Comet

MLOps platform for experiment tracking, model registry, and production monitoring

Hand-reviewed by the Toolglade team·Updated Aug 2026

mlops#experiment-tracking#mlops#model-registry#monitoring

Free plan Free trial Claimed API Self-hosted Teams

Toolglade’s take

Comet is a solid, mature choice for teams that want experiment tracking and a model registry without adopting a heavier end-to-end platform. Its free tier is genuinely useful for individuals, and per-seat Pro pricing is straightforward. It competes in a crowded space against other tracking tools, so evaluate integrations and monitoring depth against your stack. Note that Comet's GenAI observability lives in a separate product (Opik); verify current plan limits and pricing with the vendor.

About Comet

Comet is an MLOps platform for experiment tracking, model registry, and production monitoring, letting ML teams log, compare, and reproduce runs with a free tier and per-seat paid plans.

Comet is a long-established MLOps platform that gives machine learning teams a system of record for their model development. Its core is experiment tracking: engineers instrument their training code to automatically log metrics, hyperparameters, source code, datasets, and artifacts, then use Comet's UI to visualize, compare, and reproduce experiments. This addresses one of the most persistent pain points in ML, keeping track of what was tried and why one model outperformed another. Beyond tracking, Comet provides a model registry to version and stage models toward production, along with production monitoring in its higher tiers to watch for drift and performance issues once models are deployed. It is framework-agnostic and integrates with common ML libraries, fitting into existing training pipelines with minimal changes. Comet offers a free MLOps tier for individuals and small projects with generous storage and fair-usage limits, a Pro plan around $19 per user per month with higher limits and up to ten users, and an enterprise tier with unlimited usage, advanced monitoring, flexible deployment (including self-managed options), SSO, and dedicated support. Comet also develops Opik, its separate open-source GenAI observability and evaluation product, but the core Comet platform remains focused on classic MLOps and experiment management.

TL;DR

Comet is a mature MLOps platform for experiment tracking, model registry, and production monitoring, helping ML teams log, compare, and reproduce model development.

Company overview

Comet develops an MLOps platform used by data science and ML teams to track experiments, manage models, and monitor production performance. It has built a reputation as a reliable system of record for model development.

Alongside its core platform, Comet develops Opik, a separate open-source GenAI observability and evaluation product, reflecting its expansion toward LLM tooling while keeping classic MLOps as its foundation.

Product features

Comet automatically logs metrics, hyperparameters, code, datasets, and artifacts from training runs, and provides a UI to visualize and compare experiments. It includes a model registry for versioning and staging and, in higher tiers, production monitoring for drift and performance.

It is framework-agnostic, integrating with PyTorch, TensorFlow, scikit-learn, and Hugging Face, and offers flexible deployment including self-managed enterprise installations.

Target market

Comet targets ML and data science teams, researchers, and enterprises that need experiment tracking, model management, and production monitoring across their model lifecycle.