ClearML vs MLflow
A side-by-side look at 2 AI & ML tools for 2026 — the verdict, pricing, ratings, features, and what readers think.
C
clearml.com
Best for Small ML teams that want tracking plus orchestration, pipelines and remote execution in one tool.
Open-source MLOps platform for experiment tracking and automation.
Visit ClearML
M
mlflow.org
Best for Teams that want a free, portable, open-source tracker for ML models, LLM apps and agents.
Open-source platform for ML experiment tracking, model registry, and LLM and agent tracing, evaluation and prompt management.
Visit MLflowPricing compared
ClearML
$15/user/mo
MLflow
$0
Head to head
| ClearML | MLflow | |
|---|---|---|
| Starting price | $15/user/mo | $0 |
| Free tier | Yes | Yes |
| Free trial | Community plan free, no credit card required | — |
| Platforms | Web · API · Self-hosted | Web · API · CLI · Self-hosted |
Pros & cons
ClearML
Pros
- Community plan is free for teams of up to three, with 100 GB of artifact storage and 1M API calls a month.
- Combines experiment tracking, dataset versioning, pipelines, model repository and agent orchestration in one platform.
- Pro adds cloud autoscaling on AWS, GCP and Azure, hyperparameter optimisation and pipeline triggers.
- Open-source version can be self-hosted from GitHub.
- Hosted, VPC, on-prem and air-gapped deployment options for larger organisations.
Cons
- Pro pricing has a usage component (storage, metric events, API calls, application hours) that is harder to forecast than a flat fee.
- Free Community plan is limited to teams of up to three.
- A broader platform than a simple tracker, so there is more to learn.
- Scale and Enterprise plans are custom quotes.
MLflow
Pros
- Free and open source under the Apache 2.0 licence, with no per-seat fees.
- Widely adopted: the site claims more than 30 million downloads a month.
- Covers classic ML (experiment tracking, model registry, deployment) and LLM work (tracing, evaluation, prompt registry, AI Gateway) in one tool.
- Tracing is built on OpenTelemetry and supports providers and frameworks such as OpenAI, Claude, Gemini, LangChain, LangGraph and CrewAI.
- Runs locally, self-hosted or via managed offerings, and integrates with Databricks, AWS SageMaker, Azure ML and Google Vertex AI.
Cons
- Self-hosting means you run the tracking server and artifact store yourself.
- The interface is more functional than polished compared with commercial trackers.
- Managed hosting and support come from third parties such as Databricks rather than a single MLflow vendor.
Features compared
| ClearML | MLflow | |
|---|---|---|
| Plans & pricing | ||
| Free tier | — | ✓ Yes |
| Pricing model | — | Open source / Databricks managed |
| Platforms & access | ||
| Web app | — | ✓ Yes |
| Self-hosted option | — | ✓ Yes |
| Integrations & extensibility | ||
| Public API | — | ✓ Yes |
| Native integrations | — | Databricks, AWS SageMaker, Azure ML, GCP Vertex AI |
| Support | ||
| Knowledge base | — | ✓ Yes |