Comet vs DataRobot comparison

Comet and DataRobot are both solutions in the AIOps category. Comet is ranked #16 with an average rating of 9.0, while DataRobot is ranked #15 with an average rating of 8.3. Additionally, 100% of Comet users are willing to recommend the solution, compared to 100% of DataRobot users who would recommend it.

Comet

Read 2 Comet reviews

304 Views
122 Comparison Views

100% willing to recommend

DataRobot

Read 6 DataRobot reviews

2,309 Views
369 Comparison Views

100% willing to recommend

Comet

DataRobot

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Executive SummaryUpdated on Jan 4, 2026

DataRobot and Comet compete in the machine learning space. While DataRobot outshines in user satisfaction with support and pricing, Comet is favored for its feature offerings, justifying its higher cost.

Features: DataRobot focuses on automation for model development, offering high-quality predictive modeling tools and deep integration capabilities. Comet prioritizes experiment management and optimization, providing comprehensive experimentation tracking, collaboration tools, and enhanced team productivity.

Ease of Deployment and Customer Service: DataRobot offers a streamlined deployment model with efficient customer support for quick resolution of challenges. Comet provides a flexible deployment approach but may require more effort to implement fully. While Comet's support is responsive, users find DataRobot's service more efficient.

Pricing and ROI: DataRobot has an attractive cost structure and faster return on investment with its straightforward pricing model. Comet, despite a higher initial setup cost, promises long-term ROI through its advanced features and experiment management efficiency.

To learn more, read our detailed AIOps Report (Updated: January 2026).

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Comet

Ranking in AIOps

16th

Ranking in AI Observability

14th

Average Rating

9.0

Reviews Sentiment

8.8

Number of Reviews

Ranking in other categories

No ranking in other categories

DataRobot

Ranking in AIOps

15th

Ranking in AI Observability

74th

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Predictive Analytics (5th), AI Development Platforms (15th), AI Finance & Accounting (6th)

Featured Reviews

Ujjwal Mule

Data Analyst at Proton Technologies

Experiment tracking has transformed model comparisons and now supports faster, clearer insights

I would like to see more flexibility around content and reporting in Comet. Overall, the features are very strong, but having more customizable dashboards or easier ways to create shareable summaries for non-technical stakeholders would be helpful. It would make it easier to turn experiment results into clear insights without exporting data to other tools. There is a need for some improvements in Comet. It is very strong overall, but there are a few areas where it can be improved. For example, more flexibility in dashboards and content customization would be helpful, especially for creating summaries for non-technical stakeholders. Additionally, some advanced features have a learning curve, so slightly simpler onboarding or guided tips would make it easier for new users or freshers. These are minor points and they do not affect my regular day-to-day usage, but from the perspective of a fresher or new users, it could be improved.

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Naqash Ahmed

Senior Data Reporting Analyst at a educational organization with 1,001-5,000 employees

Automation has improved efficiency and decision-making while big data handling and transparency still need work

Aside from the many advantages of DataRobot, I believe there are areas that could be improved based on my experience. There is a lack of transparency in the models; sometimes it feels like a black box. For example, when I uploaded a large data set of about two gigabytes for processing, the time taken was slower than expected. Additionally, the handling of bigger data sets could be better, as it performs extremely well with smaller datasets but can lag with larger ones. The integration with some other tools used in our organization can also be challenging, and more flexibility for custom pre-processing and advanced model tuning would be beneficial. In terms of support and documentation, I believe improvements are needed. For instance, the response time from DataRobot could be quicker, which would be appreciated when we need assistance. The documentation is generally sufficient, but it can be lengthy and could use more real-world examples and step-by-step tutorials for better clarity. Lastly, creating a client community where users can share experiences and solutions might enhance the overall value and learning curve.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"Previously, it used to take a few hours to collect results and prepare comparisons, but now with Comet, this takes minutes instead of hours."

"Model metric tracking and comparison has been extremely beneficial, and Comet's customer service has also been excellent—any issue we've had, they have been able to help us resolve."

"DataRobot is highly automated, allowing data scientists to build models easily."

"It's easy to do MLOps operations. It's a lot easier to manage jobs and see the logs if there's any drift in a model."

"Tasks such as model testing, feature engineering, and predictions that used to take us days or weeks can now be accomplished in hours."

"DataRobot can be easy to use."

"By automating highly technical aspects like model comparison, DataRobot enhances productivity and reduces project timelines from three months to less than one month."

"We especially like the initial part of feature engineering, because feature engineering is included in most engines, but DataRobot has an excellent way of picking up the right features."

Cons

"Additionally, some advanced features have a learning curve, so slightly simpler onboarding or guided tips would make it easier for new users or freshers."

"The business departments will love to work with DataRobot because they use the tool to investigate their data, such as targeting what they want to investigate. They don't need any data scientists near them. They can investigate at eye level and bring into the BI tool, or can bring it to the data scientist. Data scientists can use this tool to bring increase the solution to the maximum. All the others can use it, but not to the maximum."

"DataRobot is a UI-based tool, which means it cannot provide all the features I might manually implement through notebooks or Python. In this aspect, I see room for improvement in its functionality."

"There are some performance issues."

"If we could include our existing Python or R code in DataRobot, we could make it even better. The DataRobot that we have is specific to an industry, but most of the time we would have our own algorithms, which are specific to our own use case. If we had a way by which we could integrate our proprietary things into DataRobot with a simple integration, it would help us a lot."

"There is a lack of transparency in the models; sometimes it feels like a black box."

"Generative AI has taken pace, and I would like to see how DataRobot assists in doing generative AI and large language models."

Pricing and Cost Advice

Information not available

"The price of DataRobot is good because if you take the price of the solution which is approximately $65,000, it is less than a data scientist. There are very few data scientists available."

"We dropped the plan to use DataRobot, because we found the pricing to be on the higher sise. We liked DataRobot a lot, but due to the pricing, we dropped that idea."

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Top Industries

By visitors reading reviews

No data available

Financial Services Firm

14%

Manufacturing Company

12%

Computer Software Company

10%

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What needs improvement with Comet for SageMaker Partner AI Apps?

SageMaker itself has a cumbersome interface, which makes launching Comet somewhat of a hassle.

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What is your primary use case for Comet for SageMaker Partner AI Apps?

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 installatio...

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What is your experience regarding pricing and costs for DataRobot?

While pricing falls more under my IT colleagues, from my perspective, the overall experience feels justified. The premium pricing is reasonable for the value provided, and I'd say it's worth the in...

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What needs improvement with DataRobot?

See all answers

What is your primary use case for DataRobot?

My main use case for DataRobot is to perform predictive analysis and automation of machine learning workflows. I use it to quickly build, test, and deploy models without extensive coding. One of th...

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Comparisons

Weights & Biases vs Comet

Compared 100% of the time

More Comet Competitors

Amazon SageMaker vs DataRobot

Compared 23% of the time

Microsoft Azure Machine Learning Studio vs DataRobot

Compared 10% of the time

Alteryx vs DataRobot

Compared 7% of the time

Altair RapidMiner vs DataRobot

Compared 7% of the time

Hugging Face vs DataRobot

Compared 7% of the time

More DataRobot Competitors

Product Reports

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AIOps

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DataRobot

January 2026

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Also Known As

Comet for SageMaker Partner AI Apps

No data available

Overview

Comet offers powerful capabilities for tracking, comparing, and optimizing machine learning models, making it a valuable tool for data-driven enterprises aiming to improve project outcomes.

Designed with efficiency in mind, Comet enhances experiment tracking and model management. It supports diverse machine learning workflows helping teams streamline model development and iteration. Integration with popular ML libraries provides seamless tracking and enhances model reproducibility. Valuable for projects requiring collaboration and transparency, Comet aids teams in maintaining consistency across ML pipelines.

What are Comet's key features?

Experiment Tracking: Allows detailed tracking of multiple iterations to provide insights and enhance decision-making.
Model Versioning: Facilitates clear tracking of model changes aiding in better management and deployment.
Collaboration Tools: Supports team collaboration by sharing experiments and insights efficiently.
Real-Time Visualization: Offers visualizations for quick analysis helping teams rapidly interpret results.
Integration Support: Provides easy integration with ML libraries enhancing user flexibility.

What benefits are associated with Comet?

Increased Efficiency: Streamlines processes allowing data scientists to focus on core analysis tasks.
Collaboration Enhancement: Supports collaborative environments through shared dashboards and reports.
Improved Reproducibility: Ensures that experiments can be rerun with consistent results, aiding in validation and testing.
Scalability: Adapts to growing data and model needs, supporting extensive ML operations.

In industries like finance, healthcare, and manufacturing, Comet is implemented to enhance model accuracy and efficiency. By providing robust experiment tracking and collaboration capabilities, Comet allows teams to innovate and deliver results within demanding operational frameworks.

Comet

DataRobot captures the knowledge, experience and best practices of the world’s leading data scientists, delivering unmatched levels of automation and ease-of-use for machine learning initiatives. DataRobot enables users to build and deploy highly accurate machine learning models in a fraction of the time.

DataRobot

Sample Customers

Information Not Available

Harmoney, Zidisha, ONE Marketing, DonorBureau, Trupanion, Avant

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