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Dremio vs H2O.ai comparison

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Comparison Buyer's Guide

Executive SummaryUpdated on Feb 15, 2026

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

Dremio
Ranking in Data Science Platforms
9th
Average Rating
8.4
Reviews Sentiment
6.5
Number of Reviews
14
Ranking in other categories
Cloud Data Warehouse (10th)
H2O.ai
Ranking in Data Science Platforms
15th
Average Rating
7.6
Reviews Sentiment
6.8
Number of Reviews
10
Ranking in other categories
Model Monitoring (6th)
 

Mindshare comparison

As of November 2026, in the Data Science Platforms category, the mindshare of Dremio is 2.0%, down from 2.9% compared to the previous year. The mindshare of H2O.ai is 2.5%, up from 1.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Mindshare Distribution
ProductMindshare (%)
Dremio2.0%
H2O.ai2.5%
Other95.5%
Data Science Platforms
 

Featured Reviews

Corrr Moray - PeerSpot reviewer
SR BI developer at BRQ Digital Solutions
Has simplified complex data integration workflows and supported consistent reporting across multiple sources
We also have a close relationship with the team that does the Dremio maintenance for the database, like upgrading the versions and they know about some specific problems we had in the past, such as a memory leak. We had a memory leak on some versions, which sometimes stopped the service. Since we are using Dremio installed like a server, not a SaaS solution, many times we need to stop and restart the service to clear all the cache and all that, and this is the thing I should add. I see that many times the new versions of Dremio have not fixed old bugs, and in some new versions, old problems that were previously fixed come back again, so I think the upgrade part could use improvement. I remember using some features in the past, like pivot tables, which proved to be really difficult, but I know this is a fault also for other vendors. Pivoting, transposing, and unpivoting are often not so good. CTEs also many times prove to be not so good, so I think these two main items could be improved significantly if they standardize them.
MA
Senior Manager - AI at Shamal Holding
Have improved machine learning model automation and reduced decision-making time
One improvement I would like to see in H2O.ai is regarding the integration capabilities with different data sources, as I've seen platforms like DataIQ and DataBricks offer great integration with various data sources. H2O.ai could benefit from enhanced integration with real-time versus offline data sources, as well as improvements in productionalization solutions, including better deployment options on platforms like Azure and CI/CD integration. One of the features I'd like to see included in upcoming releases of H2O.ai pertains to the growing trend of Generative AI, with applications for LLM-based models and vector databases. I would like to see a solution similar to Azure AI Foundry, which provides the flexibility to integrate different LLMs into applications, including H2O-GPT and other models for varied applications.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"Dremio enables you to manage changes more effectively than any other data warehouse platform. There are two things that come into play. One is data lineage. If you are looking at data in Dremio, you may want to know the source and what happened to it along the way or how it may have been transformed in the data pipeline to get to the point where you're consuming it."
"Overall, you can rate it as eight out of ten."
"The primary value I find in Dremio is that this tool is mainly used to query data that are split amongst several sources and to virtualize those sources so that the end user faces it as a single source of truth of data."
"Dremio has positively impacted my organization by creating faster development and POC projects."
"Dremio is very easy to use for building queries."
"Dremio allows querying the files I have on my block storage or object storage."
"Everyone uses Dremio in my company; some use it only for the analytics function."
"We primarily use Dremio to create a data framework and a data queue."
"The company is interested in using an external platform in order to have an updated environment."
"The product is definitely worth looking at, as it is one of the upcoming products where you can build large models for use cases."
"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."
"I have utilized the AutoML feature in H2O.ai, which is one of the very powerful features where you don't need to worry about which algorithm is best for your model."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"We have seen significant ROI where we were able to use the product in certain key projects and could automate a lot of processes."
"The most valuable feature of H2O.ai is that it is plug-and-play."
"One of the most interesting features of the product is their driverless component, which allows you to test several different algorithms along with navigating you through choosing the best algorithm and gives you an interpretability capability that allows you to have some understanding of what's inside the algorithm and why it's behaving a certain way, making sure you are not biased towards the outcome."
 

Cons

"It shows errors sometimes."
"Many developers and programmers, when they are starting to work in this specific field or area, are much more used to SQL Server, the Microsoft way of querying, and Dremio has some features that are different when we are talking about the syntax of coding, so I would improve that."
"We had some crash issues concerning part of the engine caused by one query that made the engine crash."
"The community version lacks information or documentation."
"Dremio takes a long time to execute large queries or the executing of correlated queries or nested queries. Additionally, the solution could improve if we could read data from the streaming pipelines or if it allowed us to create the ETL pipeline directly on top of it, similar to Snowflake."
"I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported."
"One area where Dremio can be improved is the lack of tooling or functionalities for importing data in a pipeline way, as it is not an ETL or ELT component."
"We had a memory leak on some versions, which sometimes stopped the service."
"Feature engineering."
"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."
"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."
"The model management features could be improved."
"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."
"Regarding documentation, I faced challenges as I didn't see much information from a documentation perspective."
 

Pricing and Cost Advice

"Dremio is less costly competitively to Snowflake or any other tool."
"Right now the cluster costs approximately $200,000 per month and is based on the volume of data we have."
"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
22%
Outsourcing Company
8%
Comms Service Provider
8%
Manufacturing Company
6%
Financial Services Firm
18%
Comms Service Provider
8%
Construction Company
8%
Computer Software Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise6
Large Enterprise5
By reviewers
Company SizeCount
Small Business2
Midsize Enterprise3
Large Enterprise7
 

Questions from the Community

What is your experience regarding pricing and costs for Dremio?
I chose eight out of ten because Dremio is expensive for the Enterprise version, particularly regarding licensing.
What needs improvement with Dremio?
I think Dremio can be improved to query faster than multiple sources and big data lakehouses. The community version could be updated for new data catalog connections. I think the documentation is s...
What is your primary use case for Dremio?
I have been using Dremio to select multiple data sources, retrieve multiple data sources, and create and maintain Iceberg tables for the data lakehouse. My main use case for Dremio is to actively c...
What needs improvement with H2O.ai?
Even though H2O.ai provides the best model, there could be improvements in certain areas. For instance, when you want to work with fusion models, H2O.ai doesn't provide that kind of information. Cu...
What is your primary use case for H2O.ai?
I used H2O.ai on several POCs for my previous company, and it helped me find the best model. I needed to determine which model was performing better for job portal data. At that time, H2O.ai was ev...
What advice do you have for others considering H2O.ai?
For larger datasets, model computation or model training and testing typically takes considerable time because with individual models, you need to train and test each one. With H2O.ai, these concer...
 

Comparisons

 

Also Known As

Dremio AWS - BYOL
No data available
 

Overview

 

Sample Customers

UBS, TransUnion, Quantium, Daimler, OVH
poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco
Find out what your peers are saying about Dremio vs. H2O.ai and other solutions. Updated: September 2026.
920,929 professionals have used our research since 2012.