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 #14 with an average rating of 8.5. Databricks holds a 10.7% mindshare in Data Science Platforms, compared to IBM’s 2.2% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 100% of IBM users who would recommend it.

Databricks

Read 91 Databricks reviews

18,822 Views
8,093 Comparison Views

96% willing to recommend

IBM Watson Studio

Read 16 IBM Watson Studio reviews

2,083 Views
1,354 Comparison Views

100% willing to recommend

Databricks

IBM Watson Studio

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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: December 2025).

Buyer's Guide

Databricks vs. IBM Watson Studio

December 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:

ROI

Sentiment score

6.6

Organizations benefit from Databricks' cost-effectiveness and efficiency, though some find evaluating immediate gains challenging due to specific contexts.

Sentiment score

5.8

IBM Watson Studio boosts efficiency and accuracy, reduces staffing, enhances conversion rates, and offers integration benefits, despite its cost.

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

7.1

Databricks customer service is praised for responsiveness and expertise, despite occasional delays and communication issues via Microsoft.

Sentiment score

6.5

IBM Watson Studio support is praised for responsiveness, but quality varies by plan and community access is limited.

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

7.4

Databricks provides excellent scalability, supporting diverse data sizes and sectors with high-performance cloud infrastructure and cost-effective management.

Sentiment score

7.0

IBM Watson Studio is scalable and flexible, supporting diverse needs with high costs but seamless integration and comprehensive features.

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 stable and reliable, with high performance and robustness, despite occasional minor issues resolved quickly.

Sentiment score

7.5

IBM Watson Studio is robust and reliable, performing well over time, particularly in private cloud environments, with consistent stability.

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

Databricks is definitely a very stable product and reliable.

AvivCohen

Data Engineer at a tech vendor 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 users desire advanced visualization, better integration, enhanced documentation, predictive analytics features, and improved user experience and tools.

IBM Watson Studio users want improved web functionality, modular structure, better navigation, enhanced support, and adaptable pricing for enterprises.

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

I wish learning IBM Watson Studio could be easier and more gradual, as it is a complex task.

NamanJain

Software Engineer at a computer software company with 51-200 employees

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

Setup Cost

Databricks' pricing is seen as high for large data volumes but competitive for batch processing on cloud platforms.

IBM Watson Studio's pricing is seen as reasonable but can be expensive, affecting competitiveness and regional deployments.

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 simplifies large-scale analytics with user-friendly UI, powerful integrations, and scalable features for enhanced performance and collaboration.

IBM Watson Studio offers automation, AI integration, scalability, and robust data modeling, ideal for large organizations with multi-cloud support.

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

The best features IBM Watson Studio offers are that it is good for big and complex organizations, it is multi-cloud, it has an on-prem facility, and it also has strong visual tools.

NamanJain

Software Engineer at a computer software company with 51-200 employees

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 (9th), Data Management Platforms (DMP) (5th), Streaming Analytics (1st)

IBM Watson Studio

Ranking in Data Science Platforms

14th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

AI Development Platforms (17th)

Mindshare comparison

As of December 2025, in the Data Science Platforms category, the mindshare of Databricks is 10.7%, down from 19.0% compared to the previous year. The mindshare of IBM Watson Studio is 2.2%, up from 2.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Market Share Distribution
Product	Market Share (%)
Databricks	10.7%
IBM Watson Studio	2.2%
Other	87.1%

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

Altan_Atabarut

Director, Channel and Alliances at Akinon

Automated processes improve efficiency while user interface and accessibility need enhancements

IBM Watson Studio, while powerful, lacks user-friendliness. It is not easy to use, particularly for medium or small enterprises or less experienced staff. Another aspect that requires improvement is the complexity involved in computer vision tasks. The integration capabilities have not significantly impacted workflow since there are simpler tools like Alteryx and Nine. The platform is associated with a complicated setup process and demands heavy hardware, making it expensive to scale. IBM should work on optimizing the user interface and enhancing the product's accessibility for medium and small enterprises.

Read full review

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Computer Software Company

Manufacturing Company

Healthcare Company

Financial Services Firm

12%

Manufacturing Company

11%

Computer Software Company

Educational Organization

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	25
Midsize Enterprise	12
Large Enterprise	56

By reviewers
Company Size	Count
Small Business	12
Midsize Enterprise	1
Large Enterprise	4

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

IBM Watson Studio is considered rather expensive, with a rating of six or seven. The pricing could be optimized relative to the features and capabilities of the product.

See all answers

What needs improvement with IBM Watson Studio?

I think IBM Watson Studio can be improved. I wish learning IBM Watson Studio could be easier and more gradual, as it is a complex task. Also, I think pricing is a bit high.

See all answers

What is your primary use case for IBM Watson Studio?

My main use case for IBM Watson Studio is the end-to-end ML life cycle. A specific example of a project where I used IBM Watson Studio for the end-to-end machine learning life cycle is that I built...

See all answers

Comparisons

Microsoft Power BI vs Databricks

Compared 7% of the time

Dataiku vs Databricks

Compared 7% of the time

Informatica PowerCenter vs Databricks

Compared 5% of the time

Dremio vs Databricks

Compared 5% of the time

Tableau Enterprise vs Databricks

Compared 4% of the time

More Databricks Competitors

Azure OpenAI vs IBM Watson Studio

Compared 10% of the time

Microsoft Azure Machine Learning Studio vs IBM Watson Studio

Compared 10% of the time

KNIME Business Hub vs IBM Watson Studio

Compared 7% of the time

Amazon SageMaker vs IBM Watson Studio

Compared 6% of the time

Google Vertex AI vs IBM Watson Studio

Compared 6% of the time

More IBM Watson Studio Competitors

Product Reports

Buyer's Guide

Databricks

December 2025

Download Databricks product report

Buyer's Guide

IBM Watson Studio

December 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 offers a scalable, versatile platform that integrates seamlessly with Spark and multiple languages, supporting data engineering, machine learning, and analytics in a unified environment.

Databricks stands out for its scalability, ease of use, and powerful integration with Spark, multiple languages, and leading cloud services like Azure and AWS. It provides tools such as the Notebook for collaboration, Delta Lake for efficient data management, and Unity Catalog for data governance. While enhancing data engineering and machine learning workflows, it faces challenges in visualization and third-party integration, with pricing and user interface navigation being common concerns. Despite needing improvements in connectivity and documentation, it remains popular for tasks like real-time processing and data pipeline management.

What features make Databricks unique?

Notebook: Enables collaborative work among team members.
Delta Lake: Optimizes data management operations.
Unity Catalog: Provides governance over data assets.
Cloud Integration: Seamlessly connects with major cloud platforms.

What benefits can users expect from Databricks?

Versatility: Supports diverse applications in data science and engineering.
Performance: Delivers efficient handling of large-scale analytics tasks.
Collaboration: Enhances teamwork in data projects.
Unified Environment: Centralizes machine learning and analytics activities.

In the tech industry, Databricks empowers teams to perform comprehensive data analytics, enabling them to conduct extensive ETL operations, run predictive modeling, and prepare data for SparkML. In retail, it supports real-time data processing and batch streaming, aiding in better decision-making. Enterprises across sectors leverage its capabilities for creating secure APIs and managing data lakes effectively.

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

December 2025

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