G2

Arun R.

Assistant Manager/Product Security Lead Engineer

Small-Business (50 or fewer emp.)

8/26/2026

"Comet.ml Makes ML Experiment Tracking and Visualization Effortless"

4.5/5

What do you like best about Comet.ml?

What I like most about Comet is that it keeps the entire ML experimentation process organized in one place. It makes it easy to track experiments, compare different runs, and see how changes in parameters or code affect model performance. I also like the visualization and model versioning capabilities because they make it much easier to understand results and reproduce successful experiments. For LLM and AI projects, the Opik capabilities are also useful for tracing and evaluating model and agent behavior. Review collected by and hosted on G2.com.

What do you dislike about Comet.ml?

The main downside for me is that Comet has quite a lot of features, so it can take some time to understand the platform and decide which features are actually needed for a particular project. The interface can also feel a little overwhelming when managing a large number of experiments, metrics, and artifacts. A simpler onboarding experience and more streamlined navigation would make it easier for new users to get started. Review collected by and hosted on G2.com.

What problems is Comet.ml solving and how is that benefiting you?

Comet helps solve the problem of managing and reproducing machine learning experiments as projects become more complex. Instead of manually keeping track of parameters, metrics, code versions, datasets, and models, everything can be linked and tracked in one place. This makes it much easier to compare experiments, identify what worked, reproduce results, and collaborate with other team members. It also provides useful observability and evaluation capabilities for LLM and AI applications through Opik Review collected by and hosted on G2.com.