DataRobot vs. Google Cloud AI

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
DataRobot
Score 8.2 out of 10
N/A
The DataRobot AI Platform is presented as a solution that accelerates and democratizes data science by automating the end-to-end journey from data to value and allows users to deploy AI applications at scale. DataRobot provides a centrally governed platform that gives users AI to drive business outcomes, that is available on the user's cloud platform-of-choice, on-premise, or as a fully-managed service. The solutions include tools providing data preparation enabling users to explore and…N/A
Google Cloud AI
Score 8.7 out of 10
N/A
Google Cloud AI provides modern machine learning services, with pre-trained models and a service to generate tailored models.N/A
Pricing
DataRobotGoogle Cloud AI
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
DataRobotGoogle Cloud AI
Free Trial
YesNo
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
DataRobotGoogle Cloud AI
Considered Both Products
DataRobot
Chose DataRobot
DataRobot is more friction-less and comprehensive enough to tackle a majority of our use cases.
Chose DataRobot
I've done machine learning through python before, however having to code and test each model individually was very time consuming and required a lot of expertise. The data Robot approach, is an excellent way of getting to a well placed starting point. You can then pick up the …
Chose DataRobot
DataRobot outperforms SPSS in terms of speed and efficiency. While I continue to rely on SPSS for tasks like data cleanup and data engineering, I have noticed that DataRobot significantly excels when it comes to building models. Its speed and user-friendly interface make it the …
Chose DataRobot
Comparable to H2O but my company chose DataRobot so that's why I'm using it. Pricing is reasonable and the feature coverage is probably better from an end-to-end perspective. DataRobot has less flexibility than Amazon SageMaker but is a lot simpler to use, which again for a …
Chose DataRobot
Alteryx is more of data processing only with user-friendly interface for non-technical users. Data Robot is more than that and can provide intelligent models for machine learning.
Chose DataRobot
Better user interface and collaborative capabilities to enable easy explanation among business partners with the platforms insightful visualizations.
Chose DataRobot
I have not used any comparable products. Compared to using commonly available open source libraries for machine learning, DataRobot automatically manages the partition of data, pre-processing of data, construction of processing pipelines and the evaluation of models on an …
Chose DataRobot
Robots vs. Robots. It was necessarily me who selected DR instead, but having used both, I find that DR is better suited to our needs and is just more accessible. You don't need to be a complete expert in the field to be able to use DR's platform, more just being able to …
Chose DataRobot
When we ran the purchase process, two factors were critical: price of course and the customer success service as we were new in this datascience world. H2O and DataRobot were the finalists (Dataiku too expensive for our needs), but we decide to choose DataRobot as they give us …
Chose DataRobot
DataRobot provided the perfect balance of features and price points. The other tools we tried were very expensive and provided extra things that we really didn't need. Some of the other tools also required you to host them on a server at your institution or pay for their cloud …
Chose DataRobot
We consistently return to DataRobot for its ease of use and ability to get the job done without major hurdles. Thus far, we just haven't found that in other products.
H2O.ai (Driverless AI): several test models did not complete, and H2O.ai team could not explain why.
Sagemaker:…
Chose DataRobot
At the time DataRobot provided a more automatized and complete variety of pre-trained models and ready-to-use solutions.
Chose DataRobot
We've just had an intro but DataRobot is much more specialized in predictive analytics. Dataiku seems for me a platform that aims to cover a little bit all the steps or processes of a D&A team and with this approach, you may be doing a trade-off in quality and power
Chose DataRobot
The ease of use has been one significant factor and productionizing is the second one that stands out. Comparision across various models
Chose DataRobot
DataRobot is the product that seemed to have the most professional platform all in all. It was also the best one for the second part of the model development, which is monitoring what the model is doing in production and governing what that model was doing, giving us the …
Chose DataRobot
Amazon SageMaker and Python IDLE
Chose DataRobot
DataRobot is far easier to implement and much easier to master. The license cost considerations are more economically favorable also.
Google Cloud AI
Chose Google Cloud AI
These are basic tools although useful, you can't simply ignore them or say they are not good. These tools also have their own values. But, Yes, Google is an advanced one, A king in the field of offering a wide range of tools, quality, speed, easy to use, automation, prebuild, …
Chose Google Cloud AI
This product has given us the type of space and security that we need to store data. Other companies have given us so many problems when it comes to losing power and losing data and with over 15 thousand consumers we need to make sure all of our stuff is safe and not lost.
Chose Google Cloud AI
Amazon AWS AI provides is better than Google Cloud AI if you are looking for better support to customize the AI / ML algorithms being used. Google Cloud AI does a better job than Microsoft Azur ML when customization is not needed but speed to market is needed. IBM Watson is on …
Chose Google Cloud AI
Google's documentation for their AI and Machine Learning products is a bit more straightforward and still much easier to onboard into compared to the Azure Machine Learning and other AI products. Additionally, Google's Cloud AI products provide more comprehensive specific …
Chose Google Cloud AI
Google cloud AI stacks up comprehensively and competitively with other tech providers. Their scientists are smarter, make bigger leaps forward in design, and they are always cutting edge in methods to boost productivity and skip to the next generation. We need machine AI, as we …
Chose Google Cloud AI
We decided to use the Google tool because it is better suited to our needs as a team. The other tools seemed very interesting to us, but what made us choose the Google tool is that with the others we would have had to have chosen another tool from the same provider in order to …
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DataRobotGoogle Cloud AI
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Score 7.7 out of 10
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Score 7.7 out of 10
Medium-sized Companies
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Score 10.0 out of 10
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User Ratings
DataRobotGoogle Cloud AI
Likelihood to Recommend
8.6
(0 ratings)
8.0
(0 ratings)
Likelihood to Renew
6.3
(0 ratings)
10.0
(0 ratings)
Usability
-
(0 ratings)
8.0
(0 ratings)
Support Rating
8.2
(0 ratings)
7.3
(0 ratings)
Implementation Rating
-
(0 ratings)
10.0
(0 ratings)
User Testimonials
DataRobotGoogle Cloud AI
Likelihood to Recommend
DataRobot can be used for risk assessment, such as predicting the likelihood of loan default. It can handle both classification and regression tasks effectively. It relies on historical data for model training. If you have limited historical data or the data quality is poor, it may not be the best choice as it requires a sufficient amount of high-quality data for accurate model building.
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Google Images analysis model is a good one and I think is very useful in our case of detections. Speech AI is also a good one. I can only recommend Google Cloud AI API and the model for that second will be SpeechKit by Yandex both these tools have exceptional values one can utilise to enhance their projects.
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Pros
  • The breadth of models available to use is helpful and allows much more analytical power than programming them all yourself.
  • The built-in variable diagnostics are helpful when testing large variable sets to see which perform the best.
  • Many of the adjustments on the models are easy to use/it's easy to re-run and kick off new models as you want to try new things.
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  • Smart reply and its AI suggestions make the organization think more carefully about their e-mail responses in Gmail. We were skeptical at first but it really works well for many instances.
  • We do a lot of business and contracts in Western Europe and South America, so the translate solutions make this much easier for our banking paperwork.
  • When we go to meetings or during a meeting, we often use the Google voice search to save time on research and filtering ideas or analysis.
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Cons
  • Further improvements to their text analysis tool, to be more like the Qualtrics text analysis tool, would be a great addition. Qualtrics has templates built into their text analysis tool for customer service, quality control, etc, and will automatically slot your text responses into categories associated with certain sub areas of those larger categories.
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  • Hard to find what to use - To find the right products, you need look closely at the details of each API, and find which suits your purposes. This can be easily fixed by creating a main page that details all of the products simply.
  • Expensive - The API costs can quickly add up, especially during the setup process and as engineers figure out the usage of the API.
  • No playground or training - There is a lack of an "API playground" or training sessions that could make onboarding engineers to this API much easier.
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Likelihood to Renew
DataRobot presents a machine-learning platform designed by data scientists from an array of backgrounds, to construct and develop precise predictive modeling in a fraction of the time previously taken. The tech invloved addresses the critical shortage of data scientists by changing the speed and economics of predictive analytics. DataRobot utilizes parallel processing to evaluate models in R, Python, Spark MLlib, H2O and other open source databases. It searches for possible permutations and algorithms, features, transformation, processes, steps and tuning to yield the best models for the dataset and predictive goal.
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We are extremely satisfied with the impact that this tool has made on our organization since we have practically moved from crawling to walking in the process of generating information for our main task to investigate in the field through interviews. With the audio to text translation tool there is a difference from heaven to earth in the time of feeding our internal data.
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Usability
No answers on this topic
I give 8 because although it´s a tool I really enjoy working with, I think Google Cloud AI's impact is just starting, therefore I can visualize a lot/space of improvements in this tool. As an example the application of AI in international environments with different languages is a good example of that space/room to improve.
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Support Rating
As I am writing this report I am participating with Datarobot Engineers in an complex environment and we have their whole support. We are in Mexico and is not common to have this commitment from companies without expensive contract services. Installing is on premise and the client does not want us to take control and they, the client, is also limited because of internal IT regulations ,,, soo we are just doing magic and everybody is committed.
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Every rep has been nice and helpful whenever I call for help. One of the systems froze and wouldn't start back up and with the help of our assigned rep we got everything back up in a timely manner. This helped us not lose customers and money.
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Implementation Rating
No answers on this topic
In fact, you only need the basic tech knowledge to do a Google search. You need to know if your organization requires it or not,. our organization required it. And that is why we acquired it and solved a need that we had been suffering from. This is part of the modernization of an organization and part of its growth as a company.
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Alternatives Considered
I've done machine learning through python before, however having to code and test each model individually was very time consuming and required a lot of expertise. The data Robot approach, is an excellent way of getting to a well placed starting point. You can then pick up the model from there and fine tune further if you need.
Read full review
Google's documentation for their AI and Machine Learning products is a bit more straightforward and still much easier to onboard into compared to the Azure Machine Learning and other AI products. Additionally, Google's Cloud AI products provide more comprehensive specific use-cases that are API-optimized, and easier to integrate into existing scripts and backends.
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Return on Investment
  • We have been able to cut costs by not buying leads that we will not be able to sell on
  • We have been able to deploy loan eligibility reporting which brought in new business
  • We have been able to improve the performance of our credit providers and our partners which has helped to retain business
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  • Positive impact on ROI due to reduction in staff needed to build, deploy and manage a AI workload pipeline.
  • Positive impact on the business by moving to OpEx without need for upfront CapEx investment.
  • Improvement in time to analyze the data (structured and unstructured), increasing the business's ability to act based on AI results.
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ScreenShots

DataRobot Screenshots

Screenshot of Decision FlowsScreenshot of No Code App BuilderScreenshot of AI AppsScreenshot of Automated Time SeriesScreenshot of MLOpsScreenshot of Model Insights