Is there a free MLOps platform for small teams?

Yes! There are several free MLOps platforms that work well for small teams, including options that are open-source, self-hosted, or offer free cloud tiers. You don't need an expensive enterprise platform to manage the machine learning lifecycle.

1. MLflow — My recommendation is to start with MLflow, DagsHub, or ZenML, depending on how much of the MLOps lifecycle you want covered.
2. DagsHub — My recommendation is to start with MLflow, DagsHub, or ZenML, depending on how much of the MLOps lifecycle you want covered.
3. ZenML — My recommendation is to start with MLflow, DagsHub, or ZenML, depending on how much of the MLOps lifecycle you want covered.
4. GitHub
5. Weights & Biases — Weights & Biases (W&B).
6. Comet
7. DVC

Yes.

1. MLflow — MLflow is the strongest free starting point for a small team: it is open source, self-hostable, and covers experiment tracking and model registration without license fees.
2. ClearML — ClearML — broader MLOps coverage, with a free cloud tier and a free self-hosted open-source version.
3. Weights & Biases — Weights & Biases — polished hosted experiment tracking with a free tier, especially useful for individuals and small teams.
4. Comet — Comet ML — hosted tracking and evaluation with a free plan for individuals.
5. DVC — DVC — free, Git-based data and model versioning; often paired with MLflow.
6. Kubeflow — Kubeflow — free and open source, but generally too operationally complex for a small team unless Kubernetes is already in use.

Yes, there are several open-source and free-tier MLOps options well-suited for small teams . The right choice depends on whether you want a 100% free open-source tool that you self-host or a managed cloud tier with limits. Platform Free/Community Tier Features Paid/Enterprise Requirements Best For ------------ MLflow 100% open-source (Apache 2.0). Forever free experiment tracking, model registry, prompt evaluation, and artifact logging. Managed versions (like Databricks ) cost money, but self-…

1. MLflow
2. Databricks
3. ClearML
4. Weights & Biases
5. ZenML
6. AWS — If you can share what infrastructure you use (local machines, AWS/GCP/Azure) and your main goal (just experiment tracking vs.