Comet
Experiment tracking evaluation and AI observability for ML teams, available as free cloud or self hosted OSS with enterprise options for secure collaboration.
Overview
Comet helps ML and AI teams track experiments, datasets, and models, then evaluate and monitor them in one place. Developers log metrics and artifacts from notebooks or training jobs with minimal code, visualize runs across parameters, and compare candidates for promotion. Evaluation tooling adds dataset versioning, prompts, and human feedback loops for LLM apps, while observability catches regressions and drift after deployment.
Teams organize projects, use role controls, and share dashboards that explain choices to stakeholders. Comet offers a free cloud and open source options for local control, with paid plans for SSO, private networking, and advanced governance. Integrations cover PyTorch, TensorFlow, Hugging Face, Ray, and Kubernetes.
With repeatable pipelines and searchable history, engineering and research stay aligned from ideation to production while audits remain straightforward.
Key features
- One line logging: Add a few lines to notebooks or jobs to record metrics params and artifacts for side by side comparisons and reproducibility
- Evals for LLM apps: Define datasets prompts and rubrics to score quality with human in the loop review and golden sets for regression checks
- Observability after deploy: Track live metrics drift and failures then alert owners and roll back or retrain with evidence captured for audits
- Governance and privacy: Use roles projects and private networking to meet policy while enabling collaboration across research and product
- Open and flexible: Choose free cloud or self hosted OSS with APIs and SDKs that plug into common stacks without heavy migration
- Dashboards for stakeholders: Build views that explain model choices risks and tradeoffs so leadership can approve promotions confidently
Best for
- Hyperparameter sweeps: Compare runs and pick winners with clear charts and artifact diffs for reproducible results
- Prompt and RAG evaluation: Score generations against references and human rubrics to improve assistant quality across releases
- Model registry workflows: Track versions lineage and approvals so shipping teams know what passed checks and why
- Drift detection: Monitor production data and performance so owners catch shifts and trigger retraining before user impact
- Collaborative research: Share projects and notes so scientists and engineers align on goals and evidence during sprints
- Compliance support: Maintain histories and approvals to satisfy audits and customer reviews with minimal manual work
- Education and labs: Teach best practices using free cloud or OSS while students learn to compare experiments at scale
- Kubernetes friendly: Integrate with schedulers and clusters to capture metrics from distributed training runs
Capabilities
Experiments and Artifacts
Capture metrics params and files from code then visualize and compare runs to pick the best candidate confidently.
Prompts and Rubrics
Run systematic evaluations on LLM outputs using datasets references and human ratings to guide releases.
Production Drift
Watch performance and data quality in production then alert owners with evidence to retrain or roll back.
Roles and Private Networking
Apply SSO roles and private connectivity so teams collaborate under security and compliance requirements.
Frequently Asked Questions
How does pricing start?
Comet provides a free cloud and open source options, enterprise plans add SSO private networking and governance features, contact sales for a quote.
Is there an on prem option?
Yes, self hosted OSS and private networking options exist for regulated environments and customers with strict data rules.
Which frameworks integrate?
Comet supports PyTorch TensorFlow Hugging Face Ray scikit learn and more with light instrumentation.
Can we evaluate LLM apps?
Yes, eval tooling supports prompts datasets references and human feedback loops to guide releases.
Do you support dashboards?
Teams can build dashboards that explain metrics and tradeoffs to leadership and auditors.
