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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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 |
Dimensions summary
This listing offers one pricing dimension: the Advanced Package, billed per user under a contract term. You pay based on the number of users you license. The package covers Experiment Management, Model Registry, and Monitoring. Because pricing is a single per-user dimension, your cost scales directly with how many users you add. There are no separate tiers, instance sizes, or usage add-ons to choose from within this listing. To adjust spend, you change the user count on your contract.
Top-of-mind questions for buyers
A user is one platform account granted access to the product. Each person who logs in to use Experiment Management, Model Registry, or Monitoring counts as one licensed user. You license users on your contract, and your cost scales with the number you add.
The package includes Experiment Management, Model Registry, and Monitoring. Experiment Management records and compares training runs. Model Registry versions and organizes models for deployment. Monitoring tracks models in production, including data drift and custom metrics. Other product families or features are not part of this listing.
Cost scales directly with your licensed user count. Adding a user increases the per-user charge for that additional account. There are no tiers, instance sizes, or usage add-ons in this listing. To adjust spend, you change the number of users on your contract.
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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
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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
Vignesh A.
Great for Beginner Developers Learning ML & LLMs, Though Some Gaps Remain
Reviewed on Aug 25, 2026
Review provided by G2
What do you like best about the product?
The best way for a beginner developer to understand ML and LLMs.
What do you dislike about the product?
Being more transparent leads to security threats.
What problems is the product solving and how is that benefiting you?
Supports entire development project using ML
Mohammed Mudasser
Experiment tracking has improved collaboration and simplifies comparing model iterations
Reviewed on Aug 18, 2026
Review from a verified AWS customer
What is our primary use case?
My main use case for Comet is ML experiment tracking and evaluation. A specific example of how I use Comet is that I typically track model runs, compare the metrics and the hyperparameters, and keep the experiments organized so I can easily see what worked and reproduce the better results.
What is most valuable?
What I find particularly useful about my main use case with Comet is having the experiment history, metrics, parameters, and the other runs details organized in one place.
The best features Comet offers in my opinion are the visual dashboards, the run comparison, and the experiment tracking in ML, and those features stand out for me because they automatically capture things such as parameters, code, metrics, and system information, making it much easier to understand and reproduce experiments.
Comet has positively impacted my organization by mainly improving collaboration and productivity by keeping the experiment results, the metrics, and the model comparisons in one place, making it much easier to share results with others and quickly identify which approach is performing better so we can iterate without spending as much time organizing experiments.
What needs improvement?
One area I would like to see Comet improve is in the ease of navigating and comparing a large number of experiments, as better filtering and quicker ways to find specific runs would make the workflow even smoother and save some manual effort.
The documentation part of Comet is generally very useful, but some advanced features could be explained more simply for new users, and I would also appreciate even smoother navigation and comparison when working with a large number of experiments.
I chose a rating of 8 out of 10 because Comet covers the core experiment tracking needs really well, especially tracking and comparing runs, and to make it a 10, I would mainly want smoother navigation for bigger projects and more intuitive guidance around some of the advanced features.
For how long have I used the solution?
I have been using Comet for around a year.
What do I think about the stability of the solution?
Comet's tracking and evaluation results are reliable and consistent with what I expect from my experiments, and having the metrics and run details recorded clearly also makes it easier to validate results rather than relying on assumptions.
Comet is stable and reliable during my usage, especially for logging metrics and tracking the experiments across different runs.
What do I think about the scalability of the solution?
I found Comet to be quite scalable for the projects I have worked on, including handling larger workloads without major issues, and as the number of experiments grown, the navigation can take a little more effort, but overall, it has handled my use case reliably.
Which solution did I use previously and why did I switch?
I did not previously use a different solution before Comet.
What was our ROI?
I have seen a positive return on investment with Comet in my experiment management process, as it has definitely reduced the manual work and helped me iterate on models faster.
Which other solutions did I evaluate?
Before choosing Comet, I evaluated a few alternatives, mainly MLflow, and we ended up preferring Comet because its experiment comparison, visualization, and overall workflow felt more convenient for our use case.
What other advice do I have?
I appreciate the integrations with Comet because it fits into the ML tools I am already using rather than forcing a completely different workflow, and the collaboration features are also useful for sharing experiments and comparing results with teammates, especially when multiple people are working on model iterations.
I primarily use Comet as a cloud-based platform, accessed through its web interface and integrated into my ML workflows, and I am not directly involved in managing the infrastructure and the deployment configuration part.
I mainly use AWS alongside Comet for my machine learning workflows and cloud infrastructure, with Comet itself serving as the experiment tracking layer while the compute and other supporting services run under Amazon Web Services.
I would recommend Comet to teams that want a simple way to track, compare, and organize machine learning experiments, and if you are running multiple model iterations, I would definitely try it because the experiment history and the visualization make the workflow much easier to manage.
Rakesh G.
Comet Makes ML Experiment Tracking and Team Collaboration Effortless
Reviewed on Aug 12, 2026
Review provided by G2
What do you like best about the product?
What I like most about Comet is how it helps me organize, compare, and reproduce ML experiments. It also has a clear dashboard and collaboration features, which make it much easier for the team to understand and stay aligned.
What do you dislike about the product?
What I dislike is the complexity involved in tracking and setting up an experiment during the initial stage.
What problems is the product solving and how is that benefiting you?
Comet solves the problem of managing ML experiments, metrics, parameters, and model versions all in one place. This makes it easier to compare results, and it helps me identify which approach is performing best.
Oil & Energy
Centralized ML Experiment Tracking with Clear Dashboards and Strong Reproducibility
Reviewed on Aug 10, 2026
Review provided by G2
What do you like best about the product?
This platform makes it easy to track and compare machine learning experiments all into a centralised workspace. I also like how it automatically records the needed parameters, metrics, code changes and other system information without even requiring any extensive support. Their dashboard provide clear visual comparison between the different model runs. It also helped our model registry and artefact versioning, improving the reproducibility and team collaboration. Overall, I would say this gave us better control over the complete model development life cycle.
What do you dislike about the product?
Their interface can be bit overwhelming initially because of the number of features and configuration options available. And for large projects with multiple experiment may also require careful organization to keep their workspace manageable. Some advanced capabilities and other team management features are limited to higher priced plans and uploading extensive logs and artifacts can also add more storage and overhead the performance. Having a better onboarding and simpler cost visibility or transparency would improve the overall platforms experience.
What problems is the product solving and how is that benefiting you?
This platform solves the difficulty of manually tracking model experiments across their spreadsheet and even, notebooks and disconnected tools. It gave us a reliable record of every model run and including its parameters, metrics code and other related artefacts. This allowed our team to reproduce successful experiments and understand why one model performs better than the other one. Their model registry also create a more structured process for promoting models into production, as a result of it our development became faster and more collaborative and less prone to repetitive work.
Anil B.
Simple, All-in-One Machine Learning Experiment Tracking with Comet.
Reviewed on Aug 09, 2026
Review provided by G2
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product 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.