Comet.ml
MLOps platforms
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$19 per user per month
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What is Comet.ml
Comet.ml is an MLOps platform focused on experiment tracking, model development observability, and collaboration for machine learning teams. It is used by data scientists and ML engineers to log parameters, metrics, artifacts, and code context across training runs and to compare and reproduce experiments. The product supports integrations with common ML frameworks and provides a hosted service as well as deployment options for organizations with stricter data and network requirements.
Strong experiment tracking core
Comet.ml provides structured logging for hyperparameters, metrics, artifacts, and environment details to support reproducibility. It offers run comparison and visualization features that help teams diagnose model changes over time. This focus makes it well-suited for teams that need a dedicated system of record for experiments rather than a broader data platform.
Broad ML framework integrations
Comet.ml integrates with popular Python ML libraries and training workflows, including notebook-based development. It supports programmatic logging via SDKs and can capture code and metadata to reduce manual documentation. These integrations lower the effort to standardize tracking across multiple projects and teams.
Collaboration and governance features
Comet.ml provides shared workspaces/projects to organize experiments and artifacts across teams. It supports access controls and team-level organization features that help centralize ML work. This can improve cross-functional visibility compared with ad hoc tracking in files, spreadsheets, or isolated notebooks.
Not a full ML platform
Comet.ml centers on experiment tracking and related observability rather than end-to-end data preparation, feature engineering, and large-scale compute management. Organizations may still need separate tools for data pipelines, labeling, and model serving. Buyers looking for an all-in-one environment may find the scope narrower than broader analytics and AI platforms.
Value depends on adoption discipline
Teams must consistently instrument training code and standardize logging practices to get reliable comparisons and auditability. Without agreed conventions (naming, tagging, artifact management), workspaces can become difficult to navigate. This creates an enablement and governance requirement beyond simply deploying the software.
Deployment and compliance evaluation needed
Enterprises with strict security, residency, or regulated-data requirements need to validate available deployment models and controls against internal policies. Integration with existing identity, network, and audit tooling may require additional configuration work. Procurement often involves reviewing data retention, access logging, and encryption options in detail.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Free | $0 (Free plan) | 1 platform user; core experiment tracking (track/compare training runs), dataset management & versioning, Model Registry; fair usage limits (100GB data listed in plan table); community support. |
| Pro | $19 per user/month | Up to 10 users; 1,500 training hours included; 500GB storage included; email support; includes everything in Free plan; Start Free Trial available. |
| Enterprise | Custom pricing (contact sales) | Unlimited users; unlimited training hours; flexible deployments (on-prem/VPC/managed); model production monitoring; service accounts & view-only users; single sign-on (SSO); dedicated support & SLAs; SOC 2, ISO 27001, ISO 9001, HIPAA, GDPR compliance. |
Seller details
Comet ML, Inc.
New York, NY, USA
2017
Private
https://www.comet.com/
https://x.com/cometml
https://www.linkedin.com/company/comet-ml/
