Sold by: Comet ML
Comet's machine learning platform integrates with your existing infrastructure and tools so you can reproduce, debug, manage, visualize, and optimize model - from training runs to production monitoring. Add two lines of code to your notebook or script and automatically start tracking code, hyperparameters, metrics, and more, so you can compare and reproduce training runs.
4.3
Overview
Comet's machine learning platform integrates with your existing infrastructure and tools so you can manage, visualize, and optimize model - from training runs to production monitoring.
Add two lines of code to your notebook or script and automatically start tracking code, hyperparameters, metrics, and more, so you can compare and reproduce training runs.
Comet helps ML teams: -Track and share training run results in real time. -Build their own tailored, interactive visualizations. -Track and version datasets and artifacts. -Manage their models and trigger deployments. -Monitor their models in production.
Comet's platform supports some of the world's most innovative enterprise teams deploying deep learning at scale and is used by ML teams at Uber, Zappos, Shopify, Affirm, Etsy, Ancestry.com and ML leaders across all industries.
For custom pricing, MSA, or a private contract, please contract AWS-Marketplace@comet.com for a private offer.
Highlights
- Track and share training run results in real time: Comet's ML platform gives you visibility into training runs and models so you can iterate faster.
- Manage your models and trigger deployments: Comet Model Registry allows you to keep track of your models ready for deployment. Thanks to the tight integration with Comet Experiment Management, you will have full lineage from training to production.
- Monitor your models in production: The performance of models deployed to production degrade over time, either due to drift or data quality. Use Comet's machine learning platform to identify drift and track accuracy metrics using baselines automatically pulled from training runs.
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Dimension | Description | Cost/12 months |
|---|---|---|
Advanced Package | Experiment Management, Model Registry, Monitoring | $4,500.00 |
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Non-Refundable. Unless otherwise expressly provided for in this agreement or the applicable Order Form, (i) all fees are based on services purchased and not on actual use; and (ii) all fees paid under this agreement are non-refundable.
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By Comet ML
By Fiddler AI
By LIT.AI
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Experiment Tracking and Management
Automatic tracking of code, hyperparameters, metrics, and training run data with capability to compare and reproduce training runs in real time.
Model Registry and Deployment Management
Model Registry functionality to track models ready for deployment with full lineage integration from training to production and deployment triggering capabilities.
Production Monitoring and Drift Detection
Production model monitoring with drift detection and accuracy metric tracking using baselines automatically pulled from training runs.
Dataset and Artifact Versioning
Tracking and versioning of datasets and artifacts throughout the machine learning lifecycle.
Custom Visualization and Interactive Dashboards
Capability to build tailored, interactive visualizations for analyzing and managing machine learning experiments and models.
Multi-Model Type Support
Supports monitoring and observability for tabular, deep learning, computer vision, natural language processing, and large language model deployments
Performance and Drift Detection
Identifies and mitigates model performance degradation, data drift, data integrity issues, hallucination, accuracy, safety, and security issues in production deployments
Root Cause Analysis and Diagnostics
Provides powerful root cause analysis and diagnostic capabilities with 3D UMAP visualization for macro-level trend analysis and micro-level issue identification
Enterprise Security and Access Control
Implements SOC2 Type 2 security compliance and role-based access control (RBAC) for level-specific user permissions across protected environments
Customizable Analytics and Metrics
Offers customizable dashboards, reports, and custom metrics to track model performance aligned with business KPIs and enable data-driven decision-making
Data Pipeline Management
Streamlines AI lifecycle with reproducible data builds, featuring sharding and dynamic resource optimization, with data contamination prevention and lookahead error correction
Feature Store
Enhances data reusability and ensures consistency across builds with optimized data structure for fast random access
Model Development and Experimentation
Supports deep learning with custom reusable components, automatic dimensionality transformations, hyperparameter tuning, model evaluation, and experiment tracking
Model Registry and Governance
Provides full traceability of models with security measures and prevents accidental deletions
Multi-Environment Deployment
Enables one-click deployment across versatile environments including cloud, on-premises, and edge computing
Standard contract
No
Customer reviews
reviewer2827170
Organizing research experiments has improved and supports faster model comparison and learning
Reviewed on Jun 01, 2026
Review provided by PeerSpot
What is our primary use case?
I mainly use Comet for research topics, summarizing information, and understanding difficult concepts. I use it for organizing and tracking my work on academic projects. It helps me keep track of experiments, compare results, manage data, and document my progress in one place. As a student, this makes it much easier to stay organized, analyze outcomes, and collaborate with classmates when working on research or machine learning projects.
Recently, I used Comet while working on a machine learning project that predicts student academic performance based on study habits and attendance data. I tracked different model runs, recorded parameters and results, and compared performance metrics such as accuracy and precision. Using Comet made it much easier to identify which model performed best and keep all my experiment details organized throughout the project.
What is most valuable?
Comet helps me maintain a clear record of my work, which is especially valuable in balancing multiple assignments and projects. Instead of manually tracking results in different files, I can keep experiments, metrics, and notes organized in one place. This improves reproducibility, makes it easier to revisit previous work, and saves time when preparing reports or presentations.
The features that stand out most to me are experiment tracking, performance visualization, and project organization. Experiment tracking makes it easier to compare different models, runs, and understand what changes led to better results. The visualization tools help me quickly analyze metrics and spot trends without having to create charts manually. I also appreciate how Comet keeps datasets, code versions, notes, and results organized in one place, which makes managing projects much more efficient.
The feature I rely on the most is experiment tracking. When I am testing different models or configurations, it is incredibly helpful to have all the parameters, metrics, and results automatically logged and organized. It saves me from manually documenting everything and makes comparisons much easier. As for specific tools, I use the experiment comparison dashboard all the time. Being able to view multiple runs side-by-side and quickly compare metrics such as accuracy, loss, and validation performance helps me make decisions much faster.
Comet does an excellent job of bringing different parts of the workflow together in one platform. Instead of switching between spreadsheets, notebooks, and separate tracking tools, I can see experiment metrics, visualizations, and notes in a single place. This not only saves time but also makes collaboration and project reviews much easier.
What needs improvement?
My experience with Comet has been very positive, but there are a few areas where it could be improved. One area is the learning curve for new users. Some of the more advanced features can feel overwhelming at first, especially for students who are new to machine learning experiment tracking. More beginner-friendly tutorials and guided onboarding would help. I would also like to see more customization options for dashboards and visualizations, making it easier to create views tailored to specific projects. Another improvement would be deeper integration with commonly used collaboration tools, which would streamline project documentation and team workflows.
There are a few additional areas where Comet could improve. From a performance perspective, I occasionally notice that dashboards with a large number of experiments can take longer to load or navigate. Regarding documentation, while the available resources are helpful, I would appreciate more beginner-focused examples, step-by-step tutorials, and real-world use cases. For support, my experience has generally been good, but having more community resources, discussion forums, webinars, or educational content specifically aimed at students and researchers would be valuable.
For how long have I used the solution?
I have been using Comet for approximately eight months.
What do I think about the stability of the solution?
Comet has been generally stable and reliable.
What do I think about the scalability of the solution?
In my experiments, Comet has handled scalability reasonably well for the types of projects I work on. For moderate increases in workload, such as more hyperparameter sweeps or additional experiment runs, it still performs well and keeps the data organized in a way that is easy to navigate and compare. That said, when the number of experiments grows significantly, I have noticed that loading dashboards and browsing through large experiment histories can become slower. It is not a blocker, but it does highlight that performance can vary depending on project size. Overall, I would say Comet scales very well for academic to mid-sized machine learning projects, and it remains usable.
How are customer service and support?
Customer support is pretty good, but I have not had a chance to directly reach out to them because I was able to troubleshoot all the issues with the online discussion forums. However, I heard from my colleagues and friends that customer support is actually good.
Which solution did I use previously and why did I switch?
I mainly relied on a combination of manual tracking methods, such as Jupyter notebooks, Excel, or Google Sheets. I switched to Comet because it brought all of these pieces together into a single platform. The main reason for the switch was efficiency and reproducibility.
Before choosing Comet, I explored TensorBoard, Weights & Biases, and setup using Jupyter notebook spreadsheets, which is what I initially started with. I did not do a formal head-to-head evaluation, but I explored them enough to understand their workflows. I chose Comet because I felt it had a good balance of ease of use and clean visualization tools without being too complex for my projects.
What was our ROI?
I do not calculate ROI in financial terms, but I have seen it in terms of time saved, productivity, and experiment efficiency. I estimate I spend around thirty to forty percent less time organizing and comparing experiment results compared to manual tracking. Project iteration cycles are faster, and I complete research projects more efficiently. In terms of qualitative ROI, the biggest benefit is improved workflow structure and reproducibility.
What other advice do I have?
Most of the major improvements I would like to see have already been covered, but one would be enhanced collaboration features.
I would suggest setting up Comet properly from the start and using it consistently for every experiment, even small ones. I also recommend taking time early on to learn how experiment tracking, metrics logging, and comparison views work because those are the features that provide the most value once you are actually iterating on models. Another recommendation is to keep experiments well-organized with clear naming conventions and tags.
I would rate my overall experience with Comet an 8 out of 10.
Semiconductors
Fascinating AI Agent Visualization That Brings Clarity to Debugging
Reviewed on May 19, 2026
Review provided by G2
What do you like best about the product?
The way Comet.ml visualizes the agents’ thought process is fascinating. Debugging AI agents has felt like a black box for quite a while, and Comet is helping me navigate that space with much more clarity.
What do you dislike about the product?
I think the UI/UX could be improved a bit. The intuitiveness and the availability of quick-use buttons could be better. Also, the attempt to make it look like a GitHub interface is kind of unpleasing, although it’s still okay to work with.
What problems is the product solving and how is that benefiting you?
Debugging of the AI agents, fixing them much earlier, sandboxing environments making it easier to test.
reviewer2818368
Centralized experiment tracking has improved reproducibility and collaboration across teams
Reviewed on May 17, 2026
Review from a verified AWS customer
What is our primary use case?
My main use case for Comet is experiment tracking and model lifecycle management. Comet has been a very helpful tool in our machine learning workflows. It has helped us improve reproducibility, collaboration, and visibility across all the AI projects that we manage. My primary use case is experiment tracking and machine learning.
Initially, we needed Comet as a centralized platform because we required a centralized platform that could track experiments and improve collaboration between the ML engineers and the data scientists. Comet has allowed us to consolidate experiment tracking and visualization into a single platform, making our workflow much more organized and reproducible.
Comet allowed us to consolidate experiment management, model evaluation, and visualization, everything into a single platform, which made our ML workflows much more organized and reproducible.
What is most valuable?
The most important use case of Comet would be the centralized experiment tracking. Every training run, metric, hyperparameter configuration, and model outputs are logged automatically, which makes it much easier to compare experiments and identify what is improving model performance.
The most important feature that Comet offers would be the reproducibility. Previously, we had to reproduce old experiments by ourselves, which was difficult because configuration metrics and everything else was scattered across notebooks and local systems. When we introduced Comet into our systems, all our experiments are stored in a single place, which greatly simplifies debugging and retraining workflows. Visualization is another feature that provides clear dashboards for tracking and resource utilization.
Visibility is the main benefit of Comet that has helped us create dashboards for tracking multiple models across various domains. Training curves, validation metrics, and resource utilization at different levels are all visible. This visibility has made it easier for us to understand where we are getting overfitting or where we are facing bottlenecks. Collaboration is also improved. Engineers can sit down and share findings within a single environment instead of relying on spreadsheets and multiple disconnected notebooks.
Comet has good integration capabilities with popular ML frameworks, and the integration is very strong. While using some customized pipelines, we need to have some manual configuration, and some effort is needed in that area. Apart from that, Comet is a very capable platform for ML lifecycle management.
What needs improvement?
Comet is a very powerful tool for experiment tracking and MLOps workflows, but the platform is somewhat complex for teams that are not initially familiar with the structured practices that have to be followed in MLOps. Understanding experiment organization, integrations, and tracking workflows requires some onboarding.
Pricing is one of the major challenges that Comet is facing. As our organization has increased and many users and experiment tracking requirements have increased, the platform cost can increase very quickly. The platform delivers very strong value when the users have increased or experiment tracking has increased extensively. However, as the ML workload increases, the cost also increases very quickly. Smaller teams running a limited number of ML experiments may not be able to fully utilize its capabilities as a whole.
Comet has good integration capabilities with popular ML frameworks, and the integration is very strong. While using some customized pipelines, we need to have some manual configuration, and some effort is needed in that area. The slight learning curve for teams that are unfamiliar with structured MLOps practices could have some improvement in that area. Some integrations with customized pipelines still require a lot of manual effort, which is one area that Comet could improve in.
Pricing initially seemed very high compared to other open-source experiment tracking tools. However, once we integrated the platform into our workflows, the productivity improvements justified the investment.
For how long have I used the solution?
I have been using Comet for around nine months.
What do I think about the stability of the solution?
Comet is very stable and easily scalable. Comet has been very stable in our experience, and with experiment logging, dashboard visualization, and model tracking workflows, it performs reliably even during large training workloads. We have not experienced any reliability issues affecting our ML operations. The performance platform handles scaling well as the number of experiments and users increases.
What do I think about the scalability of the solution?
The scalability of Comet is a very strong point for its use case. As we have scaled across multiple experiments, our models have increased by two to three folds. Comet is continuously able to organize runs efficiently and maintain visibility across projects, which becomes very important when we are scaling as an AI team.
Comet has been very stable in our experience, and with experiment logging, dashboard visualization, and model tracking workflows, it performs reliably even during large training workloads. We have not experienced any reliability issues affecting our ML operations. The performance platform handles scaling well as the number of experiments and users increases. The number of experiment models has increased drastically, but Comet has continued to organize runs efficiently and maintain visibility across multiple projects.
How are customer service and support?
Our overall experience with customer support has been mostly positive. Documentation has been quite detailed, and integration with PyTorch and TensorFlow are generally very straightforward. For advanced configurations, our support interactions were very responsive and technically helpful. I would rate the customer support a nine out of ten.
Which solution did I use previously and why did I switch?
Initially, we managed all our experiments manually using Jupyter notebooks, spreadsheets, TensorBoard, and some internally managed tracking scripts before switching to Comet. We thought switching would allow us to manage experiments across multiple tools easily, which had become very inefficient with the previous solutions we were using, making reproducibility very difficult. Comet provided a centralized and much more scalable alternative for experimentation altogether.
How was the initial setup?
The setup process was very straightforward, especially for teams already using modern ML frameworks, and even integration with our existing training pipelines was very smooth.
What was our ROI?
The biggest return on investment of Comet comes from improved reproducibility. We have improved reproducibility and experimentation has been way faster than before, and collaboration between teams has gotten better. This has allowed us to cut our workforce that was redundant, basically doing the manual documentation work, which has now shifted to Comet. Development lifecycles have become about one point five times faster. We spend less time debugging, and more time is spent tracking model performance and documenting experiments, which has shifted to actual model developments and overall metrics improvements. This has been our main return on investment.
Which other solutions did I evaluate?
Before choosing Comet, we evaluated MLflow, Weights & Biases, Neptune.ai, and TensorBoard. Most of these solutions handled parts of experiment tracking, but Comet stood out because it allowed us to have visualization along with centralized experiment management, which served as a base for great collaboration. That clear dashboard and strong visualization capabilities are what led us to choose Comet.
What other advice do I have?
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.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Anita Adiki
Timeline sharing has improved how I investigate health and safety incidents and prevent repeats
Reviewed on May 01, 2026
Review provided by PeerSpot
What is our primary use case?
What is most valuable?
The best features Comet offers include the ability to create the timeline in as much detail as possible and to share it with others.
I usually share the timeline with others via a link, but sometimes via export.
Comet has positively impacted my organization because it's a good system, and the interface is really simple and easy to use, which allows everyone to have a look at these health and safety investigations, of course, if they have access through the company itself. It's a good way to share knowledge amongst people.
For example, with the health and safety incidents, being able to share the timelines with others allows them to distribute it to their sites and create toolbox talks to help those on site understand the situation that's happened and how they can avoid it happening at their site.
What needs improvement?
I'm not sure how Comet can be improved, as I've only been working on it for three months, so I feel like I need a bit more time to be able to answer this question.
For how long have I used the solution?
I have been using Comet for three months.
What do I think about the stability of the solution?
Comet is stable in my experience.
What do I think about the scalability of the solution?
The scalability of Comet is fine, as you can do either one investigation or multiple, which is in regards to the work that myself and my team do.
How are customer service and support?
I'm not sure how Comet's customer support is, as I'm yet to use it. From my three months of usage so far, I would say Comet is a good product to use, and if others have any issues, they should contact the customer support. It's a very good interface, simple to use.
Which solution did I use previously and why did I switch?
I did not use a different solution before Comet.
What was our ROI?
I haven't seen a return on investment with Comet, not that I'm aware of, as I wouldn't have those figures being a graduate, but it may potentially have saved the team some time from whatever their previous method was.
Which other solutions did I evaluate?
I wasn't part of the evaluation process before choosing Comet, as I had joined, and Comet was already the chosen product.
What other advice do I have?
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.
Prem Flara
Integrated AI workflows have accelerated experiment tracking and model debugging for me
Reviewed on Apr 30, 2026
Review from a verified AWS customer
What is our primary use case?
What is most valuable?
The standout features of Comet that I find especially useful include LLM-specific evaluations, tracing, and debugging, along with easy deployment abilities on cloud and self-hosted on-premise solutions. I utilize it in a hosted environment at my end. Additionally, features such as integrations with frameworks like PyTorch , TensorFlow , Hugging Face , and LangChain significantly aid in building enterprise-grade applications while maintaining data sovereignty.
I have built PyTorch programs and leveraged libraries inside some of my POCs and integrated them with Comet, which helps save time and enables me to utilize my already tested features. Comet has positively impacted my organization by facilitating the integration of my AI implementation, which saved research time and enhanced integration with existing frameworks, allowing me to leverage my existing code and libraries. The ability to debug and conduct what-if analysis across new experiments enabled me to run programs on Comet quickly and receive feedback, refining the entire feedback loop and saving time on new research and adaptations to developments in AI and generative AI.
I save approximately thirty percent of overall time in the release cycle thanks to Comet.
What needs improvement?
Some areas I believe Comet can improve include scalability limits, as I face challenges when scaling. Enhancing UI customization would leverage themes within my organization, and expanding on quant trading-specific features would be beneficial, especially since I am focusing on algorithmic trading features and mathematical model enhancements. Scalability, UI and visualization enhancements, as well as including more mathematical models, would be improvements I would appreciate.
For how long have I used the solution?
I have been using Comet for one year.
What other advice do I have?
My advice for others looking into using Comet is to leverage the integration features, as they allow you to quickly utilize existing frameworks, libraries, and code from various areas. This is one of its key features. By leveraging that, engaging in small project POCs can help you discover related experimental data, signal detections, or any mathematical models, thereby saving considerable time in research. I would rate this product a nine out of ten.