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 19.1% 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 85 Databricks reviews

17,588 Views
11,896 Comparison Views

96% willing to recommend

H2O.ai

Read 8 H2O.ai reviews

1,366 Views
978 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

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 January 2025, in the Data Science Platforms category, the mindshare of Databricks is 19.1%, up from 18.5% compared to the previous year. The mindshare of H2O.ai is 1.5%, down from 1.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Parag Bhosale

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

Integrating engineering and learning, but cost challenges arise with cluster management

We often use a single cluster to ingest Databricks, which Databricks doesn't recommend. They suggest using a no-cluster solution like job clusters. This can be overwhelming for us because we started smaller. 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. We need to stay in sync with the DVR versions, and migrations can pose challenges. For example, issues arose when we moved a cluster from a previous version to the latest one. We could use their job clusters, however, that increases costs, which is challenging for us as a startup. Maintaining this infrastructure can be a headache.

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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

"The solution offers a free community version."

"The most valuable features of the solution are the hardware and the resources it quickly provides without much hassle."

"A very valuable feature is the data processing, and the solution is specifically good at using the Spark ecosystem."

"Databricks gives you the flexibility of using several programming languages independently or in combination to build models."

"I like the ability to use workspaces with other colleagues because you can work together even without seeing the other team's job."

"We can scale the product."

"Databricks' Lakehouse architecture has been most useful for us. The data governance has been absolutely efficient in between other kinds of solutions."

"The Delta Lake data type has been the most useful part of this solution. Delta Lake is an opensource data type and it was implemented and invented by Databricks."

More Databricks pros

"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."

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

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

"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."

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

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

"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."

Cons

"Databricks is not geared towards the end-user, but rather it is for data engineers or data scientists."

"There should be better integration with other platforms."

"I would like more integration with SQL for using data in different workspaces."

"Databricks requires writing code in Python or SQL, so if you're a good programmer then you can use Databricks."

"The ability to customize our own pipelines would enhance the product, similar to what's possible using ML files in Microsoft Azure DevOps."

"They release patches that sometimes break our code. These patches are supposed to fix issues, but sometimes they cause disruptions."

"Some of the error messages that we receive are too vague, saying things like "unknown exception", and these should be improved to make it easier for developers to debug problems."

"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."

More Databricks cons

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

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

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

"The model management features could be improved."

"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."

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

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

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

Pricing and Cost Advice

"The price of Databricks is reasonable compared to other solutions."

"The licensing costs of Databricks is a tiered licensing regime, so it is flexible."

"I do not exactly know the costs, but one of our clients pays between $100 USD and $200 USD monthly."

"We only pay for the Azure compute behind the solution."

"We have only incurred the cost of our AWS cloud services. This is because during this period, Databricks provided us with an extended evaluation period, and we have not spent much money yet. We are just starting to incur costs this month, I will know more later on the full cost perspective."

"We're charged on what the data throughput is and also what the compute time is."

"We find Databricks to be very expensive, although this improved when we found out how to shut it down at night."

"The solution requires a subscription."

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...

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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 ...

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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

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Informatica PowerCenter vs Databricks

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Amazon SageMaker vs Databricks

Compared 8% of the time

Microsoft Power BI vs Databricks

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Amazon SageMaker vs H2O.ai

Compared 17% of the time

Dataiku vs H2O.ai

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Microsoft Azure Machine Learning Studio vs H2O.ai

Compared 14% of the time

KNIME vs H2O.ai

Compared 12% of the time

IBM Watson Studio vs H2O.ai

Compared 5% of the time

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Product Reports

Buyer's Guide

Databricks

January 2025

Download Databricks product report

Buyer's Guide

Data Science Platforms

January 2025

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Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

No data available

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Databricks

H2O.ai

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.

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.

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

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