Comet ML / comet.com
ML experiment tracking, model registry, and LLM evaluation platform that captures training metrics, hyperparameters, and artefacts automatically for reproducibility and team collaboration.
Pricing
$179/mo
Free plan
Yes
Category
Developer Tools
Platforms
4
Free plan
Yes
API access
Yes
Open source
No
Platforms
4
Comet ML is one of the established ML experiment tracking platforms, competing with Weights & Biases as a comprehensive platform for logging ML experiments, tracking model performance across training runs, managing model registries, and evaluating LLM outputs. Founded in 2017, Comet predates the current LLM wave and has expanded from classical ML tracking to include LLMOps tooling.
Automatic experiment tracking captures hyperparameters, code snapshots, metrics, system metrics, and model artefacts from ML training runs with minimal instrumentation — adding a few lines of code to a training script instruments everything Comet tracks. The no-instrumentation auto-logging option works with popular frameworks (Keras, PyTorch Lightning, Hugging Face Trainer) without any code changes.
Comet Optics is the LLM evaluation and monitoring layer, providing prompt management, LLM output evaluation (hallucination detection, relevance scoring), and production monitoring of LLM applications — positioning Comet alongside Langfuse and Braintrust for the LLMOps market.
The model registry manages the model lifecycle from experiment to production — tracking which experiment produced which model, storing model artefacts with versioning, managing deployment status, and providing a single interface for model governance.
Team collaboration features allow multiple data scientists to share experiments, compare runs across team members, and annotate results — critical for large ML teams where parallel experimentation is the norm.
Comet ML runs as ml platform software built around data and code workflows. Users typically start with a prompt, upload, or connected data source, and the underlying model handles the heavy lifting before returning a result you can refine or export. It's available on python, r, and java, with API access for teams that want to embed it into their own products.
The capabilities that matter most for teams evaluating Comet ML.
Captures hyperparameters, metrics, code snapshots, and artefacts from ML training with minimal instrumentation using auto-logging for common frameworks.
Prompt management, LLM output evaluation (hallucination, relevance), and production monitoring for LLM applications extending beyond classical ML tracking.
Manages model lifecycle from experiment to production with artefact versioning, deployment status tracking, and model governance in a single interface.
Free plan (1 user, limited storage). Team $179/month. Enterprise custom.
Model
Freemium
Starting price
$179/mo
Free trial
No
Weights & Biases (covered) is the primary competitor with stronger community. ClearML (covered, rank 437) is the open source alternative. MLflow (covered later) is the Apache open source option. Langfuse (covered) focuses specifically on LLM observability.
A side-by-side look at the closest alternative in this category.
Key facts about model providers, platforms, and team support.
Model Provider
Comet ML
Platforms
Python, R, Java, CLI
Deployment
SaaS, On-premise, Self-hosted
Integrations
PyTorch, TensorFlow, Keras, Hugging Face, scikit-learn, Jupyter, GitHub, API
Team Collaboration
Yes
Launch Year
2022
Compliance signals and data-handling notes as reported by the vendor.
SOC 2 Type II. ISO 27001. GDPR compliant. On-premise deployment option for data privacy. Enterprise data handling agreements.
Review Comet ML's data handling policy. Experiment data, metrics, and artefacts processed on Comet's infrastructure. On-premise deployment keeps data on customer infrastructure.
Editorial Verdict
Comet ML is a solid ML experiment tracking platform for teams wanting a W&B alternative with on-premise deployment option and expanding LLM evaluation capabilities.
Last verified July 24, 2026.
Free plan (1 user, limited storage). Team $179/month. Enterprise custom.
Open source self-hosted is free. ClearML Cloud managed from $13/month. Enterprise custom.
SOC 2 Type II. ISO 27001. GDPR compliant. On-premise deployment option for data privacy. Enterprise data handling agreements.
Open source self-hosted keeps all data on customer infrastructure. ClearML Cloud subject to ClearML's data handling policy. Enterprise includes data handling agreements.
Review Comet ML's data handling policy. Experiment data, metrics, and artefacts processed on Comet's infrastructure. On-premise deployment keeps data on customer infrastructure.
Self-hosted deployment keeps all ML experiment data on customer infrastructure. ClearML Cloud transmits experiment data to ClearML's servers — review privacy policy.
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