Sold by: Comet
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Comet provides an end-to-end model evaluation platform for AI developers, with best in class LLM evaluations, experiment tracking, and production monitoring.
4.8
Overview
Comet provides a comprehensive platform designed to streamline and enhance the machine learning (ML) lifecycle.
Ability to build trustworthy GenAI applications via robust evals: Comet's LLM Eval product Opik allows teams to detect hallucinations in generated outputs, ensure prompt quality, and establish robust metrics for GenAI application performance assessment,
Model Reproducibility and Efficient R&D for ML teams: Comet enables reproducibility by tracking experiments, logging metrics, providing powerful visualization and collaboration tools, and versioning datasets and models for consistent results across runs.
Free trials on your VPC or on Comet.com are available. Please contact us at sagemaker@comet.com
Highlights
- End to End LLM evaluations and observability with Opik
- Best in class experiment tracking and visualizations
- Deeply integrated to Sagemaker products
Details
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Financing for AWS Marketplace purchases
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Pricing
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Try this product free according to the free trial terms set by the vendor.
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/month |
|---|---|---|
Platform Users | Platform users for Comet | $149.00 |
Traces | Total trace amount used in Opik | $0.001 |
Vendor refund policy
For refunds please contact us as support@comet.com
Custom pricing options
Request a private offer to receive a custom quote.
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Delivery details
Software as a Service (SaaS)
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Resources
Vendor resources
Support
Vendor support
contact us at support@comet.com
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.
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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)