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Dremio vs H2O.ai comparison

 

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
10th
Average Rating
8.4
Reviews Sentiment
6.6
Number of Reviews
11
Ranking in other categories
Cloud Data Warehouse (9th)
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 August 2026, in the Data Science Platforms category, the mindshare of Dremio is 2.1%, down from 3.5% compared to the previous year. The mindshare of H2O.ai is 2.6%, up from 1.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Mindshare Distribution
ProductMindshare (%)
Dremio2.1%
H2O.ai2.6%
Other95.3%
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.
Abhay Vyas - PeerSpot reviewer
Technical Architect Data Engineering at a tech vendor with 201-500 employees
Advanced model selection and time efficiency meet needs but documentation and fusion model support are needed
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. Currently, it provides individual models as outcomes. If it could offer combinations of models, such as suggesting using XGBoost along with SVM for wonderful results, that fusion model concept would be a good option for developers. I hope the fusion model concept will be implemented soon in H2O.ai. Regarding documentation, I faced challenges as I didn't see much information from a documentation perspective. When I was trying to learn how to train and test H2O.ai, there was limited documentation available. If they could improve in that area, it would be really beneficial.

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."
"Dremio has positively impacted my organization as nowadays we are connected to multiple databases from multiple environments, multiple APIs, and applications, and Dremio organizes everything in an amazing way for me."
"The native error interface has been most valuable."
"Everyone uses Dremio in my company; some use it only for the analytics function."
"The most valuable feature of Dremio is it can sit on top of any other data storage, such as Amazon S3, Azure Data Factory, SGFS, or Hive. The memory competition is good. If you are running any kind of materialized view, you'd be running in memory."
"Dremio is very easy to use for building queries."
"The first feature that stands out for me in Dremio is the federated type of query, which allows the possibility to use multiple endpoints without worrying about writing custom SQL that runs only for SQL Server or for Postgres and Redshift."
"It's almost similar, yet it's better than Starburst in spinning up or connecting to the new source since it's on SaaS."
"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."
"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 product is definitely worth looking at, as it is one of the upcoming products where you can build large models for use cases."
"The company is interested in using an external platform in order to have an updated environment."
"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."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"The most valuable feature of H2O.ai is that it is plug-and-play."
"We have seen significant ROI where we were able to use the product in certain key projects and could automate a lot of processes."
 

Cons

"There are performance issues at times due to our limited experience with Dremio, and the fact that we are running it on single nodes using a community version."
"They have an automated tool for building SQL queries, so you don't need to know SQL. That interface works, but it could be more efficient in terms of the SQL generated from those things. It's going through some growing pains. There is so much value in tools like these for people with no SQL experience. Over time, Dermio will make these capabilities more accessible to users who aren't database people."
"We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."
"Dremio could be improved by making it easier for data cataloging, especially when working with open table formats, as you have to choose a data format and then go into it."
"Dremio doesn't support the Delta connector. Dremio writes the IT support for Delta, but the support isn't great. There is definitely room for improvement."
"Dremio takes a long time to execute large queries or the executing of correlated queries or nested queries."
"They need to have multiple connectors."
"They need to have multiple connectors. Starburst is rich in connectors, however, they are lacking Salesforce connectivity as of today."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
"H2O.ai can improve in areas like multimodal support and prompt engineering."
"The model management features could be improved."
"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."
"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."
"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."
 

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
25%
Computer Software Company
7%
Outsourcing Company
7%
Manufacturing Company
6%
Financial Services Firm
20%
Construction Company
7%
Manufacturing Company
7%
Computer Software Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business2
Midsize Enterprise5
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 don't have information about pricing, setup cost, and licensing for Dremio, so I am not entitled to discuss it.
What needs improvement with Dremio?
I wouldn't say there is anything Dremio can be improved on. If I could change something, I would say many developers and programmers, when they are starting to work in this specific field or area, ...
What is your primary use case for Dremio?
I have been using Dremio for a year and a half. My main use case for Dremio is that I am able to access multiple databases and I can easily and quickly connect Dremio with my dashboards. In my rece...
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: June 2026.
908,834 professionals have used our research since 2012.