Anaconda vs Darwin comparison

Anaconda and SparkCognition are both solutions in the Data Science Platforms category. Anaconda is ranked #12 with an average rating of 8.8, while SparkCognition is ranked #27. Anaconda holds a 2.1% mindshare in Data Science Platforms, compared to SparkCognition’s 0.3% mindshare. Additionally, 95% of Anaconda users are willing to recommend the solution, compared to 100% of SparkCognition users who would recommend it.

Anaconda

Read 18 Anaconda reviews

1,734 Views
1,421 Comparison Views

95% willing to recommend

Darwin

Read 8 Darwin reviews

330 Views
179 Comparison Views

100% willing to recommend

Anaconda

Darwin

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 5, 2024

Anaconda and Darwin compete in data science platforms. Anaconda holds an advantage with its broad open-source ecosystem, while Darwin leverages its strength in automated machine learning.

Features: Anaconda offers a comprehensive package management system, Jupyter notebooks integration, and extensive library support. Its virtual environment capabilities and active community support are also valuable. Darwin provides automated model generation, accurate predictive analytics, and user-friendly automation. Its REST API for integration and data quality assessment features enhance its offering.

Room for Improvement: Anaconda could improve by offering more pre-built models for specific use cases and refining its interface for easier navigation. Additional cloud deployment options and simplified customization processes would be beneficial. Darwin would benefit from enhancing manual model control and improving initial model accuracy. Enhanced dataset cleaning suggestions and expanded partner network solutions could further optimize its performance.

Ease of Deployment and Customer Service: Anaconda excels with efficient deployment across local environments, comprehensive documentation, and community support. Darwin focuses on cloud deployment, highlighting automation and offering responsive technical support. Anaconda appeals to traditional environments, while Darwin supports rapid cloud scaling.

Pricing and ROI: Anaconda's lower setup cost due to its open-source nature provides exceptional value for experimental needs. Darwin's higher initial cost is offset by its advanced automation features, promising improved ROI for predictive analytics. Both products require considering budgetary constraints and specific business goals.

To learn more, read our detailed Anaconda vs. Darwin Report (Updated: January 2025).

Buyer's Guide

Anaconda vs. Darwin

January 2025

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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

Anaconda

Ranking in Data Science Platforms

12th

Average Rating

8.2

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Darwin

Ranking in Data Science Platforms

27th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of January 2025, in the Data Science Platforms category, the mindshare of Anaconda is 2.1%, down from 2.3% compared to the previous year. The mindshare of Darwin is 0.3%, down from 0.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Rohan Sharma

AI/ML Co-Lead at Developer Student Clubs - GGV

Provides all the frameworks and makes it easy to create environments for multiple projects

The best thing is that it provides all the frameworks and makes it easy to create environments for multiple projects using Anaconda. It is easy for a beginner to learn to use Anaconda. Comparatively, it is easier than using virtual environments or other environments because of the Conda environment. However, there are many things in Anaconda that people need to be aware of, so it can be challenging.

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AaronCooke

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.

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Quotes from Members

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

Pros

"It helped us find find the optimal area for where our warehouse should be located."

"The notebook feature is an improvement over RStudio."

"The product is responsive, sleek and has a beautiful interface that is pleasant to use. It helps users to easily share code."

"It's interesting. It's user friendly. That's what makes it outstanding among the others. It has a collection of R, Python, and others. Their platform strategy has a collection of many other visualization tools, apart from Spyder and RStudio, which is really helpful for data science. For any data science professional, Anaconda is really handy. It has almost all the tools for data science."

"The documentation is excellent and the solution has a very large and active community that supports it."

"The solution is stable."

"The most advantageous feature is the logic building."

"The most valuable feature is the set of libraries that are used to support the functionality that we require."

More Anaconda pros

"Darwin has increased efficiency and productivity for our company. With our risk management team, there were models that took them more than three days to process each, only to see the outcome. Now, it takes minutes for Darwin to process the current model. So, we can have it in minutes. We don't have to wait three days for all the models to be tested, then make a decision."

"I liked the data checking feature where it looks at your data and sees how viable it is for use. That's a really cool feature. Automatic assessment of the quality of datasets, to me, seems very valuable."

"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."

"In terms of streamlining a lot of the low-level data science work, it does a few things there."

"The thing that I find most valuable is the ability to clean the data."

"The solution helps with the automatic assessment of the quality of datasets, such as missing data points or incorrect data types."

"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."

"I find it quite simple to use. Once you are trained on the model, you can use it anyway you want."

Cons

"One thing that hurts the product is that the company is not doing more to advertise it as a solution and make it more well known."

"It also takes up a lot of space."

"I think that the framework can be improved to make it easier for people to discover and use things on their own."

"Anaconda should be optimized for RAM consumption."

"I think better documentation or a step-by-step guide for installation would help, especially for on-premise users."

"The interface could be improved. Other solutions, like Visual Studio, have much better UI."

"Having a small guide or video on the tool would help learn how to use it and what the features are."

"Anaconda can't handle heavy workloads."

More Anaconda cons

"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 it to get the data sets ready for Darwin."

"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 are issues around the ethics of artificial intelligence and machine learning. You need to have a lot of transparency regarding what is going on under the hood in order to trust it. Because so much is done under the hood of Darwin, it is hard to trust how it gets the answers it gets."

"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."

"There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do."

"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."

"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 analyze function takes a lot of time."

Pricing and Cost Advice

"The product is open-source and free to use."

"My company uses the free version of the tool. There is also a paid version of the tool available."

"The tool is open-source."

"The licensing costs for Anaconda are reasonable."

"Anaconda is free to use, but in terms of hardware costs, you might need heavy GPUs to run CUDA and other demanding tasks."

"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."

"As far as I understand, my company is not paying anything to use the product."

"I believe our cost is $1,000 per month."

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

20%

Computer Software Company

10%

Manufacturing Company

University

Computer Software Company

25%

Real Estate/Law Firm

15%

Financial Services Firm

13%

Educational Organization

11%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about Anaconda?

The tool's most valuable feature is its cloud-based nature, allowing accessibility from anywhere. Additionally, using Jupyter Notebook makes it easy to handle bugs and errors.

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What is your experience regarding pricing and costs for Anaconda?

Anaconda does not require a pricing structure, and it is available as an open-source tool. The features of Python, Jupyter, and others are free to use.

See all answers

What needs improvement with Anaconda?

Anaconda consumes a significant amount of processing memory when working on it. This is something that needs improvement as it can impact performance.

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Darwin

January 2025

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Learn More

Anaconda

SparkCognition

Overview

Anaconda makes it easy for you to install and maintain Python environments. Our development team tests to ensure compatibility of Python packages in Anaconda. We support and provide open source assurance for packages in Anaconda to mitigate your risk in using open source and meet your regulatory compliance requirements.

Python is the fastest growing language for data science. Anaconda includes 720+ Python open source packages and now includes essential R packages. This powerful combination allows you to do everything you want from BI to advanced modeling on complex Big Data

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.

Sample Customers

LinkedIn, NASA, Boeing, JP Morgan, Recursion Pharmaceuticals, DARPA, Microsoft, Amazon, HP, Cisco, Thomson Reuters, IBM, Bridgestone

Hunt Oil, Hitachi High-Tech Solutions

Buyer's Guide

Anaconda vs. Darwin

January 2025

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