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.
4.4
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
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
Mihir Jadhav
Tracing ai agents has boosted debugging, cut latency, and optimizes our support chatbot
Reviewed on Feb 16, 2026
Review provided by PeerSpot
What is our primary use case?
My main use case for Comet is building AI agents, optimizing them, getting traces, and experimentation.
A specific example of how I use Comet for optimizing AI agents and tracing is with our chatbot called Oscar, which is a RAG-based, LLM-based chatbot where users can manage their chats, ask questions regarding the products, and it also acts as a support agent. Comet helps us log the traces, capture them, organize the LLM calls, and optimize the costing accordingly.
Comet has an inbuilt AI that helps us get conclusions for the cost of LLM calls and many other things.
What is most valuable?
The best features Comet offers in my opinion are the datasets and the prompt library, along with the evaluation. The traces are very deep, covering the tool calls and everything, so we have flow diagrams.
The flow diagrams help my team by aiding the log traces to capture and organize application calls, which helps in tracing from the first to last call if an API gets hit using LLM. We analyze how it works, how it travels, and each log trace using the flow diagram, which provides a visual representation.
Testing with multiple test datasets has helped us significantly.
Comet has positively impacted my organization as we have seen a drastic change. The product has improved since we started using Comet, and debugging has become much easier.
Debugging became easier for us because the tracing helps us in production to trace an LLM call, save the cost, reduce the tool calling, optimize the AI responses, increase the speed, and reduce the latency.
What needs improvement?
Comet can be improved by adding the MCP server to integrate with Chat GPT and other applications. If they provide skills to integrate while building applications to seamlessly incorporate Comet in AI agents, it would help significantly.
For how long have I used the solution?
I have been using Comet for within one year.
What do I think about the stability of the solution?
Comet is stable, and I have not faced any bugs as of now; it is going very well.
What do I think about the scalability of the solution?
Comet handles scalability well, accommodating growth and increased workloads.
How are customer service and support?
We have not reached out to customer support yet because we have not needed support as all things are going great.
Which solution did I use previously and why did I switch?
We previously used Grafana , which was not meant for our purpose, so we felt Comet is better in terms of LLM Ops and tracing AI queries.
What was our ROI?
I have seen a return on investment since time has obviously been saved and productivity has improved. With the automation brought by Comet, the work we were doing with other tools has changed, so time has been saved and while we have not calculated the cost saved yet, it is likely there.
What's my experience with pricing, setup cost, and licensing?
My experience with pricing, setup cost, and licensing has been straightforward and easy to understand, offering unlimited team members and some metrics without needing customization for our plan yet.
Which other solutions did I evaluate?
Before choosing Comet, I evaluated Sentry , but it is not more focused on AI; it is a combination of various logging and tracing software.
What other advice do I have?
My advice for others looking into using Comet is that if they are utilizing LLM bots at the enterprise level, tracing is essential, and Comet offers the best solution to trace logs and optimize the overall solution. I would rate this product a 9 out of 10.
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