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
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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
Consulting
Comet.ml Makes Experiment Tracking Effortless with Clear Dashboards
Reviewed on Sep 01, 2026
Review provided by G2
What do you like best about the product?
The best thing about Comet.ml is that it takes a lot of the manual effort out of experiment tracking. It's easy to see how different model runs performed, and the dashboards make it simple to spot trends without digging through logs. It has definitely helped make my workflow more organized. The automatic logging of metrics, parameters, and training runs has also made it much easier to compare experiments
What do you dislike about the product?
Although Comet integrates with major frameworks like PyTorch and TensorFlow, highly customized ML pipelines actually require additional logging and instrumentation work which is not good
What problems is the product solving and how is that benefiting you?
Since my work involves People Consulting, competency frameworks, working with large datasets of JDs/ competencies, using comet helps me in bringing more structure to this process as I can track experiments and their results in 1 place instead of relying on scattered files or notes or manually maintained records.
The biggest benefit is visibility and reduction of time spent manually.
The biggest benefit is visibility and reduction of time spent manually.
Sangeeta S.
Keeps ML experiments organized and comparable.
Reviewed on Aug 30, 2026
Review provided by G2
What do you like best about the product?
I really like that Comet.ml allows me to track experiment metrics, parameters, model versions, and training outputs all in one place. When I make changes to features or model parameters, comparing runs becomes easy-eliminating the need to take separate notes or maintain spreadsheets. The dashboard also lets me see at a glance which experiment is performing better.
What do you dislike about the product?
The platform is packed with features. Because of this, I initially found the user interface a bit cluttered. It can take some time to learn how to organize logging effectively, and setting things up for large projects might also require some time.
What problems is the product solving and how is that benefiting you?
Avoid losing track of your machine learning experiments with Comet.ml. We often test multiple models, datasets, and hyperparameters simultaneously. Comet.ml enables us to keep a record of what was changed and the resulting output, allowing for quick and efficient model comparisons. It is also excellent for collaboration, as others can see previous experiments without any extra effort.
Nilesh C.
Clear Visibility Into Experiments, Metrics, and Model Performance
Reviewed on Aug 29, 2026
Review provided by G2
What do you like best about the product?
It gives good visibility into experiments, metrics, and model performance without making the workflow complicated.
What do you dislike about the product?
The main thing I would improve is the learning curve for new users. Some features and settings can take a little time to understand, especially when managing a large number of experiments. The overall experience is good, but the initial setup and navigation could be more straightforward.
What problems is the product solving and how is that benefiting you?
Comet.ml helps solve the problem of keeping machine learning experiments organized and easy to compare. Instead of manually tracking different runs, metrics, and model versions, everything can be monitored in one place. This makes it easier to understand what is working, compare experiments, and avoid losing track of previous results. It saves time and makes the overall ML workflow more manageable.
Aggunuru V.
Powerful Experiment Tracking and ML Workflow Management
Reviewed on Aug 27, 2026
Review provided by G2
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product 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.
Arun R.
Comet.ml Makes ML Experiment Tracking and Visualization Effortless
Reviewed on Aug 26, 2026
Review provided by G2
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product 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