We and our customers use Comet's audit trail feature. Whenever any of the browsing capabilities has been completed, we typically take out a kind of weekly report and monthly report and do the auditing of what are the different services that the client looks out for on our platform and how they are reaching out to us. We check what the browsing capabilities of the different searches are that the persons are looking for who are coming onto our platform. Our auditing has been working fine with Comet as well. We are not using Comet's visualization tools. We have our own dashboarding tool where all the audits, logs, all the revenue, sales, ROI, and anything has been maintained and we are plotting the graphs there. How effective the performance is, how the latency is issued, what kind of time constraints it is giving, what the browsing capabilities are, how faster the browsing capabilities are coming into picture, throughput of the scenarios and most importantly, scalability are the metrics I track using Comet's experiment management interface. We are looking to reach out to multiple integrations and that is where we need the scalability options to be very important. It took a couple of days to understand the repository of the platform, how it works, and how if I give accesses to different team members, how much time it usually takes to learn and then start implementing the solutions. I stand as an implementer of the product. I have provided this review with an overall rating of eight out of ten.
ML Engineer at a energy/utilities company with 51-200 employees
Real User
Top 5
May 17, 2026
My advice for others looking into using Comet would be to evaluate the scale and level that their organization operates at. If a team is running occasional ML experiments with a smaller number of researchers, lightweight tracking tools may be sufficient. However, for organizations managing multiple models and datasets, Comet provides a great load of benefits for them. The platform is very valuable when reproducibility, centralized visibility, and experiment comparison become important priorities. For AI-focused organizations or ML teams starting to scale, I would definitely recommend Comet. Comet is a very valuable platform when it comes to reproducibility, collaboration, experiment tracking, and visibility. Even though there is a slight initial learning curve for teams trying to use Comet, once you are familiar with it and once your workflows and integrations are sorted, Comet becomes a very powerful platform for managing all your ML experimentation. I believe this review is overall quite good and would help anyone understand whether Comet is built for their team or if they would require it. I give this review an overall rating of eight out of ten.
On a scale of 1 to 10, I would rate Comet overall a nine. I chose nine out of 10 because so far I haven't run into any issues regarding the Comet interface, and I've been able to do my job well using it. My experience with pricing, setup cost, and licensing wasn't something that I experienced directly, as it was done through the company. They had given me the laptop, and it already had access, so I just needed to confirm it with someone. I believe the company has a license, and you just have to message the licensing team to have access. Comet is deployed in my organization as I believe on-premises, so only the laptops associated with the company can use Comet. My overall review rating for Comet is 9 out of 10.
Software Engineer at a marketing services firm with 1-10 employees
Real User
Top 5
Apr 11, 2026
I choose a rating of six out of ten for Comet because it is not fully developed. I recognize it might be the first release and the first version of what they are building, so I expect more improvements in the future. I recommend Comet to those who are learning, conducting research, or are college students and university graduates who want to read through lengthy articles. My overall rating for this product is six out of ten.
I have not found the voice mode useful so far, and I have not used the summarizing feature because I am usually busy with other tasks. That said, I think everything is fine and Comet needs to keep improving to benefit everyone. I wish Comet could evolve into a more personalized agent where you either pay little or nothing to be more productive, especially considering the competitive AI landscape. Generally, my projects do not depend on the internet; they rely on my personal skills using specific software on my computer, so I cannot quantify metrics such as projects completed quicker due to using Comet. However, when I do use the internet, I get information faster with Comet, which may not translate into specific monetary gains, but the time saved is significant, as time is money. I recommend giving Comet a try, using it to fill tabs and asking questions. Utilize the assistant smartly to save time by having answers provided by a group of AIs rather than solely relying on Google. Try to integrate your work with Comet to improve efficiency and your company's bottom line. I rated this product an eight out of ten.
I would definitely advise new users to take advantage of Comet's experiment tracking, run comparison, and dashboards from the beginning. Make sure to tag and note runs consistently. This will save time later and help your team get the most value. Additionally, explore the collaboration and insights features. They can help speed up analysis and decision-making. I would rate my overall experience with this product as an 8 out of 10.
Comet offers powerful capabilities for tracking, comparing, and optimizing machine learning models, making it a valuable tool for data-driven enterprises aiming to improve project outcomes. Designed with efficiency in mind, Comet enhances experiment tracking and model management. It supports diverse machine learning workflows helping teams streamline model development and iteration. Integration with popular ML libraries provides seamless tracking and enhances model reproducibility. Valuable...
We and our customers use Comet's audit trail feature. Whenever any of the browsing capabilities has been completed, we typically take out a kind of weekly report and monthly report and do the auditing of what are the different services that the client looks out for on our platform and how they are reaching out to us. We check what the browsing capabilities of the different searches are that the persons are looking for who are coming onto our platform. Our auditing has been working fine with Comet as well. We are not using Comet's visualization tools. We have our own dashboarding tool where all the audits, logs, all the revenue, sales, ROI, and anything has been maintained and we are plotting the graphs there. How effective the performance is, how the latency is issued, what kind of time constraints it is giving, what the browsing capabilities are, how faster the browsing capabilities are coming into picture, throughput of the scenarios and most importantly, scalability are the metrics I track using Comet's experiment management interface. We are looking to reach out to multiple integrations and that is where we need the scalability options to be very important. It took a couple of days to understand the repository of the platform, how it works, and how if I give accesses to different team members, how much time it usually takes to learn and then start implementing the solutions. I stand as an implementer of the product. I have provided this review with an overall rating of eight out of ten.
My advice for others looking into using Comet would be to evaluate the scale and level that their organization operates at. If a team is running occasional ML experiments with a smaller number of researchers, lightweight tracking tools may be sufficient. However, for organizations managing multiple models and datasets, Comet provides a great load of benefits for them. The platform is very valuable when reproducibility, centralized visibility, and experiment comparison become important priorities. For AI-focused organizations or ML teams starting to scale, I would definitely recommend Comet. Comet is a very valuable platform when it comes to reproducibility, collaboration, experiment tracking, and visibility. Even though there is a slight initial learning curve for teams trying to use Comet, once you are familiar with it and once your workflows and integrations are sorted, Comet becomes a very powerful platform for managing all your ML experimentation. I believe this review is overall quite good and would help anyone understand whether Comet is built for their team or if they would require it. I give this review an overall rating of eight out of ten.
On a scale of 1 to 10, I would rate Comet overall a nine. I chose nine out of 10 because so far I haven't run into any issues regarding the Comet interface, and I've been able to do my job well using it. My experience with pricing, setup cost, and licensing wasn't something that I experienced directly, as it was done through the company. They had given me the laptop, and it already had access, so I just needed to confirm it with someone. I believe the company has a license, and you just have to message the licensing team to have access. Comet is deployed in my organization as I believe on-premises, so only the laptops associated with the company can use Comet. My overall review rating for Comet is 9 out of 10.
I choose a rating of six out of ten for Comet because it is not fully developed. I recognize it might be the first release and the first version of what they are building, so I expect more improvements in the future. I recommend Comet to those who are learning, conducting research, or are college students and university graduates who want to read through lengthy articles. My overall rating for this product is six out of ten.
I have not found the voice mode useful so far, and I have not used the summarizing feature because I am usually busy with other tasks. That said, I think everything is fine and Comet needs to keep improving to benefit everyone. I wish Comet could evolve into a more personalized agent where you either pay little or nothing to be more productive, especially considering the competitive AI landscape. Generally, my projects do not depend on the internet; they rely on my personal skills using specific software on my computer, so I cannot quantify metrics such as projects completed quicker due to using Comet. However, when I do use the internet, I get information faster with Comet, which may not translate into specific monetary gains, but the time saved is significant, as time is money. I recommend giving Comet a try, using it to fill tabs and asking questions. Utilize the assistant smartly to save time by having answers provided by a group of AIs rather than solely relying on Google. Try to integrate your work with Comet to improve efficiency and your company's bottom line. I rated this product an eight out of ten.
I would definitely advise new users to take advantage of Comet's experiment tracking, run comparison, and dashboards from the beginning. Make sure to tag and note runs consistently. This will save time later and help your team get the most value. Additionally, explore the collaboration and insights features. They can help speed up analysis and decision-making. I would rate my overall experience with this product as an 8 out of 10.