What I like most about Comet.ml is how easy it is to track experiments and how clearly it visualizes model performance. It makes it straightforward to compare runs, monitor metrics, keep results organized, and collaborate with team members. Overall, it helps make my machine learning workflow more efficient and reproducible. Review collected by and hosted on G2.com.

What do you dislike about Comet.ml?

The main thing I dislike is that Comet.ml can have a bit of a learning curve for new users. Some of the more advanced features and dashboard configurations can feel overly complex, especially when you’re managing a large number of experiments. A simpler interface, along with more customization options, would make the overall experience better. Review collected by and hosted on G2.com.

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

Comet.ml helps solve the challenge of managing and tracking machine learning experiments, including parameters, metrics, datasets, and model versions. It lets me compare experiments more easily, reproduce successful results, and collaborate better with others. Overall, it saves time, reduces the need for manual tracking, and makes the ML development process more organized and efficient. Review collected by and hosted on G2.com.

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.

Anil B.

Fresher

Small-Business (50 or fewer emp.)

8/8/2026

"Simple, All-in-One Machine Learning Experiment Tracking with Comet."

4.5/5

What do you like best about Comet.ml?

The main thing that I like in Comet.ml is that it is really simple to track the results of my machine learning experiments. With Comet.ml, I can compare different models, track metrics, save the parameters, and organize the results of the experiments all in one place. The dashboards are useful for evaluating the performance of the models. Review collected by and hosted on G2.com.

What do you dislike about Comet.ml?

The reason why I don’t like the platform of Comet.ml is that there are some complex features which need some time to grasp. Besides, the interface might be quite complicated when dealing with several experiments and the need to configure some functions. I believe that the customization options of the reporting and dashboard can be more versatile. Review collected by and hosted on G2.com.

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

Comet.ml addresses the issue of having to do all of the experiment management and comparisons manually. It stores all the experiment parameters, metrics, models, and results together in one place. This allows me to keep track of my progress, figure out which models work better, and replicate experiments more efficiently. Review collected by and hosted on G2.com.

Muhammed A.

Technical Project Manager

Logistics and Supply Chain

Small-Business (50 or fewer emp.)

7/31/2026

"Comet ML Makes Experiment Tracking and Collaboration Effortless"

4.5/5

What do you like best about Comet.ml?

Keeping machine learning experiments organized becomes much easier with Comet ML. It provides clear visualizations for metrics, reliable experiment tracking, artifact management, and collaboration features that fit naturally into existing ML workflows. Comparing model iterations is straightforward, integrations with popular frameworks work smoothly, and the platform helps accelerate model development while improving reproducibility and team productivity. Review collected by and hosted on G2.com.

What do you dislike about Comet.ml?

Getting the most out of Comet ML requires some initial setup, especially when configuring advanced dashboards and collaborative workflows. As experiment histories grow, navigating large numbers of runs can become less convenient without additional filtering options. More flexible reporting, deeper customization of visualizations, and lower pricing for smaller teams would make the platform even more appealing. Review collected by and hosted on G2.com.

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

Managing machine learning projects across multiple experiments used to involve spreadsheets, scattered logs, and manual tracking of model versions. Comet ML brings all of that into one centralized platform, making it easy to monitor training progress, compare results, and reproduce successful runs. The result has been faster experimentation, fewer mistakes when evaluating models, and a more efficient development process that allows the team to focus on improving model performance instead of managing experiment records. Review collected by and hosted on G2.com.

Muhammad O.

Salesforce Business Analyst

Information Technology and Services

Small-Business (50 or fewer emp.)

7/30/2026

"Simple and Reliable AI Experiment Tracking"

4/5

What do you like best about Comet.ml?

What I like most about Comet.ml is how clearly it shows what’s happening behind the scenes in AI and LLM workflows. The interface feels well organized, and it’s easy to navigate through logs, traces, and experiment data all in one place. I also appreciate that it supports different AI frameworks, which makes debugging and monitoring a lot more manageable overall. Review collected by and hosted on G2.com.

What do you dislike about Comet.ml?

The biggest drawback for me is the learning curve when using it for the first time. Some of the observability and evaluation features can feel a bit advanced if you’re just getting started, which makes the initial setup and exploration less intuitive than it could be. A few guided tutorials or simpler onboarding examples would go a long way toward making the first experience smoother. Review collected by and hosted on G2.com.

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

Comet.ml makes AI and LLM development easier by providing clearer visibility into experiments, logs, and overall model behavior. Rather than spending time manually tracking down issues, I can quickly see what happened during a run and pinpoint the areas that need improvement. It saves me time and keeps debugging and monitoring far more organized and consistent. Review collected by and hosted on G2.com.

Mrinal A.

Full-stack Developer

Enterprise (> 1000 emp.)

10/12/2026

"Promising Open-Source LLM Tracing, but Hosted Dependency and Python-Centric"

3.5/5

What do you like best about Comet.ml?

Comet is the newest open-source tool for tracing and evaluating ML LLM applications, reflecting the shift toward LLM work. Check their site for the current feature set, as this part is evolving very quickly. The main thing about this platform is that it is 3-tier for individuals and academics, which suits students building portfolios. Review collected by and hosted on G2.com.

What do you dislike about Comet.ml?

The most hated part is that it has a vendor dependency because the full-fledged product is hosted as SaaS, so your experiment history lives on their platform, and self-hosting is typically an enterprise feature. It is Python-centric and supports other languages less well. Review collected by and hosted on G2.com.

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

When you work on an LLM model, the results will be scattered across notebooks, terminal output, and spreadsheets, causing much confusion. But when we started using Comet, most of the time, whenever we ran our LLM models, it recorded every single output in the same format. Comet can put them side by side, allowing you to sort by accuracy, overlay loss curves, and hyperparameter changes. Review collected by and hosted on G2.com.

Ritweeja A.

technical project co-ordinate

Enterprise (> 1000 emp.)

10/12/2026

"Fast, Efficient Metric Streaming—But SDK and UI Features Don’t Always Match"

3.5/5

What do you like best about Comet.ml?

The most liked part about this platform is instead of forcing HTTP requests every time a metric is calculated, which would cripple the GPU training performance, the SDK pushes raw metrics into an in-memory queue. It automatically batches the payload and flushes them to the Comet server over a persistent WebSocket connection. Review collected by and hosted on G2.com.

What do you dislike about Comet.ml?

The most disliked part about this platform is it has advanced features, a common frustration among ML engineers is the occasional drift between what can be done by the Python SDK versus what functions in the browser UI. Review collected by and hosted on G2.com.

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

A training job run for 14 hours on a cloud GPU cluster and suddenly crashes and underperforms. Without live monitoring, the engineer building that ML model has no idea if it ran out of VRAM, overheated, or suffered from exploding gradients. Here, Comet comes in and streams log real-time hardware metrics alongside training metrics. If a model fails, you can see the exact moment the hardware or math broke down. Review collected by and hosted on G2.com.

Nishu K.

Cloud Consultant

Enterprise (> 1000 emp.)

10/13/2026

"Strong ML Team Collaboration, But Limited Support Beyond Python"

3.5/5

What do you like best about Comet.ml?

The platform is best for when we are working in a team on a ML Model. It has a good collaboration features, reports and panels which can be share among the team members. Free tier for individuals and academics person which suits students build portfolio. Review collected by and hosted on G2.com.

What do you dislike about Comet.ml?

It is mainly focus on Python, it supports other languages very less. It track and monitor the ML Models but does not run your pipelines like Cubeflow or airflow and the team and enterprise addition of this platform is very expensive as we compared to open source options. Review collected by and hosted on G2.com.

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

The machine learning work is experimental and messy, you try dozens of code to run with different hyperparameter dataset codeversion and architecture but after we use format we get to know that it is an experimental tracking and ML Lifecycle program that records everything about each run so the results are comparable, sharable and reproducible. Review collected by and hosted on G2.com.

Harsh S.

SDE

Enterprise (> 1000 emp.)

10/8/2026

"Easy Python ML Setup, but Limited Language Support and Can Get Pricey"

3/5

What do you like best about Comet.ml?

It is very easy to setup you can setup it in very few lines of code and you can autolog in a minute it work across many python ml libraries like skitlearn and the code environment and parameter are saved automatically saved after one run of ml model. Review collected by and hosted on G2.com.

What do you dislike about Comet.ml?

It is mainly python centric it supports less other languages like ruby kotlin and java and the cost of team and enterprise edition camn get very expensive as compared to any open source option like github. Review collected by and hosted on G2.com.

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

Building an ml in this day to day life is not same as writing normal software, the same code give same output but in ml the result depends upon the code , data which you upload what setting you setup some randomness and depends upon which environment you are working and you can change all of these constantly while experimenting the ai but when we use this comet ml platform, it make a proper environment for a team so it has same data setting in an environment which improves the quality of ml model as compared to a normal software Review collected by and hosted on G2.com.

Abhyansh S.

Software Developer

Enterprise (> 1000 emp.)

10/7/2026

"Great for Team LLM Collaboration, but Pricing and Complexity Can Be Tough"

3.5/5

What do you like best about Comet.ml?

The best part about this is that when we work on a model, several teammates can collaborate on it, mostly on local systems. This often causes duplication issues when some members work on the same thing without knowing others are also working on it. However, using Comet ML, this issue can be easily resolved as it helps show which team member is working on which area/part of the LLM and how much is complete, thus saving the team's time in developing an LLM. Review collected by and hosted on G2.com.

What do you dislike about Comet.ml?

The worst part I know is that it has high usage charges and advanced observation and evaluation, which sometimes feel very complex. It is difficult to use for someone using it for the first time, as it takes time to learn. Review collected by and hosted on G2.com.

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

Traditional software version control tools like GitHub work for text-based code but fail to handle the complex, heavy data, and iterative nature of machine learning. However, Comet ML acts as GitHub for machine learning models, specifically designed for machine learning models, and has everything needed to create ML models. Review collected by and hosted on G2.com.