mlop
mlop is an open-source experiment tracking platform for machine learning teams, available through web and self-hosted deployment.
What mlop is
mlop is an open-source experiment tracking platform for machine learning teams, available through web and self-hosted deployment. It records and compares experiments, model versions, hyperparameters, metrics, artifacts, and gradients. Documentation describes real-time training monitoring, interactive dashboards, and experiment comparison views. The platform can alert users to poor training performance and offer suggestions, including responses to exploding gradients. It tracks experiment versions and uncommitted files in real time for reproducibility. mlop states that it is fully compatible with the Weights & Biases API, with migration guidance for PyTorch Lightning and Hugging Face Transformers. A Python SDK is installed with pip install "mlop[full]". The Free plan costs nothing and includes one seat, unlimited logging hours, and 10 GB storage. Pro has up to 10 seats and 100 GB storage; Enterprise adds unlimited seats and storage, self-hosting, a security audit, and 24/7 founder support. Compute is Private Beta and Inference is Coming Soon.
Good
- Tracks experiments, models, metrics, artifacts, and gradients
- Real-time monitoring and comparison views
- Weights & Biases API compatibility
- Free plan with unlimited logging hours
- Web and self-hosted deployment options
Watch out
- Compute is labeled Private Beta
- Inference is labeled Coming Soon
Who it suits
It suits individual developers and small teams training machine learning models who need experiment tracking, comparisons, dashboards, and API or SDK access.
Verdict
mlop focuses on tracking machine learning work with comparisons, alerts, reproducibility details, and framework migration support. Its free tier provides core logging capacity, while higher tiers add seats, storage, support, and self-hosting.
Plans and prices
From mlop's own pricing page, checked 8 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| Free | Free | 1 seat · Unlimited logging hours · 10 GB storage · $0/month |
| Enterprise | Priced on request | Unlimited seats · Unlimited logging hours · Unlimited storage · 24/7 founder support · Self-hosted option · Security audit · Private Slack channel · Custom |
| Pro | Priced on request | Up to 10 seats · Unlimited logging hours · 100 GB storage · Email support · Let's Talk |
The facts
Each one read from mlop's own pages, with the date it was checked.
What it does
- API key security
- The documentation says secure API keys are hashed and shown only when generated, while insecure keys are not hashed.checked 8 Oct 2026
- Availability
- The homepage labels Compute as Private Beta and Inference as Coming Soon.checked 8 Oct 2026
- Contact
- The contact page offers a 15-minute consultation by Google Meet or Zoom.checked 8 Oct 2026
- Experiment tracking
- The platform tracks and compares experiments, model versions, hyperparameters, metrics, artifacts, and gradients.checked 8 Oct 2026
- Framework integrations
- The documentation provides migration instructions for PyTorch Lightning and Hugging Face Transformers.checked 8 Oct 2026
- Insights and alerts
- mlop says it can alert users to poor training performance and provide suggestions, including how to address exploding gradients.checked 8 Oct 2026
- Intended users
- The documentation describes mlop as a tool for people who train machine learning models and says its free tier is for individual developers and small teams.checked 8 Oct 2026
- Product
- mlop describes itself as an open source experiment tracking platform for machine learning teams.checked 8 Oct 2026
- Reproducibility
- The maker says the platform tracks experiment versions and uncommitted files in real time.checked 8 Oct 2026
- SDK
- The quickstart instructs users to install the Python SDK with pip install "mlop[full]".checked 8 Oct 2026
- Security option
- The Enterprise plan lists a security audit and a self-hosted option.checked 8 Oct 2026
- Support
- The pricing page includes email support for Pro and 24/7 support from founders for Enterprise.checked 8 Oct 2026
- Visualization
- Its documentation describes real-time training monitoring, interactive performance dashboards, and experiment comparison views.checked 8 Oct 2026
- Weights & Biases compatibility
- The site says mlop is 100% compatible with the Weights & Biases API, and the documentation describes migration support.checked 8 Oct 2026
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