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Darwin vs Google Cloud Datalab comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 5, 2024

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

Darwin
Ranking in Data Science Platforms
25th
Average Rating
8.0
Reviews Sentiment
6.7
Number of Reviews
8
Ranking in other categories
No ranking in other categories
Google Cloud Datalab
Ranking in Data Science Platforms
18th
Average Rating
7.8
Reviews Sentiment
6.4
Number of Reviews
6
Ranking in other categories
Data Visualization (16th)
 

Mindshare comparison

As of June 2026, in the Data Science Platforms category, the mindshare of Darwin is 1.5%, up from 0.3% compared to the previous year. The mindshare of Google Cloud Datalab is 1.7%, up from 1.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Mindshare Distribution
ProductMindshare (%)
Google Cloud Datalab1.7%
Darwin1.5%
Other96.8%
Data Science Platforms
 

Featured Reviews

AC
Founder at Helio Summit
Empowers SMEs to build solutions and interface them with the existing business systems, products and workflows.
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.
LJ
System Architect at UST Global España
dashboards are good and data visualization is more meaningful for the end-user
Access is always via URL, and unless your network is fast, it would be a little tough in India. In India, if we had a faster network, it would be easier. In a big data environment, like when forcing your database with over a billion records, it can be tough for the end-user to manage the data. You need to have a single entity system in each environment. It's not because of GCP, but it would be great to have options like MongoDB or other similar tools in GCP. Then, we wouldn't always need to connect to the cloud and execute SQL queries. Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated. Once the data is collected, it should be easily sorted.

Quotes from Members

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

Pros

"The thing that I find most valuable is the ability to clean the data."
"Our main goal is to transform data into knowledge and Darwin is definitely helping us to do that faster."
"I find it quite simple to use. Once you are trained on the model, you can use it anyway you want."
"It did help us to convert data into knowledge."
"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."
"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."
"The solution helps with the automatic assessment of the quality of datasets, such as missing data points or incorrect data types."
"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"All of the features of this product are quite good."
"Google Cloud Datalab is very customizable."
"For me, it has been a stable product."
"The APIs are valuable."
 

Cons

"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."
"We have used Darwin as a complement to other tools like R and SPSS to get the accuracy we want."
"Darwin is stable, but it just doesn't provide the functionality that an analyst would need."
"Our main data repository is on AWS. The trouble we are having is that we have to download the data from our repository to bring it into Darwin. It would be great if there was an API to connect our repository to Darwin."
"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."
"If you give a lot of data to Darwin, sometimes it can hang."
"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."
"We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience."
"The product must be made more user-friendly."
"There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"The interface should be more user-friendly."
"Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated."
 

Pricing and Cost Advice

"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."
"I believe our cost is $1,000 per month."
"As far as I understand, my company is not paying anything to use the product."
"It is affordable for us because we have a limited number of users."
"The pricing is quite reasonable, and I would give it a rating of four out of ten."
"The product is cheap."
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Top Industries

By visitors reading reviews
Construction Company
19%
Financial Services Firm
15%
Manufacturing Company
10%
University
7%
Financial Services Firm
19%
Construction Company
18%
University
7%
Outsourcing Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business6
Large Enterprise2
No data available
 

Questions from the Community

Ask a question
Earn 20 points
What needs improvement with Google Cloud Datalab?
Access is always via URL, and unless your network is fast, it would be a little tough in India. In India, if we had a faster network, it would be easier. In a big data environment, like when forcin...
What is your primary use case for Google Cloud Datalab?
It's for our daily data processing, and there's a batch job that executes it. The process involves more than ten servers or systems. Some of them use a mobile network, some are ONTAP networks, and ...
What advice do you have for others considering Google Cloud Datalab?
Overall, I would rate it a nine out of ten. Google Cloud is very good. Once you go through the features of Google Cloud, it's a good idea to get a GCP certification so you have an idea of how it ca...
 

Comparisons

 

Overview

 

Sample Customers

Hunt Oil, Hitachi High-Tech Solutions
Information Not Available
Find out what your peers are saying about Darwin vs. Google Cloud Datalab and other solutions. Updated: June 2026.
900,644 professionals have used our research since 2012.