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.
4.2
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
Nishu K.
Strong ML Team Collaboration, But Limited Support Beyond Python
Reviewed on Oct 13, 2026
Review provided by G2
What do you like best about the product?
The platform is best for when we are working in a team on a ML Model. It has a good collaboration features, reports and panels which can be share among the team members. Free tier for individuals and academics person which suits students build portfolio.
What do you dislike about the product?
It is mainly focus on Python, it supports other languages very less. It track and monitor the ML Models but does not run your pipelines like Cubeflow or airflow and the team and enterprise addition of this platform is very expensive as we compared to open source options.
What problems is the product solving and how is that benefiting you?
The machine learning work is experimental and messy, you try dozens of code to run with different hyperparameter dataset codeversion and architecture but after we use format we get to know that it is an experimental tracking and ML Lifecycle program that records everything about each run so the results are comparable, sharable and reproducible.
Computer Software
Simple Tracing for Debugging Workflows
Reviewed on Oct 13, 2026
Review provided by G2
What do you like best about the product?
I explored Comet's experiment observability capabilities through its Opik tracing workflow. I found it useful to see function inputs and outputs recorded in a centralized dashboard, making it easier to inspect execution results and understand what happened during a run. The trace details were straightforward to navigate, and having the input, output, and execution duration visible together was helpful for debugging a small test workflow. For someone learning to monitor AI application behavior, this kind of visibility can be a useful starting point. My evaluation was limited to a small hands-on test, so I have not assessed the full experiment-tracking feature set or its value in a production environment.
What do you dislike about the product?
My evaluation was limited to a small tracing workflow, so I haven't explored enough of Comet.ml to assess all its limitations. During setup, the initial configuration required a few steps, and clearer guidance around API key setup and project configuration would make onboarding smoother for new users. A more beginner-friendly quickstart with troubleshooting tips and a simple example could help users get their first trace working faster. I would need more hands-on experience with the broader platform before commenting on its performance, pricing, or advanced features.
What problems is the product solving and how is that benefiting you?
I explored Opik's tracing workflow to understand how application execution can be monitored and debugged. For my small test, it recorded a function's input and output in a centralized dashboard, making it easier to inspect the result without relying only on terminal output. This was useful for learning how tracing can help developers investigate application behavior and identify issues. My evaluation was limited to a test function, so I haven't measured business impact or used Comet.ml in a production workflow.
Mrinal A.
Promising Open-Source LLM Tracing, but Hosted Dependency and Python-Centric
Reviewed on Oct 12, 2026
Review provided by G2
What do you like best about the product?
Comet is the newest open-source tool for tracing and evaluating ML LLM applications, reflecting the shift toward LLM work. Check their site for the current feature set, as this part is evolving very quickly. The main thing about this platform is that it is 3-tier for individuals and academics, which suits students building portfolios.
What do you dislike about the product?
The most hated part is that it has a vendor dependency because the full-fledged product is hosted as SaaS, so your experiment history lives on their platform, and self-hosting is typically an enterprise feature. It is Python-centric and supports other languages less well.
What problems is the product solving and how is that benefiting you?
When you work on an LLM model, the results will be scattered across notebooks, terminal output, and spreadsheets, causing much confusion. But when we started using Comet, most of the time, whenever we ran our LLM models, it recorded every single output in the same format. Comet can put them side by side, allowing you to sort by accuracy, overlay loss curves, and hyperparameter changes.
Bilal A.
Flexible Enterprise Deployment and Team Collaboration, but Control Panel Needs Work
Reviewed on Oct 12, 2026
Review provided by G2
What do you like best about the product?
it has flexible deployment on cloud on premises for enterprise with data control needs you can collaborate with your team and share project panel and report with each other.
What do you dislike about the product?
Basic logging is easy but the control panel artifacts and monitoring takes time to master which causes overhead for small projects.
What problems is the product solving and how is that benefiting you?
The model works in testing but in the real world the data changes every day and accuracy drops quietly because ML experiments generate a lot of info and humans track it very badly by hand. Now Comet acts like a lab notebook that writes itself, recording automatically and storing everything in a central place. It lets us compare and analyze the data and helps us track the model beyond training.
Ritweeja A.
Fast, Efficient Metric Streaming—But SDK and UI Features Don’t Always Match
Reviewed on Oct 12, 2026
Review provided by G2
What do you like best about the product?
The most liked part about this platform is instead of forcing HTTP requests every time a metric is calculated, which would cripple the GPU training performance, the SDK pushes raw metrics into an in-memory queue. It automatically batches the payload and flushes them to the Comet server over a persistent WebSocket connection.
What do you dislike about the product?
The most disliked part about this platform is it has advanced features, a common frustration among ML engineers is the occasional drift between what can be done by the Python SDK versus what functions in the browser UI.
What problems is the product solving and how is that benefiting you?
A training job run for 14 hours on a cloud GPU cluster and suddenly crashes and underperforms. Without live monitoring, the engineer building that ML model has no idea if it ran out of VRAM, overheated, or suffered from exploding gradients. Here, Comet comes in and streams log real-time hardware metrics alongside training metrics. If a model fails, you can see the exact moment the hardware or math broke down.