MLOps & experiment tracking · Comet ML, Inc.

Comet

Hosted experiment-tracking platform that also offers a separate LLM observability product (Opik).

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Comet is an experiment tracker: it logs metrics, hyperparameters, code, and artifacts from ML training runs into a searchable, comparable dashboard, putting it in the same bucket as MLflow, W&B, and neptune.ai rather than production model monitoring. A separate product line, Opik, extends Comet's offering into LLM application tracing, evaluation, and production monitoring for generative-AI systems — a distinct use case from classic experiment tracking. Comet is delivered as hosted SaaS, with self-hosted and hybrid deployment options for enterprise and regulated customers. It works with any Python ML framework and integrates with common training and pipeline tools. Free tiers exist for both the core MLOps platform and Opik, aimed at individuals and small teams, with paid per-seat plans adding higher usage limits, longer retention, and governance controls.

At a glance

Vendor Comet ML, Inc.
Pricing model Free tier + paid plans
Free tier Yes
Deployment Cloud, Self-hosted
Open source No
Best for Teams wanting one vendor for both classic experiment tracking and LLM/agent observability.

Pricing

Free tiers exist on both the core MLOps platform and the Opik LLM product; Pro plans bill per user per month with usage caps, and Enterprise is custom-quoted.

Plan Price Notes
Free (MLOps) $0 Fair usage policy; 100GB data storage; 1 team member
Pro (MLOps) $19/user/month 1,500 training hours included, 500GB storage, up to 10 users
Enterprise Custom Unlimited users and training hours, plus advanced production monitoring features

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Experiment tracking and comparison dashboards
  • Model production monitoring (via Opik)
  • Artifact and dataset versioning
  • Hyperparameter optimization
  • LLM tracing and evaluation (Opik)
  • Team collaboration and reporting
  • Self-hosted and hybrid deployment
  • Custom visualization panels

Integrations

Profile last reviewed September 21, 2026

Alternatives

MLflow Open-source experiment tracker and model lifecycle toolkit, governed by the Linux Foundation. Weights & Biases Hosted experiment tracker and AI evaluation platform for logging training runs, metrics, and model lineage. neptune.ai Experiment tracker built for high-throughput logging of large-scale and foundation-model training runs. ClearML Open-source experiment tracker that also bundles pipeline orchestration, dataset versioning, and model deployment.

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Terms to know

Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Data lineage A traceable record of where data came from, how it was transformed, and where it is used downstream. Hyperparameter tuning The process of finding the settings that control how a model learns, such as learning rate or tree depth, rather than what it learns. LLM observability Monitoring a language model's live behavior in production, tracking its inputs, outputs, latency, cost, and failure patterns. MLOps (machine learning operations) The set of practices for deploying, monitoring and maintaining machine learning models reliably in production, extending DevOps to ML systems. Model monitoring Ongoing tracking of a deployed model's inputs, predictions and outcomes to catch accuracy loss, drift or failures after launch.

Related guides

How to choose an MLOps & experiment tracking tool "MLOps" covers five different jobs — tracking, monitoring, orchestration, serving, versioning — and most teams need more than one, not the biggest single tool.