Amazon SageMaker vs Anaconda comparison

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Amazon SageMaker

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Executive SummaryUpdated on Dec 5, 2024

Anaconda and Amazon SageMaker are competitors in the data science and machine learning sector, with SageMaker edging out in power due to its advanced features and AWS integration.

Features: Anaconda is notable for package dependency management, virtual environment creation, and a wide array of libraries for data analysis. Amazon SageMaker stands out with built-in data labeling, automatic model tuning, and seamless AWS integration, facilitating complete machine learning workflows.

Room for Improvement: Anaconda could enhance its features by providing more pre-built solutions and expanding Jupyter Notebook integrations. Additionally, improving the learning curve for beginners would be beneficial. SageMaker can improve user interface simplicity and broaden support for third-party tools. Enhancements in cost management transparency and usability documentation could also be advantageous.

Ease of Deployment and Customer Service: Anaconda offers straightforward deployments with robust community support, suitable for individual data scientists. Meanwhile, Amazon SageMaker provides complex, scalable deployments supported by AWS's extensive customer service, ideal for enterprise applications demanding scalability.

Pricing and ROI: Anaconda's free distribution model ensures lower setup costs, appealing to cost-conscious users with a swift ROI for small projects. Amazon SageMaker, with its pay-as-you-go pricing, incorporates higher initial costs, but its extensive features offer substantial long-term ROI, especially beneficial for large-scale projects.

To learn more, read our detailed Amazon SageMaker vs. Anaconda Report (Updated: March 2025).

Amazon SageMaker vs. Anaconda

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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

Amazon SageMaker

Ranking in Data Science Platforms

3rd

Average Rating

7.8

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

AI Development Platforms (5th)

Anaconda

Ranking in Data Science Platforms

11th

Average Rating

8.2

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of April 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 7.3%, down from 9.8% compared to the previous year. The mindshare of Anaconda is 2.1%, down from 2.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Hemant Paralkar

Lead Consultant at Saama

Improves team collaboration with advanced feature sharing but needs a better user experience

Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker. This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background. Additionally, dealing with frequent UI updates can be challenging, especially for infrastructure architects like myself. It involves effort to migrate to new UIs, making the updates not seamless. User auditing requires enhancements as tracking operations performed by users can be difficult due to dynamic IP validation and role propagation.

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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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Quotes from Members

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

Pros

"Allows you to create API endpoints."

"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."

"The feature I found most valuable is the data catalog, as it assists with the lineage of data through the preparation pipeline."

"The tool makes our ML model development a bit more efficient because everything is in one environment."

"Amazon SageMaker is highly valuable for managing ML workloads. It connects to AWS cloud resources, making it easy to deploy algorithms and collaborate using tools like GitLab. It offers a wide range of Python libraries and other necessary tools for modelling and algorithms."

"SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project."

"The intuitive interface and streamlined user experience make it easy to navigate and set up various tools like Visual Studio Code or Jupyter Notebook."

"I have seen a return on investment, probably a factor of four or five."

More Amazon SageMaker pros

"It has a lot of functionality available, supports many libraries, and the developers are continually improving it."

"It provides a unified platform where you can install Jupyter, Python Spider, and other related tools without needing separate installations."

"I can use Anaconda for non-heavy tasks."

"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."

"Voice Configuration and Environmental Management Capabilities are the most valuable features."

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

"With Anaconda Navigator, we have been able to use multiple IDEs such as JupyterLab, Jupyter Notebook, Spyder, Visual Studio Code, and RStudio in one place. The platform-agnostic package manager, "Conda", makes life easy when it comes to managing and installing packages."

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

More Anaconda pros

Cons

"Lacking in some machine learning pipelines."

"One area for improvement is the pricing, which can be quite high."

"Amazon might need to emphasize its capabilities in generative models more effectively."

"The documentation must be made clearer and more user-friendly."

"While integration is available, there are concerns about how secure this integration is, particularly when exposing data to SageMaker."

"The entry point can be a bit difficult. Having all documentation easily accessible on the front page of SageMaker would be a great improvement."

"One area for improvement is the pricing, which can be quite high."

"The dashboard could be improved by including more features and providing more information about deployed models, their drift, performance, scaling, and customization options."

More Amazon SageMaker cons

"Anaconda consumes a significant amount of processing memory when working on it."

"The solution would benefit from offering more automation."

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

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

"Anaconda should be optimized for RAM consumption."

"It also takes up a lot of space."

"A lot of people and companies are investing in creating automated data cleaning and processing environments. Anaconda is a bit behind in that area."

"One feature that I would like to see is being able to use a different language in a different cell, which would allow me to mix R and Python together."

More Anaconda cons

Pricing and Cost Advice

"The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation."

"The solution is relatively cheaper."

"There is no license required for the solution since you can use it on demand."

"The pricing is comparable."

"The product is expensive."

"The tool's pricing is reasonable."

"SageMaker is worth the money for our use case."

"Amazon SageMaker is a very expensive product."

More Amazon SageMaker pricing and cost advice

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

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

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Educational Organization

13%

Computer Software Company

11%

Manufacturing Company

Financial Services Firm

20%

Computer Software Company

Manufacturing Company

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

How would you compare Databricks vs Amazon SageMaker?

We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...

What do you like most about Amazon SageMaker?

We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for t...

What is your experience regarding pricing and costs for Amazon SageMaker?

Before deploying SageMaker, I reviewed the pricing, especially for notebook instances. The cost for small to medium instances is not very high.

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.

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.

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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Product Reports

Amazon SageMaker

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Also Known As

AWS SageMaker, SageMaker

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Overview

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

Amazon Web Services (AWS)

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

Sample Customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit

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

Amazon SageMaker vs. Anaconda