

Find out in this report how the two AI Finance & Accounting solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
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.
On average, we're saving about 10 to 15 hours per project.
It saved a lot of money, with 50 to 60% of our cost saved, especially through automation.
I have more time to work on meaningful tasks since automation has been very helpful in automating repetitive and time-consuming tasks.
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.
They provide very detailed responses that enable us to handle any issues effectively.
The response times were slow to turn around.
DataRobot's scalability is very strong and grows with my organization's needs.
It is scalable from the solution perspective.
DataRobot is very stable.
I found it to be high on stability, and I would rate it at nine.
The solution is generally stable, though we have faced issues with increased transaction loads causing latency and occasional hang-ups.
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.
It was not developed in a consumption-based manner, however, rather in a fixed-price licensing model that did not account for volumes.
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.
The pricing model was not modern, as it wasn't designed on a consumption basis or as a service basis.
The licensing cost can be a bit expensive compared to its competitors.
Overall, my experience with pricing, setup cost, and licensing is that for large organizations and medium organizations, it is very cost-effective.
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.
The control room feature, which allows me to run and monitor the automation, is to be the most useful.
| Product | Mindshare (%) |
|---|---|
| SS&C Blue Prism | 1.3% |
| DataRobot | 1.7% |
| Other | 97.0% |


| Company Size | Count |
|---|---|
| Small Business | 2 |
| Midsize Enterprise | 1 |
| Large Enterprise | 5 |
| Company Size | Count |
|---|---|
| Small Business | 3 |
| Large Enterprise | 7 |
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.
SS&C Blue Prism, renowned for its language capabilities and workflow design, supports detailed automation building, enhancing productivity. Despite some challenges like cost and limited integration, it offers substantial potential in automating diverse processes.
SS&C Blue Prism offers strong capabilities in document reading and a straightforward workflow design, making it accessible with basic BPM knowledge. Detailed automation design in the studio and effective monitoring in the control room are notable features. While facing higher costs and a steeper learning curve, it supports process mining and generative AI initiatives, crucial for industries aiming at transformation and activation services. Limited external system integration and lack of agile delivery encourage a strategic approach in its deployment.
What are the key features?
What ROI should users expect?
SS&C Blue Prism finds its application across industries. In service industries, it automates repetitive tasks while supporting migration projects. Within the insurance sector, it helps automate claims handling and pricing by integrating data efficiently. Companies use it when transitioning processes, such as upgrading systems from older versions to new applications.
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