Darwin Reviews

Name: Darwin
Brand: SparkCognition
Rating: 4.0 (8 reviews)

4.0 out of 5

8 reviews
100% willing to recommend

What is Darwin?

SparkCognition builds leading artificial intelligence solutions to advance the most important interests of society. We help customers analyze complex data, empower decision making, and transform human and industrial productivity with award-winning machine learning technology and expert teams focused on defense, IIoT, and finance.

Get the Darwin Buyer's Guide and find out what your peers are saying about Darwin, Databricks, Microsoft Azure Machine Learning Studio and more!

Darwin is the #26 ranked solution in top Data Science Platforms. PeerSpot users give Darwin an average rating of 8.0 out of 10. Darwin is most commonly compared to Databricks: Darwin vs Databricks.

Helped 861,390 peers since 2012

Featured Darwin reviews

AaronCooke

Founder at Helio Summit

There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do. Because it's so much better than traditional methods, we don't get a ton of complaints of, "Oh, we wish we could do that." Most people are happy to see that they can build models that quickly, and that it can be done by the people who actually understand the problem, i.e. SMEs, rather than having to rely on data scientists. There's a small learning curve, but it's shorter for an SME in a given industry to learn Darwin than it takes for data scientists to learn industry-specific problems. The industry I work in deals with tons and tons of data and a lot of it lends itself to Darwin-created solutions. Initially, there were some limitations around the size of the datasets, the number of rows and number of columns. That was probably the biggest challenge. But we've seen the Darwin product, over time, slowly remove those limitations. We're happy with the progress they've made.

Read full review

NataliaCueto

Head of Technology at CapitalTech

The challenge is very big toward making models operational or to industrialize them. E.g., what we want to do is to make unique credit models for each customer. So, we are preparing the types of customers who we can try new credit models on Darwin. But, I see this still very challenging to be able to get the data sets so Darwin can work. At this point, we are working with it to get the data sets ready for Darwin. However, once they are in Darwin, I believe we will not have any problems and will have very good results, just as we have had for the risk portfolio management. We are trying to aim it for a more specific group of clients to target them more specifically. Right now, we have been using Darwin with clients that we don't want. That's how we have been reducing our delinquency index. Darwin is helping us identify clients that we need to close a relationship with, but we need it now to tell us the clients where we should be aiming to give them new products, new opportunities, or go to the market and reach new clients. The dashboards and displaying of the data needs improvement. Currently, only IT and business intelligence people are using the results we get from Darwin, but less sophisticated areas in technology could also benefit from it if we had more user-friendly dashboards. People get scared, and they think, "We will need to run something in Python," which is not the case. We could use more user-friendly dashboards so everyone could use them. However, they have already let me know that Darwin is already working on the dashboard implementation so our commercial areas can have access to the data in a more user-friendly way. This is great because it is a very important area of opportunity. We want to be able to test different updates. E.g., we've been waiting for the user-friendly dashboards since August. We really want to start working with that but don't know when it will be released. The people at SparkCognition told me that as soon as they were ready that they would contact us, so we could have a workshop for this. However, they haven't contacted us for this yet.

Read full review

reviewer1244334

Business Intelligence Director at a financial services firm with 51-200 employees

We have used Darwin as a complement to other tools like R and SPSS to get the accuracy we want. This is one thing we've told the people at SparkCognition and they're working on it. In these kinds of situations, we don't use Darwin 100 percent because of that limitation. The solution does help us towards making models operational but we are not at the place we want to be. We want models giving the answer to whether we should make a loan or not, but we are not at that point yet. It still has some limitations. These are things we have given as feedback and they're working on them. Also, it would be great to have a solution that can organize the models. Right now, when there are a lot of models, they are disorganized. In the future, when we have more models, it will be more complicated to find the things that we are working on. It's about the user interface. You have the screen where you can look for other models but you can't organize models by name or by date. Something they are working on, which is great, is to have an API that can access data directly from the source. Currently, we have to create a specific dataset for each model. But it would be great to have an API that gives us the opportunity to have a connection with our datasets or data lakes for each one, and a specific file for each model. Sometimes, you find that you have to add a new variable and you have to create a new file with that variable instead of having a connection via API to your datasets. We have also asked SparkCognition that instead of automatic suggestions for addressing dataset issue, things should be defined by the user instead. There have been occasions where we have numerical data and Darwin has suggested using a nominal variable. We would prefer to define categories ourselves, instead of the recommendation that Darwin makes.

Read full review

Darwin mindshare

As of July 2025, the mindshare of Darwin in the Data Science Platforms category stands at 0.4%, up from 0.2% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Data Science Platforms

PeerResearch reports based on Darwin reviews

Type	Title	Date
Category	Data Science Platforms	Jul 11, 2025	Download
Product	Reviews, tips, and advice from real users	Jul 11, 2025	Download
Comparison	Darwin vs Databricks	Jul 11, 2025	Download
Comparison	Darwin vs KNIME	Jul 11, 2025	Download
Comparison	Darwin vs Amazon SageMaker	Jul 11, 2025	Download

Title	Rating	Mindshare	Recommending
Databricks	4.1	15.9%	96%	91 interviews Add to research
Microsoft Azure Machine Learning Studio	3.9	5.1%	93%	62 interviews Add to research

Valuable Features

Darwin offers valuable features such as data cleaning and automated model generation, significantly reducing modeling time. Its interface is user-friendly, allowing easy data analysis and model building. Users appreciate the accuracy in predictions and the ability to assess dataset quality automatically. It offers interactive suggestions for data improvement and integrates well with existing systems via REST API. Partner networks enhance its capabilities, making machine learning accessible to non-data scientists.

"The most valuable feature is the model-generation. With a nice dataset, Darwin gives you a nice model. That's a really nice feature because, if we're doing that ourselves, it's trial and error; we change the parameters a little and try again. We save time by just giving the dataset to Darwin and letting Darwin generate a model. We find the models it generates are good; better than we can generate."
"In terms of streamlining a lot of the low-level data science work, it does a few things there."
"The key feature is the automated model-building. It has a good UI that will let people who aren't data scientists get in there and upload datasets and actually start building models, with very little training. They don't need to have any understanding of data science."

Room for Improvement

Darwin users suggest improvements such as an API for direct data integration to save time on downloading and uploading. Users would like non-supervised models and a more user-friendly interface, especially in the dashboard area. The automatic functionality needs enhancement, and users desire more control over model organization and dataset definitions. There's feedback that tutorials and user training require enhancement, with emphasis on better transparency regarding AI operations and faster UI performance.

"An area where Darwin might be a little weak is its automatic assessment of the quality of datasets. The first results it produces in this area are good, but in our experience, we have found that extra analysis is needed to produce an extra-clean set of data."
"The Read Me's and the tutorials need to be greatly improved to get customers to understand how things work. It might be helpful to have some sample data sets for people to play around with, as well as some tutorial videos. It was very hard to find information on this in the time crunch that we had, to see how it worked and then make it work, while interfacing with folks at SparkCognition."
"There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do."

ROI

Many entities see increased efficiency and productivity with Darwin, especially in tasks that previously took days now completed in minutes. In six months, it helped avoid losing six million pesos. While some have yet to calculate their return, some users note it offers value by enabling machine learning that would otherwise need significant resources. Despite challenges in quantifying ROI, they expect returns as more models and analyses are implemented.

Pricing

Enterprise users of Darwin find the licensing cost significant, particularly in markets like Mexico. However, many see it as a valuable investment due to potential disruption benefits. While most only incur the licensing fee, which some report as $1,000 monthly, additional costs may arise for custom system integration. Opinions suggest that Darwin delivers great value, as it often proves more cost-effective compared to hiring data scientists. No unexpected fees have been reported.

"I believe our cost is $1,000 per month."
"In just six months, we calculated six million pesos that we have prevented in revenue from going away with another customer because of this solution. Thanks to Darwin, we didn't lose those six million pesos."
"The license cost is not cheap, especially not for markets like Mexico. But sometimes, you do have to make these leap of faith for some tools to see if they can get you the disruption that you are aiming for. The investment has paid off for us very well."

Popular Use Cases

Organizations use Darwin primarily for predicting payment defaults by clients and risk management in lending. They employ it for client segmentation, assessing delinquency probabilities, analyzing current client trends to prevent defaults, and generating models from data analytics. It's also used for PoC projects in industries like oil and gas for data classification. Darwin aids in building predictive models, empowering SMEs to classify data without heavily relying on data scientists, and often utilizes cloud solutions for its operations.

Service and Support

Technical support for Darwin is described as responsive and fast. Users have had login issues, but the team quickly addressed them. They find the support to be helpful, with specific personnel offering immediate assistance. Customers have also appreciated the help in understanding and using the service for their projects. The customer success team is praised for quick responsiveness, with sales and support being described as outstanding. SparkCognition team members have been particularly helpful and resourceful.

Deployment

Many found Darwin's setup complex, especially when new to AI tools, needing weeks to months for deployment. Some required extensive training and guidance, while others found it straightforward, deploying in hours with minimal technical needs. SDK documentation was often unclear. Prior experience with machine learning aided some teams. Various departments were involved, from business analysts to IT, with goals ranging from loan decisions to process improvement. Consultants played a critical role in integrating specific needs.

Scalability

Darwin demonstrates strong scalability, though challenges like data organization and processing speed are noted. Users appreciate its capability to manage complex datasets and deliver accurate predictions. Some teams plan to expand its use, aiming to extend access to commercial areas for enhanced decision-making. Deployment models help mitigate traditional scalability issues, though internal adjustments are needed for broader integration. Certain users report occasional system hang-ups with large datasets.

Stability

Stability of Darwin has improved over versions. Users report it is stable, with availability over 99%. While stability has been good, there are occasional issues, such as unclear model completion and login problems during fixes. Users mention improved stability after early limitations are resolved. Some experienced rare crashes when processes didn't handle unexpected actions, though support was responsive in addressing concerns. Overall, users generally find Darwin reliable with minor suggestions for further enhancement.

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

Download our Darwin Buyer's Guide for additional reliable information.