Databricks vs H2O.ai comparison

Databricks and H2O.ai are both solutions in the Data Science Platforms category. Databricks is ranked #1 with an average rating of 8.3, while H2O.ai is ranked #20 with an average rating of 7.0. Databricks holds a 18.8% mindshare in Data Science Platforms, compared to H2O.ai’s 1.5% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 100% of H2O.ai users who would recommend it.

Databricks

Read 88 Databricks reviews

16,774 Views
11,473 Comparison Views

96% willing to recommend

H2O.ai

Read 8 H2O.ai reviews

1,304 Views
952 Comparison Views

100% willing to recommend

Databricks

H2O.ai

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 5, 2024

Databricks and H2O.ai are competing platforms in data analytics and machine learning. Databricks has an edge in scalability and integration, making it suitable for handling vast amounts of data, while H2O.ai excels with specialized AI capabilities, appealing to users needing comprehensive machine learning functions.

Features: Databricks offers superior integration with Apache Spark and cloud services, robust data processing capabilities, and a collaborative environment. It also supports seamless use of multiple programming languages and provides a user-friendly notebook interface. H2O.ai delivers strong machine learning capabilities, including AutoML for automating model training, a centralized AI platform to streamline development, and support for Jupyter Notebooks to enhance collaborative efforts.

Room for Improvement: Databricks could enhance its deep learning support and simplify certain advanced machine learning tasks. An increase in available customer support channels would also be beneficial. H2O.ai may improve its scalability and ease of integration with external systems. Offering a wider range of built-in algorithms could attract users looking for more out-of-the-box solutions.

Ease of Deployment and Customer Service: Databricks provides cloud-native deployment, ensuring quick setup and scalability with strong technical support and training resources. H2O.ai offers flexible deployment options, including both cloud and on-premise solutions, along with comprehensive support focused on deep learning applications and AI-specific guidance.

Pricing and ROI: Databricks offers flexible pricing models that can align well with its scalable infrastructure, often yielding favorable ROI for large-scale data operations. H2O.ai tends to have a higher initial cost, but its advanced AI features can lead to substantial returns, especially for applications that benefit from specialized machine learning.

To learn more, read our detailed Databricks vs. H2O.ai Report (Updated: January 2025).

Buyer's Guide

Databricks vs. H2O.ai

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

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)

H2O.ai

Ranking in Data Science Platforms

20th

Average Rating

7.6

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

Model Monitoring (6th)

Mindshare comparison

As of February 2025, in the Data Science Platforms category, the mindshare of Databricks is 18.8%, up from 18.5% compared to the previous year. The mindshare of H2O.ai is 1.5%, down from 1.5% 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.

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Kashif Yaseen

Trainee Decision Scientist at a tech services company with 1,001-5,000 employees

Plug-and-play convenience enhances productivity but needs better multimodal support

We mostly used the solution in the domain that I'm working. We had most of the use cases around chatbots and conversational BI The solution was plug-and-play, meaning most of the components were handled by the solution itself rather than building them from scratch. This was useful for our banking…

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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

"Databricks has helped us have a good presence in data."

"Databricks is a scalable solution. It is the largest advantage of the solution."

"It helps integrate data science and machine learning capabilities."

"What I like about Databricks is that it's one of the most popular platforms that give access to folks who are trying not just to do exploratory work on the data but also go ahead and build advanced modeling and machine learning on top of that."

"We like that this solution can handle a wide variety and velocity of data engineering, either in batch mode or real-time."

"Databricks is based on a Spark cluster and it is fast. Performance-wise, it is great."

"The solution is easy to use and has a quick start-up time due to being on the cloud."

"I work in the data science field and I found Databricks to be very useful."

More Databricks pros

"One of the most interesting features of the product is their driverless component. The driverless component allows you to test several different algorithms along with navigating you through choosing the best algorithm."

"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."

"It is helpful, intuitive, and easy to use. The learning curve is not too steep."

"Fast training, memory-efficient DataFrame manipulation, well-documented, easy-to-use algorithms, ability to integrate with enterprise Java apps (through POJO/MOJO) are the main reasons why we switched from Spark to H2O."

"The most valuable feature of H2O.ai is that it is plug-and-play."

"The most valuable features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people."

"The ease of use in connecting to our cluster machines."

Cons

"When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand."

"Databricks may not be as easy to use as other tools, but if you simplify a tool too much, it won't have the flexibility to go in-depth. Databricks is completely in the programmer's hands. I prefer flexibility rather than simplicity."

"The stability of the clusters or the instances of Databricks would be better if it was a much more stable environment. We've had issues with crashes."

"The API deployment and model deployment are not easy on the Databricks side."

"It would be very helpful if Databricks could integrate with platforms in addition to Azure."

"It would be nice to have more guidance on integrations with ETLs and other data quality tools."

"The interface of Databricks could be easier to use when compared to other solutions. It is not easy for non-data scientists. The user interface is important before we had to write code manually and as solutions move to "No code AI" it is critical that the interface is very good."

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

More Databricks cons

"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."

"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."

"The interpretability module has room for improvement. Also, it needs to improve its ability to integrate with other systems, like SageMaker, and the overall integration capability."

"H2O.ai can improve in areas like multimodal support and prompt engineering."

"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."

"I would like to see more features related to deployment."

"The model management features could be improved."

"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."

Pricing and Cost Advice

"The billing of Databricks can be difficult and should improve."

"Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery."

"It is an expensive tool. The licensing model is a pay-as-you-go one."

"We implement this solution on behalf of our customers who have their own Azure subscription and they pay for Databricks themselves. The pricing is more expensive if you have large volumes of data."

"I would rate Databricks' pricing seven out of ten."

"The cost for Databricks depends on the use case. I work on it as a consultant, so I'm using the client's Databricks, so it depends on how big the client is."

"Databricks are not costly when compared with other solutions' prices."

"I am based in South Africa, where it is expensive adapting to the cloud, and then there is the price for the tool itself."

More Databricks pricing and cost advice

"We have seen significant ROI where we were able to use the product in certain key projects and could automate a lot of processes. We were even able to reduce staff."

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

11%

Manufacturing Company

Healthcare Company

Financial Services Firm

21%

Computer Software Company

11%

Manufacturing Company

10%

Energy/Utilities Company

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 ...

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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...

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What needs improvement with H2O.ai?

H2O.ai can improve in areas like multimodal support and prompt engineering. They are already working on updates and changes. Although I haven't explored all the new products they've added to their ...

See all answers

What is your primary use case for H2O.ai?

We mostly used the solution in the domain that I'm working. We had most of the use cases around chatbots and conversational BI.

See all answers

What advice do you have for others considering H2O.ai?

It is important to address data privacy concerns and ensure you're choosing the right vendor that meets your use case demands. Also, you may leave my name, Kashif, but please keep the company name ...

See all answers

Comparisons

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Compared 11% of the time

Dremio vs Databricks

Compared 8% of the time

Microsoft Power BI vs Databricks

Compared 8% of the time

Informatica PowerCenter vs Databricks

Compared 8% of the time

Amazon SageMaker vs Databricks

Compared 7% of the time

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Dataiku vs H2O.ai

Compared 18% of the time

Amazon SageMaker vs H2O.ai

Compared 17% of the time

Microsoft Azure Machine Learning Studio vs H2O.ai

Compared 13% of the time

KNIME vs H2O.ai

Compared 12% of the time

RapidMiner vs H2O.ai

Compared 5% of the time

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Product Reports

Buyer's Guide

Databricks

February 2025

Download Databricks product report

Buyer's Guide

Data Science Platforms

January 2025

Download H2O.ai product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

No data available

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

H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O’s supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models. The H2O platform is used by over 14,000 organizations globally and is extremely popular in both the R & Python communities.

H2O.ai

Sample Customers

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

poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco

Buyer's Guide

Databricks vs. H2O.ai

January 2025

Free Report: Databricks vs. H2O.ai

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