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.
Details
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Delivery method
Deployed on AWS
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Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Advanced Package | Experiment Management, Model Registry, Monitoring | $4,500.00 |
Vendor refund policy
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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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
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
Model Tracking
"Automatically track code, hyperparameters, metrics, and training run details with minimal code integration"
Experiment Management
"Enable real-time tracking and sharing of machine learning experiment results across team environments"
Model Registry
"Maintain comprehensive model versioning and lineage tracking from training to production deployment"
Production Monitoring
"Detect model performance degradation through drift identification and accuracy metric tracking"
Visualization Support
"Create custom interactive visualizations for machine learning experiment analysis and comparison"
Model Performance Monitoring
Comprehensive tracking and analysis of model performance across various machine learning domains including tabular, deep learning, computer vision, natural language processing, and large language models
Anomaly Detection
Advanced capabilities to identify and mitigate model drift, data integrity issues, hallucination, accuracy, safety, and security problems in AI deployments
Advanced Analytics
Utilization of 3D UMAP visualization for macro-level trend analysis and root cause diagnostics for micro-level model performance investigation
Security Compliance
SOC2 Type 2 security compliance with role-based access control (RBAC) for secure model operationalization and environment protection
Model Validation
Comprehensive model validation and improvement mechanisms to enhance model outputs and optimize deployment outcomes before production
Data Pipeline Management
Supports data sharding, dynamic resource optimization, and prevents data contamination with error correction mechanisms
Model Authoring Capabilities
Provides deep learning features with custom reusable components and automatic dimensionality transformations
Experiment Tracking
Enables hyperparameter tuning, model evaluation, and comprehensive model evolution tracking
Model Registry and Deployment
Offers secure model storage with full traceability and one-click deployment across cloud, on-premises, and edge environments
Security Infrastructure
Implements comprehensive security features to protect data and models throughout the machine learning lifecycle
Standard contract
No
Customer reviews
Ujjwal Mule
Experiment tracking has transformed model comparisons and now supports faster, clearer insights
Reviewed on Jan 28, 2026
Review from a verified AWS customer
What is our primary use case?
My main use case for Comet is experiment tracking and performance analysis. I initially used it as a tracking model. As a data analyst, I use it to monitor metrics, compare different model runs, and track changes. I also use it to analyze results in a structured way. It helps me identify trends, validate model performance, and share clear insights with the data science teams for better decision-making.
One example of how I have used Comet for experiment tracking recently is when we were testing different versions of a prediction model. I used Comet to track each experiment's parameters, accuracy, and loss values. I also used it for comparing runs in Comet. I could clearly see how changing features and hyperparameters impact performance. This helped us identify the best-performing model and confidently share the results with the team.
On a day-to-day basis, I use Comet mainly to keep experiments organized and easy to review. Whenever a new model run is completed, I check the log metrics, add notes, and tag the experiments, so it is easy to find later. During discussions, I quickly pull up the comparisons in Comet instead of creating manual reports. It saves time and helps me explain performance clearly to both technical and non-technical team members.
What is most valuable?
My favorite features of Comet are experiment tracking and the ability to visually compare multiple runs side-by-side. Experiment tracking lets me automatically log metrics, parameters, and results in one place, so nothing gets lost or mixed up. The dashboards and visualizations make it easy to analyze trends without building custom plots. The collaboration tools are also very helpful. Notes, tags, and shared views make it easier to communicate findings with my teams. Metrics history is also a very good feature. Seeing historical experiments and performance over time helps in decision-making and reproducibility.
Experiment tracking is one of the most important features for me, and it is valuable because it gives me a clear view of how different model runs perform under different parameters. Instead of checking results manually, I can quickly compare metrics such as accuracy or loss and understand what actually improved performance. This saves time and makes my analysis more reliable.
The overall impact of Comet on my organization has been positive. It is structured and transparent in how we track and analyze experiments. Before using Comet, results were scattered across files and spreadsheets, which caused confusion. Now, our team spends less time tracking results and more time analyzing them. Collaboration has improved. Reviews are faster and decisions are more data-driven because everyone is looking at the same reliable information.
One clear outcome that highlights the positive impact of Comet is the reduction in time spent analyzing experiments. Previously, it used to take a few hours to collect results and prepare comparisons, but now with Comet, this takes minutes instead of hours. We also see fewer mistakes because metrics and parameters are automatically logged. As a result, model reviews become faster, and the team can move to the next iteration more quickly and confidently. It has saved 40 to 50 percent of our time.
What needs improvement?
I would like to see more flexibility around content and reporting in Comet. Overall, the features are very strong, but having more customizable dashboards or easier ways to create shareable summaries for non-technical stakeholders would be helpful. It would make it easier to turn experiment results into clear insights without exporting data to other tools.
There is a need for some improvements in Comet. It is very strong overall, but there are a few areas where it can be improved. For example, more flexibility in dashboards and content customization would be helpful, especially for creating summaries for non-technical stakeholders. Additionally, some advanced features have a learning curve, so slightly simpler onboarding or guided tips would make it easier for new users or freshers. These are minor points and they do not affect my regular day-to-day usage, but from the perspective of a fresher or new users, it could be improved.
For how long have I used the solution?
I have been using Comet for six to 12 months.
What do I think about the stability of the solution?
In my experience, Comet is very stable. I have not encountered significant downtimes, data loss, or performance issues when tracking experiments or comparing runs. It works consistently, even when multiple team members are using it, which gives us confidence in relying on it for our day-to-day work.
What do I think about the scalability of the solution?
Comet is very scalable because we continuously use it. As our number of experiments and team members grows, it handles the increased load without any issue. We can track multiple models, large data sets, and numerous runs simultaneously, which makes it easy to scale up projects without worrying about the infrastructure, performance, or storage limitations.
How are customer service and support?
In my experience, Comet's customer support is responsive and helpful. Whenever we had questions about setup or features, the support team provided clear guidance. They also have good documentation and resources, which makes it easy to resolve common issues quickly. Overall, the support has been reliable and adds confidence when using the platform.
How would you rate customer service and support?
Negative
Which solution did I use previously and why did I switch?
Before Comet, we were using manual tracking with spreadsheets and shared documents to log experiments and results. It worked for small projects, but it was time-consuming, prone to errors, and hard to collaborate on. Switching to Comet made tracking more structured, reliable, and easy to review, especially for multiple experiments and team collaboration.
How was the initial setup?
We have deployed Comet in our organization on a public cloud. We access it as a cloud-based platform, so there is no heavy infrastructure to manage on our side. This makes it easy for the team to access from different locations and helps with scalability and maintenance.
What about the implementation team?
We have used AWS for deploying Comet. The authorities have purchased Comet through the AWS Marketplace . This makes it easy to manage subscriptions, integrate with our existing AWS infrastructure, and ensures secure and cloud-based access for our team.
What was our ROI?
We have seen a clear return on investment with Comet by reducing the time spent manually tracking experiments and preparing reports. Our team can focus more on analysis and improving models. For example, tasks that previously took hours now take minutes, which has improved productivity and accelerated project timelines. While I cannot share exact financial figures, the time and efficiency savings have been significant and have clearly added value to our operations.
What's my experience with pricing, setup cost, and licensing?
My experience with pricing and licensing for Comet was very straightforward. Since we purchased it through the AWS Marketplace , the subscription and billing are clear and easy to manage. Additionally, the setup costs were minimal because it is a cloud-based platform, so we did not have to invest in hardware or complicated infrastructure. Overall, the licensing model is flexible for our team size, and scaling up is easy when needed.
Which other solutions did I evaluate?
We were using Weights & Biases and MLflow before choosing Comet. While they all offer experiment tracking, we chose Comet because it had a cleaner UI, better run comparison, and easier collaboration for our team. It also integrated smoothly with our existing Python workflow, which made adoption faster and simpler.
What other advice do I have?
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.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
reviewer2751006
Experiment and asset tracking enhance model development and ease of on-prem maintenance
Reviewed on Aug 19, 2025
Review provided by PeerSpot
What is our primary use case?
I use Comet for experiment and asset tracking during model development, as well as to support model reproducibility and transparency. I also appreciate the ability to perform an on-prem installation without the need to maintain the installation.
How has it helped my organization?
Previously, we had an on-prem installation that required frequent re-deployment due to internal security standards, which could cause down-time during model development. Using Comet within SageMaker streamlined the deployment process to require zero maintenance and also simplified billing.
What is most valuable?
Model metric tracking and comparison has been extremely beneficial. Comet's customer service has also been excellent. Any issue we've had, they have been able to help us resolve.
What needs improvement?
SageMaker itself has a cumbersome interface, which makes launching Comet somewhat of a hassle.
For how long have I used the solution?
I have used the solution for 3 months.
Which deployment model are you using for this solution?
On-premises
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Shreyansh J.
Comet.ml: Streamlining Machine Learning and Collaborative Experiment Tracking Platform
Reviewed on Feb 09, 2023
Review provided by G2
What do you like best about the product?
Comet.ml provides an easy-to-use interface for tracking experiments, comparing results, and reproducing past results. This helps data scientists and machine learning engineers to keep track of their progress and make informed decisions based on their experiments. Comet.ml integrates with popular version control systems like Git, allowing users to track changes in their code and experiments over time.
What do you dislike about the product?
Comet.ml may not be suitable for large-scale machine learning projects, as it has limited scalability compared to other solutions. Some users may find the platform's user interface and features to be limited, as it may not provide the level of customization they need for their projects.
What problems is the product solving and how is that benefiting you?
Machine learning projects can involve a large number of experiments and it can be difficult to keep track of all the results and make decisions based on them. Comet.ml provides a platform for tracking experiments, comparing results, and reproducing past results, making it easier to manage machine learning projects.
Avi P.
Solid platform overall but there's competition
Reviewed on Jun 20, 2022
Review provided by G2
What do you like best about the product?
Simplicity to integrate into my project. Nice UI and UX overall
What do you dislike about the product?
Expensive and not so customizable overall. There are platforms that compete with this one and have better offerings, which is why i switched.
What problems is the product solving and how is that benefiting you?
Helps me speed up building my neural networks and ML tests...
Taha S.
Easy to Use !! Great UI
Reviewed on May 24, 2022
Review provided by G2
What do you like best about the product?
User interface
Easy to use
Support different View and Easy to search Text
Easy to use
Support different View and Easy to search Text
What do you dislike about the product?
Price.
time take to pull data
small notification view
time take to pull data
small notification view
What problems is the product solving and how is that benefiting you?
Code Debug
Application monitoring
Application monitoring