on one of the big cloud platforms, my guide to what Vertex AI is explains how Google packages this work.
MLOps tools at a glance
I checked the free plan column on each tool's own website, as of Oct 2026. Yes means the site names a free plan, a free version, a free tier or an open source edition. Trial only means a time-limited trial. Not stated means the pages I read named neither, even where there's a button inviting you to try it, so check the vendor's page before you sign up.
| Tool | Type | Good for | Free plan |
|---|---|---|---|
| BentoML | Model serving and inference platform | Packaging models for serving | Yes |
| ClearML | AI infrastructure platform | Squeezing more from GPUs | Not stated |
| Comet | AI developer platform | Experiment tracking plus LLM evaluation | Not stated |
| Flyte | Open source AI orchestration | Durable pipelines on Kubernetes | Yes |
| Hopsworks | AI lakehouse and feature store | Real-time machine learning features | Not stated |
| Kubeflow | Open source AI platform | Kubernetes-native ML | Yes |
| Metaflow | ML and data science framework | Data scientists shipping real projects | Yes |
| MLflow | Open source AI platform | Experiment tracking and model registry | Yes |
| Polyaxon | AI control plane for Kubernetes | Self-hosted experiment management | Yes |
| Valohai | MLOps platform | Specialised models and LLM fine-tuning | Not stated |
| ZenML | Open source ML pipelines | Portable pipelines across clouds | Yes |
Eleven MLOps tools worth a look
BentoML
Best for: ML engineers who want to package and serve any model anywhere, with an open-source framework and a managed inference platform.
ClearML
Best for: Teams with their own GPUs who want an AI infrastructure platform for scheduling, scaling and running workloads.
Comet
Best for: Teams who want experiment tracking and the Opik evaluation tool for LLM apps from one AI developer platform.
Flyte
Best for: Platform teams who want an open source, Apache 2.0 durable runtime for orchestrating AI and data pipelines.
Hopsworks
Best for: Real-time ML builders who want an AI lakehouse with a feature store and very low latency serving.
Kubeflow
Best for: Kubernetes-heavy teams who want an open source, community-built platform for training and serving models.
Metaflow
Best for: Data scientists who want the framework Netflix open-sourced for building and running real-life ML and AI projects.
MLflow
Best for: Data science groups who want a widely used open source platform for tracking experiments, models, agents and LLM apps.
Polyaxon
Best for: Infrastructure teams who want an open-source AI control plane on Kubernetes for running and managing experiments.
Valohai
Best for: Companies training specialised models and LLMs who want an MLOps platform that tracks every run automatically.
ZenML
Best for: Engineers who want Apache 2.0 open source pipelines they can self-host and move between clouds and stacks.
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Where to start
- Experiment tracking and a model registry: MLflow or Comet.
- Kubernetes-native ML: Kubeflow or Polyaxon.
- Portable pipelines: ZenML or Flyte.
- Data science projects that ship: Metaflow.
- Serving and inference: BentoML.
- Real-time features: Hopsworks.
- GPU infrastructure: ClearML.
- Specialised models and LLM fine-tuning: Valohai.
Logging may be a legal requirement, not a nice extra
Article 12 of the European Union's AI Act, published on EUR-Lex as Regulation (EU) 2024/1689, says high-risk AI systems shall technically allow for the automatic recording of events (logs) over the lifetime of the system.
If your models could ever fall into a high-risk category, lineage and logging aren't optional. Pick tools that record which data, code and settings produced each model, and keep those records for as long as the model is in use.
Open source stack or managed platform?
Open source tools need no licence fees and let you mix and match, but your team runs and upgrades them. They suit engineering-heavy teams that want control.
Managed platforms take the hosting and scaling off your hands. They're quicker to start, so check where your data and models are stored, and how easy it'd be to move away later.
Questions to ask before you choose
- Do I need experiment tracking, pipelines, serving or all three?
- Will it run on my cloud or on Kubernetes?
- Does it record data and model lineage?
- Can it handle large language models as well as classic ML?
- Is there an open source version I can self-host?
- How easy is it to export everything if we switch?
Signs your ML team needs MLOps tools
- Nobody knows which model is live. Add a registry.
- Results can't be reproduced. Track experiments.
- Deployments are manual. Add pipelines.
- Models drift unnoticed. Add monitoring.
Do you make MLOps or machine learning platforms? Tool makers can ask to be considered for a featured place on this list.
My plain advice on choosing MLOps tools
Start with experiment tracking and a model registry, because that's where most of the pain sits. Add pipelines and serving once you have more than one model in production, not before.
Prefer tools that work with open formats, so you're not locked in if your needs change, and write down who owns each model once it's live. If you're new to how modern models reason, my explainer on reasoning models is a useful primer for the wider team. It's worth sharing before your next planning meeting.
Frequently asked questions
What are MLOps tools?
They're tools for tracking experiments, versioning models, building pipelines and deploying and monitoring machine learning models in production.
Are there free MLOps tools?
Yes. Many tools in this list are open source and free to self-host.
Why does logging matter for machine learning?
It shows which data and settings produced a model. The EU AI Act's Article 12 says high-risk AI systems must allow automatic recording of events over their lifetime.
Do I need Kubernetes for MLOps?
Not always. Some tools are built for Kubernetes, while others run on a laptop or a managed cloud.
Can MLOps tools handle large language models?
Many now do, with features for tracking prompts, evaluations and fine-tuning runs.
Should a small team use a managed platform?
Often, yes, because it saves running infrastructure, though open source tools give more control.
