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Databricks vs Firebolt comparison

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Comparison Buyer's Guide

Executive Summary

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 Cloud Data Warehouse
3rd
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
95
Ranking in other categories
Data Science Platforms (1st), Data Management Platforms (DMP) (3rd), Streaming Analytics (1st)
Firebolt
Ranking in Cloud Data Warehouse
16th
Average Rating
9.0
Reviews Sentiment
6.9
Number of Reviews
1
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of November 2026, in the Cloud Data Warehouse category, the mindshare of Databricks is 9.3%, up from 8.8% compared to the previous year. The mindshare of Firebolt is 2.4%, up from 1.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Warehouse Mindshare Distribution
ProductMindshare (%)
Databricks9.3%
Firebolt2.4%
Other88.3%
Cloud Data Warehouse
 

Featured Reviews

SimonRobinson - PeerSpot reviewer
Governance And Engagement Lead
Improved data governance has enabled sensitive data tracking but cost management still needs work
I believe we could improve Databricks integration with cloud service providers. The impact of our current integration has not been particularly good, and it's becoming very expensive for us. The inefficiencies in our implementation, such as not shutting down warehouses when they're not in use or reserving the right number of credits, have led to increased costs. We made several beginner mistakes, such as not taking advantage of incremental loading and running overly complicated queries all the time. We should be using ETL tools to help us instead of doing it directly in Databricks. We need more experienced professionals to manage Databricks effectively, as it's not as forgiving as other platforms such as Snowflake. I think introducing customer repositories would facilitate easier implementation with Databricks.
Iqbal Hossain Raju - PeerSpot reviewer
Junior Software Engineer at a healthcare company with 10,001+ employees
Can quickly query it to generate quick results
We have used Snowflake before. We support both. Firebolt has better performance, executing queries much quicker than Snowflake. However, Snowflake has more functionality. Depending on the client's needs, we can recommend the best option. Firebolt is a relatively new technology. Snowflake has many functionalities. Firebolt does not support unloading data to S3. There is no built-in way to do this in Firebolt. Alternatively, the data can be retrieved using API calls and loaded to S3 manually. Data can be unloaded to S3 directly using Snowflake. Firebolt significantly improves our performance over Snowflake because it takes less time to execute queries. This is especially important for our company because we use some KPIs that require fast loading times.

Quotes from Members

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

Pros

"The initial setup phase of Databricks was good."
"The load distribution capabilities are good, and you can perform data processing tasks very quickly."
"Stability of the product is good, whether it's handling large volumes, diverse elements of data or processing data at speed."
"It is fast, it's scalable, and it does the job it needs to do."
"A very valuable feature is the data processing, and the solution is specifically good at using the Spark ecosystem."
"It has allowed our data engineers, data scientists, and analysts to collaborate and work on the same platform."
"Prior to using Azure Databricks in the cloud, we had Databricks installed in clusters, and since our implementation, the performance has increased and our cost has been reduced."
"Databricks is quite easy to use and requires less coding and customizations than a solution like AWS SageMaker, enabling more people to efficiently build and host their ML code while leveraging the already integrated MLflow to track and monitor all our different experiments."
"Firebolt is fast for analytical purposes. For example, we have analytical data in our data warehouse, and Firebolt can quickly query it to generate quick results."
 

Cons

"I present a lot of projects in various forums and seminars and there aren't a lot of credits and trial options with Databricks. Even if we want to explore, we're not able to and that's a challenge."
"Doesn't provide a lot of credits or trial options."
"The integration of data could be a bit better."
"Anyone who doesn't know SQL may find the product difficult to work with."
"There is room for improvement in visualization."
"This is a fairly expensive solution for any service outside of the basic package, and costs can add up quite quickly if there are large scaling requirements."
"Implementation of Databricks is still very code heavy."
"Databricks is a very expensive solution. Pricing is an area that could definitely be improved."
"Firebolt's engine takes a long time to start because it needs to make engine calls."
 

Pricing and Cost Advice

"Databricks are not costly when compared with other solutions' prices."
"Databricks is a very expensive solution. Pricing is an area that could definitely be improved. They could provide a lower end compute and probably reduce the price."
"The licensing costs of Databricks depend on how many licenses we need, depending on which Databricks provides a lot of discounts."
"We find Databricks to be very expensive, although this improved when we found out how to shut it down at night."
"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."
"Whenever we want to find the actual costing, we have to send an email to Databricks, so having the information available on the internet would be helpful."
"The billing of Databricks can be difficult and should improve."
"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."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
16%
Manufacturing Company
10%
Outsourcing Company
6%
Healthcare Company
6%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business26
Midsize Enterprise14
Large Enterprise60
No data available
 

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 ...
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...
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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Comparisons

 

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash
No data available
 

Overview

 

Sample Customers

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
Information Not Available
Find out what your peers are saying about Snowflake Computing, Google, Databricks and others in Cloud Data Warehouse. Updated: October 2026.
920,769 professionals have used our research since 2012.