

Find out in this report how the two AI Observability solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
We have automated complete order-to-cash processes for multiple customers, saving over 90 million dollars.
The return is faster since development efforts are minimized, allowing for quicker integration delivery.
I have observed a 100% return on investment, with considerable time saved and clients very happy with the output they received.
On average, we're saving about 10 to 15 hours per project.
I have seen a return on investment, specifically with increased data science productivity by four times, time saved with deploying models, and homogeneous analysis models developed easily.
Seamlessly meeting SLAs and providing excellent responses to challenges and issues related to interface and data connectivity.
The tech support is very good, offering immediate responses and chat options.
We receive service that is more than adequate, even exceptional.
They answer all my questions and share guidance on using DataRobot scripts if certain functionalities are not available in the UI.
Being cloud-hosted enables automatic resource scaling, which supports collaboration across teams.
The customer support from DataRobot is proactive and responsive.
The reason we wanted to use our own Kubernetes cluster was to do automatic scaling for utilized resources, allowing us to save.
We have built approximately 1,100 interfaces for one customer, which is a significant achievement.
DataRobot's scalability is very strong and grows with my organization's needs.
DataRobot is very stable.
The integration landscape has become complex, and having a data strategy with unified data models would make integration easier for any platform, including Boomi.
A significant area for improvement is version control.
The ETL aspect of Boomi iPaaS is not mature enough at the moment.
DataRobot is a UI-based tool, which means it cannot provide all the features I might manually implement through notebooks or Python.
There is a lack of transparency in the models; sometimes it feels like a black box.
Another improvement that DataRobot needs is integrating the capability to modify the whole pipeline with Python.
While pricing is relative, compared to others, it is favorable.
The pricing for Boomi iPaaS is reasonable, costing around $6,000 per year.
My experience with pricing, setup cost, and licensing indicates it is a little bit high but still reasonable.
The setup cost was minimal because it's cloud-hosted, eliminating the need for heavy on-premises infrastructure, allowing us to start using it immediately after purchase.
My experience with pricing, setup cost, and licensing reveals that the price points can be improved and DataRobot is not so cost-effective, especially for smaller organizations.
It automates the creation and population of a data warehouse, reducing the need to write SQL scripts and procedures.
I can definitely say I see specific positive outcomes after moving to Boomi iPaaS, noting improvements in efficiency, cost savings, and faster project delivery.
The most valuable features of Boomi are the integration capabilities, the Data Hub product, and the UDI integration.
By automating highly technical aspects like model comparison, DataRobot enhances productivity and reduces project timelines from three months to less than one month.
DataRobot has positively impacted our organization in many ways. First, it has improved efficiency; tasks such as model testing, feature engineering, and predictions that used to take us days or weeks can now be accomplished in hours.
When business leaders ask for the fastest possible solution, DataRobot is our go-to platform.
| Product | Mindshare (%) |
|---|---|
| Boomi iPaaS | 0.2% |
| DataRobot | 0.5% |
| Other | 99.3% |

| Company Size | Count |
|---|---|
| Small Business | 10 |
| Midsize Enterprise | 4 |
| Large Enterprise | 16 |
| Company Size | Count |
|---|---|
| Small Business | 2 |
| Midsize Enterprise | 1 |
| Large Enterprise | 5 |
The Boomi AtomSphere integration platform as a service (iPaaS) supports all your application integration processes – between cloud platforms, software-as-a-service applications, and on-premises systems. Your entire team has online access to a powerful range of integration and data management capabilities, that can be realized in a fraction of the time of legacy middleware technologies.
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.
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