Amazon SageMaker vs Databricks comparison

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Amazon SageMaker

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Executive SummaryUpdated on Apr 20, 2025

Databricks and Amazon SageMaker compete in the data analytics and machine learning platform category. Databricks seems to have the upper hand in terms of flexible integration and performance optimization features.

Features: Databricks offers scalable Spark clusters, a versatile notebook environment, and support for multiple programming languages, enhancing big data and machine learning capabilities. Delta data format ensures fast performance and built-in optimization recommendations streamline analytics. Amazon SageMaker provides excellent AWS integration, diverse built-in algorithms, and features like Autopilot for automated processes, serverless execution, and easy API deployment, catering to non-coding users effectively.

Room for Improvement: Databricks should improve Git integration, expand machine learning libraries, and provide advanced data visualization. Additional cloud platform support and better BI tool integration are needed alongside more intuitive notebook usability. SageMaker could improve its pricing model, enhance documentation, and provide better GPU model integration and hyperparameter tuning suggestions.

Ease of Deployment and Customer Service: Databricks is mainly deployed on public clouds, offering flexibility with hybrid setups, though support can be inconsistent. SageMaker is available on public clouds, with satisfactory technical support, though users seek more comprehensive documentation and timely responses.

Pricing and ROI: Databricks pricing varies, with a pay-as-you-go model benefiting cloud users, providing positive ROI due to efficient data processing. SageMaker faces criticism for high costs, especially with adding resources, but costs are often justified when fully utilizing the AWS ecosystem.

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

Amazon SageMaker vs. Databricks

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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:

ROI

Sentiment score

7.3

Amazon SageMaker offers significant ROI with cost reductions, time savings, and notable financial benefits, especially in fraud detection and targeted ads.

Sentiment score

6.6

Organizations experience mixed returns from Databricks, with benefits from cost savings and efficiency, but challenges in initial migration.

The return on investment varies by use case and offers significant value in revenue increases and cost saving capabilities, especially in real time fraud detection and targeted advertisements.

For more quotes and insights, download the Amazon SageMaker report

Senior Solutions Architect at Amazon

For a lot of different tasks, including machine learning, it is a nice solution.

For more quotes and insights, download the Databricks report

Senior Data Engineer at a logistics company with 51-200 employees

When it comes to big data processing, I prefer Databricks over other solutions.

IshwarSukheja

Head CEO at bizmetric

Customer Service

Sentiment score

7.2

Amazon SageMaker customer service has mixed reviews, with satisfaction varying based on user experience, support promptness, and service tier.

Sentiment score

7.2

Databricks customer service is generally effective with prompt responses, though some report issues mainly with third-party support channels.

The technical support from AWS is excellent.

Lead Consultant at Saama

The support is very good with well-trained engineers.

For more quotes and insights, download the Amazon SageMaker report

Senior Solutions Architect at Amazon

The response time is generally swift, usually within seven to eight hours.

Saurabh Jaiswal

Python AWS & AI Expert at a tech consulting company

Whenever we reach out, they respond promptly.

Senior Data Engineer at a logistics company with 51-200 employees

As of now, we are raising issues and they are providing solutions without any problems.

For more quotes and insights, download the Databricks report

Data Platform Architect at KELLANOVA

Scalability Issues

Sentiment score

7.6

Amazon SageMaker is highly scalable, handling diverse data needs effectively but may require deployment expertise for optimal efficiency.

Sentiment score

7.4

Databricks is praised for efficient scalability and cloud compatibility, allowing easy resource adjustment across diverse projects and industries.

The availability of GPU instances can be a challenge, requiring proper planning.

Senior Solutions Architect at Amazon

It works very well with large data sets from one terabyte to fifty terabytes.

Saurabh Jaiswal

Python AWS & AI Expert at a tech consulting company

Amazon SageMaker is scalable and works well from an infrastructure perspective.

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Lead Consultant at Saama

The patches have sometimes caused issues leading to our jobs being paused for about six hours.

Senior Data Engineer at a logistics company with 51-200 employees

Databricks is an easily scalable platform.

For more quotes and insights, download the Databricks report

Data Platform Architect at KELLANOVA

Stability Issues

Sentiment score

7.8

Amazon SageMaker is stable and reliable, with minor issues mainly due to user configuration errors, not infrastructure problems.

Sentiment score

7.6

Databricks is stable and efficient for large data, with minor issues during updates and occasional connectivity challenges.

There are issues, but they are easily detectable and fixable, with smooth error handling.

Saurabh Jaiswal

Python AWS & AI Expert at a tech consulting company

I rate the stability of Amazon SageMaker between seven and eight.

For more quotes and insights, download the Amazon SageMaker report

Lead Consultant at Saama

They release patches that sometimes break our code.

Senior Data Engineer at a logistics company with 51-200 employees

Although it is too early to definitively state the platform's stability, we have not encountered any issues so far.

Data Platform Architect at KELLANOVA

Cluster failure is one of the biggest weaknesses I notice in our Databricks.

For more quotes and insights, download the Databricks report

Data Engineer at a engineering company with 1,001-5,000 employees

Room For Improvement

Amazon SageMaker needs UI simplification, better documentation, cost efficiency improvements, enhanced security, scalability, training resources, and performance optimization.

Databricks users desire improved UI, enhanced data visualization, better integration, clearer error messages, robust support, and comprehensive documentation.

Having all documentation easily accessible on the front page of SageMaker would be a great improvement.

Syed Muhammad Noman Akhtar

AWS & Azure Engineer at a media company with 11-50 employees

This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background.

Lead Consultant at Saama

Integration of the latest machine learning models like the new Amazon LLM models could enhance its capabilities.

For more quotes and insights, download the Amazon SageMaker report

Senior Solutions Architect at Amazon

Adjusting features like worker nodes and node utilization during cluster creation could mitigate these failures.

Data Engineer at a engineering company with 1,001-5,000 employees

We prefer using a small to mid-sized cluster for many jobs to keep costs low, but this sometimes doesn't support our operations properly.

For more quotes and insights, download the Databricks report

Senior Data Engineer at a logistics company with 51-200 employees

We use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools.

Rama Subba Reddy Thavva

Solution Architect at Mercedes-Benz AG

Setup Cost

Amazon SageMaker is costly, especially for notebook instances, with better visibility needed to optimize pay-as-you-go costs.

Databricks pricing depends on usage, with flexibility in licensing, and can vary in competitiveness compared to other solutions.

The cost for small to medium instances is not very high.

Syed Muhammad Noman Akhtar

AWS & Azure Engineer at a media company with 11-50 employees

For a single user, prices might be high yet could be cheaper for user-managed services compared to AWS-managed services.

Lead Consultant at Saama

The pricing can be up to eight or nine out of ten, making it more expensive than some cloud alternatives yet more economical than on-premises setups.

For more quotes and insights, download the Amazon SageMaker report

Senior Solutions Architect at Amazon

It is not a cheap solution.

For more quotes and insights, download the Databricks report

Data Platform Architect at KELLANOVA

Valuable Features

Amazon SageMaker provides flexible AI/ML solutions with easy AWS integration, strong deployment features, and comprehensive tools for scalability.

Databricks provides a unified platform for data engineering, machine learning, seamless cloud integration, and robust data management capabilities.

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

Syed Muhammad Noman Akhtar

AWS & Azure Engineer at a media company with 11-50 employees

They offer insights into everyone making calls in my organization.

Maxwell Seefeld

President & CEO at Y12

The most valuable features include the ML operations that allow for designing, deploying, testing, and evaluating models.

For more quotes and insights, download the Amazon SageMaker report

Senior Solutions Architect at Amazon

Databricks' capability to process data in parallel enhances data processing speed.

Data Engineer at a engineering company with 1,001-5,000 employees

The platform allows us to leverage cloud advantages effectively, enhancing our AI and ML projects.

For more quotes and insights, download the Databricks report

Data Platform Architect at KELLANOVA

The notebooks and the ability to share them with collaborators are valuable, as multiple developers can use a single cluster.

Rama Subba Reddy Thavva

Solution Architect at Mercedes-Benz AG

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)

Databricks

Ranking in Data Science Platforms

1st

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (7th), Streaming Analytics (1st)

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 Databricks is 18.2%, down from 19.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

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

Lead Consultant at Saama

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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Capability to integrate diverse coding languages in a single notebook greatly enhances workflow

Data Engineer at a engineering company with 1,001-5,000 employees

Databricks offers various courses that I can use, whether it's PySpark, Scala, or R. I can leverage all these courses in a single notebook, which is beneficial for clients as they can access various tools in one place whenever needed. This is quite significant. I usually work with PySpark based on client requirements. After coding, I feed the Databricks notebooks into the ADF pipeline for updates. Databricks' capability to process data in parallel enhances data processing speed. Furthermore, I can connect our Databricks notebook directly with Power BI and other visualization tools like Qlik. Once we develop code, it allows us to transform raw data into visualizations for clients using analysis diagrams, which is very helpful.

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Educational Organization

11%

Computer Software Company

11%

Manufacturing Company

Financial Services Firm

18%

Computer Software Company

10%

Manufacturing Company

Healthcare Company

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.

Which do you prefer - Databricks or Azure Machine Learning Studio?

Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...

Which would you choose - Databricks or Azure Stream Analytics?

Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their orga...

What do you like most about Databricks?

Databricks is hosted on the cloud. It is very easy to collaborate with other team members who are working on it. It is production-ready code, and scheduling the jobs is easy.

Azure OpenAI vs Amazon SageMaker

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Product Reports

Amazon SageMaker

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Also Known As

AWS SageMaker, SageMaker

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

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)

Databricks is utilized for advanced analytics, big data processing, machine learning models, ETL operations, data engineering, streaming analytics, and integrating multiple data sources.

Organizations leverage Databricks for predictive analysis, data pipelines, data science, and unifying data architectures. It is also used for consulting projects, financial reporting, and creating APIs. Industries like insurance, retail, manufacturing, and pharmaceuticals use Databricks for data management and analytics due to its user-friendly interface, built-in machine learning libraries, support for multiple programming languages, scalability, and fast processing.

What are the key features of Databricks?

User-friendly interface: Simplifies operations and usability.
Built-in machine learning libraries: Facilitates machine learning tasks.
Support for multiple programming languages: Enhances flexibility.
Scalability: Efficiently handles growing data needs.
Fast processing: Improves performance and speed.
Automated optimization: Reduces manual efforts.
Data visualization: Provides insightful visuals.
Collaborative features: Enhances teamwork.
Delta Lake performance: Boosts data management.
Seamless cluster management: Simplifies system operations.

What are the benefits or ROI to look for in Databricks reviews?

Efficiency in handling large datasets: Ensures smooth processing.
Interactive workspace environment: Improves user collaboration.
Integration with platforms: Provides connectivity benefits.
Performance optimization: Enhances overall system performance.
Support for data governance and security: Ensures data integrity and protection.

Databricks is implemented in insurance for risk analysis and claims processing; in retail for customer analytics and inventory management; in manufacturing for predictive maintenance and supply chain optimization; and in pharmaceuticals for drug discovery and patient data analysis. Users value its scalability, machine learning support, collaboration tools, and Delta Lake performance but seek improvements in visualization, pricing, and integration with BI tools.

Sample Customers

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

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware

Amazon SageMaker vs. Databricks