Databricks vs IBM Watson Studio comparison

Databricks and IBM are both solutions in the Data Science Platforms category. Databricks is ranked #1 with an average rating of 8.4, while IBM is ranked #11 with an average rating of 8.4. Databricks holds a 17.2% mindshare in Data Science Platforms, compared to IBM’s 2.0% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 100% of IBM users who would recommend it.

Databricks

Read 90 Databricks reviews

13,763 Views
10,002 Comparison Views

96% willing to recommend

IBM Watson Studio

Read 15 IBM Watson Studio reviews

1,946 Views
1,192 Comparison Views

100% willing to recommend

Databricks

IBM Watson Studio

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 5, 2024

Databricks and IBM Watson Studio compete in data science and analytics. Databricks seems to have an advantage in performance for large-scale data processing and Spark integration, while IBM Watson Studio excels in AI capabilities and offers a comprehensive suite of data science tools.

Features: Databricks is known for its ease in running large-scale analytics, built-in optimization recommendations, and support for collaboration in notebooks. It integrates seamlessly with Spark and offers notable machine learning capabilities. The scalability and support for multiple programming languages enhance its versatility with cloud integrations. IBM Watson Studio stands out for its AI capabilities, built-in data science tools like SPSS Modeler, and use of Jupyter notebooks, making it powerful for machine learning and data integration tasks.

Room for Improvement: Databricks could improve by expanding advanced visualization and machine learning libraries, enhancing ETL tool integration, and providing more user-friendly documentation and interfaces for popular BI tools. IBM Watson Studio could enhance its intuitive user experience, ease of deployment, and support for new AI features. Users often cite navigation challenges, desiring a more integrated interface.

Ease of Deployment and Customer Service: Databricks offers versatile deployment options with support for public, private, and hybrid clouds. It provides comprehensive documentation, though technical support experiences vary. IBM Watson Studio also supports cloud and on-premises deployments. Its documentation is praised for clarity, with users reporting reliability that reduces the need for frequent technical support.

Pricing and ROI: Databricks operates on a pay-as-you-go model, seen as costly for extensive data usage but flexible and offering positive ROI by reducing infrastructure costs. IBM Watson Studio is viewed as reasonably priced for its features, although complex workloads may incur higher costs. Its pricing is generally considered straightforward, delivering beneficial ROI for large-scale data projects.

To learn more, read our detailed Databricks vs. IBM Watson Studio Report (Updated: April 2025).

Buyer's Guide

Databricks vs. IBM Watson Studio

April 2025

Download the complete report

Helped 851,604 peers since 2012

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

6.5

Databricks offers cloud scalability and ease of use, but financial benefits and effectiveness depend on specific use cases.

Sentiment score

7.0

Organizations saw positive ROI from IBM Watson Studio due to improved efficiency, scalability, sales, and reduced staffing needs.

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

Parag Bhosale

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

For more quotes and insights, download the Databricks report

The product offers a significant return on investment through its scalability and integration capabilities.

Willie Torres

Process Automation Lead at CONDIACTOR

My customers have seen returns on investment through increased efficiency, automated calculations, improved accuracy in pricing, and reduced staffing needs due to the automation.

Altan_Atabarut

Director, Channel and Alliances at Akinon

For more quotes and insights, download the IBM Watson Studio report

Customer Service

Sentiment score

7.2

Databricks offers highly rated, responsive support with clear documentation, proactive engagement, and multiple contact options, despite occasional delays.

Sentiment score

7.2

IBM Watson Studio customer service is praised for efficiency and responsiveness, though support quality varies by subscription plan and community resources.

Whenever we reach out, they respond promptly.

Parag Bhosale

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.

Prabhakar Bonam

Data Platform Architect at KELLANOVA

I rate the technical support as fine because they have levels of technical support available, especially partners who get really good support from Databricks on new features.

Lax Kas

Data Engineer at CRAFT Tech

For more quotes and insights, download the Databricks report

The support quality depends on the SLA or the contract terms.

Willie Torres

Process Automation Lead at CONDIACTOR

The community access is weak, which limits the ability to engage in discussions and find documentation and examples of similar cases effectively.

Altan_Atabarut

Director, Channel and Alliances at Akinon

For more quotes and insights, download the IBM Watson Studio report

Scalability Issues

Sentiment score

7.5

Databricks offers seamless scalability across clouds, efficiently handling data sizes and increasing users, with praised ease and robust performance.

Sentiment score

7.5

IBM Watson Studio is scalable with excellent integration, but its resource intensity may increase costs for extensive scaling.

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

Parag Bhosale

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

Databricks is an easily scalable platform.

Prabhakar Bonam

Data Platform Architect at KELLANOVA

I would rate the scalability of this solution as very high, about nine out of ten.

Lax Kas

Data Engineer at CRAFT Tech

For more quotes and insights, download the Databricks report

Watson Studio is very scalable.

Willie Torres

Process Automation Lead at CONDIACTOR

I rate IBM Watson Studio seven out of ten for scalability because while it scales, it requires significant resources to do so, making it expensive compared to some competitors.

Altan_Atabarut

Director, Channel and Alliances at Akinon

For more quotes and insights, download the IBM Watson Studio report

Stability Issues

Sentiment score

7.7

Databricks is highly stable and reliable, despite minor issues, effectively managing large data volumes and supporting enterprise use.

Sentiment score

7.4

IBM Watson Studio is praised for stability and reliability, comparable to or better than competitors, with effective optimization for large tasks.

They release patches that sometimes break our code.

Parag Bhosale

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.

Prabhakar Bonam

Data Platform Architect at KELLANOVA

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

ShubhamSharma7

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

For more quotes and insights, download the Databricks report

Expertise in optimization is necessary to manage such issues effectively.

Altan_Atabarut

Director, Channel and Alliances at Akinon

For more quotes and insights, download the IBM Watson Studio report

Room For Improvement

Databricks needs an improved UI, better integration, affordable pricing, enhanced machine learning, and streamlined, collaborative features.

IBM Watson Studio needs a more intuitive interface, better support, and reduced setup complexity for wider adoption.

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

ShubhamSharma7

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.

Parag Bhosale

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

For more quotes and insights, download the Databricks report

The platform is associated with a complicated setup process and demands heavy hardware, making it expensive to scale.

Altan_Atabarut

Director, Channel and Alliances at Akinon

One area that could be improved is the backup and restoration of the database and the overall database configuration.

Willie Torres

Process Automation Lead at CONDIACTOR

For more quotes and insights, download the IBM Watson Studio report

Setup Cost

Databricks pricing is usage-based and competitive, with mixed reviews regarding cost-effectiveness, especially for heavy data processing.

IBM Watson Studio's pricing varies, offering a free light edition, yet considered expensive by some, especially in Africa.

It is not a cheap solution.

Prabhakar Bonam

Data Platform Architect at KELLANOVA

For more quotes and insights, download the Databricks report

IBM Watson Studio is considered rather expensive, with a rating of six or seven.

Altan_Atabarut

Director, Channel and Alliances at Akinon

For more quotes and insights, download the IBM Watson Studio report

Valuable Features

Databricks offers a user-friendly interface, fast querying, multi-language support, and integrated AI for streamlined, collaborative data processing.

IBM Watson Studio excels in automation, predictive analytics, and integration, offering comprehensive AI capabilities for diverse data-driven tasks.

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

ShubhamSharma7

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.

Prabhakar Bonam

Data Platform Architect at KELLANOVA

The Unity Catalog is for data governance, and the Delta Lake is to build the lakehouse.

Lax Kas

Data Engineer at CRAFT Tech

For more quotes and insights, download the Databricks report

This capability saves a significant amount of time by automating processes that typically involve manual work, such as data cleaning, feature engineering, and predictive analytics.

Altan_Atabarut

Director, Channel and Alliances at Akinon

It integrates well with other platforms and offers good scalability.

Willie Torres

Process Automation Lead at CONDIACTOR

For more quotes and insights, download the IBM Watson Studio report

Categories and Ranking

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)

IBM Watson Studio

Ranking in Data Science Platforms

11th

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

AI Development Platforms (10th)

Mindshare comparison

As of May 2025, in the Data Science Platforms category, the mindshare of Databricks is 17.2%, down from 19.5% compared to the previous year. The mindshare of IBM Watson Studio is 2.0%, down from 2.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

ShubhamSharma7

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

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

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.

Read full review

Abilio Duarte

Senior Solutions Architect at Self-employed

A highly robust and well-documented platform that simplifies the complex world of AI

The main challenge lies in visibility and ease of use. Providing training sessions can be immensely helpful in helping users navigate and understand the tool's potential. This approach would empower users to explore and make the most of the tools and technologies at their disposal. Another area where IBM could enhance its offering is by providing more visibility to end users regarding the vast potential that Watson offers.

Read full review

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

10%

Manufacturing Company

Healthcare Company

Financial Services Firm

15%

Computer Software Company

11%

Manufacturing Company

10%

Educational Organization

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

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 ...

See all answers

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...

See all answers

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...

See all answers

What do you like most about IBM Watson Studio?

Stability-wise, it is a great tool.

See all answers

What is your experience regarding pricing and costs for IBM Watson Studio?

The pricing of Watson Studio is justified by the benefits and power it offers.

See all answers

What needs improvement with IBM Watson Studio?

One area that could be improved is the backup and restoration of the database and the overall database configuration. There were also challenges with programming the network extension in the last p...

See all answers

Comparisons

Dataiku vs Databricks

Compared 11% of the time

Microsoft Power BI vs Databricks

Compared 9% of the time

Dremio vs Databricks

Compared 7% of the time

Informatica PowerCenter vs Databricks

Compared 7% of the time

Tableau vs Databricks

Compared 5% of the time

More Databricks Competitors

Microsoft Azure Machine Learning Studio vs IBM Watson Studio

Compared 13% of the time

Azure OpenAI vs IBM Watson Studio

Compared 13% of the time

Google Vertex AI vs IBM Watson Studio

Compared 12% of the time

Amazon SageMaker vs IBM Watson Studio

Compared 4% of the time

Amazon Comprehend vs IBM Watson Studio

Compared 4% of the time

More IBM Watson Studio Competitors

Product Reports

Buyer's Guide

Databricks

May 2025

Download Databricks product report

Buyer's Guide

IBM Watson Studio

May 2025

Download IBM Watson Studio product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

Watson Studio, IBM Data Science Experience, Data Science Experience, DSx

Overview

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.

Databricks

IBM Watson Studio provides tools for data scientists, application developers and subject matter experts to collaboratively and easily work with data to build and train models at scale. It gives you the flexibility to build models where your data resides and deploy anywhere in a hybrid environment so you can operationalize data science faster.

IBM

Sample Customers

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

GroupM, Accenture, Fifth Third Bank

Buyer's Guide

Databricks vs. IBM Watson Studio

April 2025

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