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 sharing training run results and model performance data.
Multi-Model Type Support
Monitoring and observability capabilities for tabular, deep learning, computer vision, natural language processing, and large language model deployments
Performance and Drift Detection
Identification and mitigation of model performance degradation, data drift, data integrity issues, hallucination, accuracy, safety, and security problems
Root Cause Analysis and Diagnostics
Advanced analytics using 3D UMAP for macro-level trend and outlier analysis, with micro-level root cause analysis and diagnostic capabilities for model issues
Security and Access Control
SOC2 Type 2 security compliance certification and role-based access control (RBAC) for enterprise-level permission management across protected environments
Custom Metrics and Dashboards
Customizable dashboards and reports with support for custom metrics to track model performance aligned with business KPIs
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 and Data Reusability
Enhances data reusability and ensures consistency across builds with optimized data structures for fast random access
Model Development and Experimentation
Supports deep learning model authoring with custom reusable components, automatic dimensionality transformations, hyperparameter tuning, and experiment tracking with model evolution monitoring
Model Registry and Governance
Provides model security with full traceability, version control, and accidental deletion prevention through centralized model management
Multi-Environment Deployment
Enables one-click model deployment across versatile environments including cloud, on-premises, and edge computing platforms
Standard contract
No
Customer reviews
Harsh S.
Easy Python ML Setup, but Limited Language Support and Can Get Pricey
Reviewed on Oct 08, 2026
Review provided by G2
What do you like best about the product?
It is very easy to setup you can setup it in very few lines of code and you can autolog in a minute it work across many python ml libraries like skitlearn and the code environment and parameter are saved automatically saved after one run of ml model.
What do you dislike about the product?
It is mainly python centric it supports less other languages like ruby kotlin and java and the cost of team and enterprise edition camn get very expensive as compared to any open source option like github.
What problems is the product solving and how is that benefiting you?
Building an ml in this day to day life is not same as writing normal software, the same code give same output but in ml the result depends upon the code , data which you upload what setting you setup some randomness and depends upon which environment you are working and you can change all of these constantly while experimenting the ai but when we use this comet ml platform, it make a proper environment for a team so it has same data setting in an environment which improves the quality of ml model as compared to a normal software
Kavyansh G.
Great for Team Model Collaboration, but Pricing and Credit Usage Are Hard to Predict
Reviewed on Oct 08, 2026
Review provided by G2
What do you like best about the product?
The best part is when we build a model in a team, it helps us check which team member is working on which part of the model, reducing time and duplication.
What do you dislike about the product?
The most disliked part of this software/platform is that we can't predict the price and the amount of credits that will be used on a project, leading to high usage and platform fees that are not easily affordable by a normal organization.
What problems is the product solving and how is that benefiting you?
When working on traditional software to create a model like GitHub, we can't identify which part of the work is being done by which member, leading to long hours and reducing our team's potential. Comet ML solves the problem of duplication among team members.
Ashish Kumar P.
Great for Tracking and Optimizing ML/LLM Experiments, But Costs Scale with Usage
Reviewed on Oct 07, 2026
Review provided by G2
What do you like best about the product?
It helps us in data science projects to track, compare, visualize, and optimize machine learning experiments and LLM workflows in one place, and you can integrate Comet into your training script by adding just a few lines of code.
What do you dislike about the product?
It has paid tiers and usage fees in billing, such as extra usage of Opik or per user seat licenses, which increase the cost to use it and rise with an increase in team members and growing data.
What problems is the product solving and how is that benefiting you?
In engineering teams, team members often work individually on their local machines without a centralized hub, causing duplication of work when two members do the same task, leading to time loss. However, when we use Comet.ml, it helps team members track who is working on which project and then work accordingly.
Financial Services
Comet Keeps Every Serious Run—Metrics, Parameters, and Context—in One Place
Reviewed on Oct 02, 2026
Review provided by G2
What do you like best about the product?
Before Comet, the “best” model lived in someone’s memory or an old notebook name. What I like is that every serious run leaves a record—metrics, parameters, and context—in one place, so we can find what actually worked instead of re-running guesses. Comparing a few candidates takes minutes, and handing a result to someone else does not depend on them recreating my local setup.
What do you dislike about the product?
If the team logs inconsistently, the workspace gets noisy. You still need light discipline on what to track and how to name projects. It will not fix a messy process by itself.
What problems is the product solving and how is that benefiting you?
It solves the lost-experiment problem: results scattered across machines and chat threads. The benefit is faster decisions and less repeated work, because we can see prior runs and build on them instead of starting from scratch each time.
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.