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Firebolt vs Snowflake Analytics comparison

 

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

Firebolt
Ranking in Cloud Data Warehouse
16th
Average Rating
9.0
Reviews Sentiment
7.1
Number of Reviews
1
Ranking in other categories
No ranking in other categories
Snowflake Analytics
Ranking in Cloud Data Warehouse
10th
Average Rating
8.4
Reviews Sentiment
7.0
Number of Reviews
43
Ranking in other categories
Web Analytics (2nd)
 

Mindshare comparison

As of January 2026, in the Cloud Data Warehouse category, the mindshare of Firebolt is 1.6%, up from 0.5% compared to the previous year. The mindshare of Snowflake Analytics is 1.5%, up from 0.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Warehouse Market Share Distribution
ProductMarket Share (%)
Snowflake Analytics1.5%
Firebolt1.6%
Other96.9%
Cloud Data Warehouse
 

Featured Reviews

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.
Garima Goel - PeerSpot reviewer
Associate Principal Engineer at Nagarro
Have created secure cloud-based data lakes and improved real-time data processing using integrated AI features
There are many capabilities which Snowflake Analytics offers that I find valuable, such as the storage and compute engine that allows working with any cloud system such as AWS or Azure, alongside its efficiencies in storage computation and cost-effectiveness, which saves money compared to on-premise systems. We also have features such as pre-cached results, Time Travel, and fail-safe, which are very useful for restoring data if deleted accidentally, and the streams and data pipes that facilitate real-time ingestion are great features as well. Snowflake Analytics offers multiple new connectors, allowing me to connect it with Kafka, and with Snowpark, I can work with any programming language such as Python, Java, or Scala for data processing and analysis. The data sharing feature offered by Snowflake Analytics is good because it allows sharing specific sets of data to end customers or users from different Snowflake Analytics accounts without exposing the entire dataset for data security reasons. Snowflake Analytics' support for machine learning models and real-time insights has enhanced significantly. Originally, it wasn't strong in AI/ML, but now it has multiple models and forecasting capabilities, providing good competition to tools such as Databricks and Spark. In BI, I have worked majorly with Microsoft Power BI, and the integration with Snowflake Analytics is very easy. The way we integrate Snowflake Analytics with other on-premise systems just requires the warehouse details, username, passwords, and the account name, along with multiple options such as client ID and credentials for logging in and creating a session. The end-to-end encryption provided by Snowflake Analytics is very important because, in my previous firm, working in finance and investment management, data encryption is necessary due to the sensitive nature of customer data and the involvement of people's money. It's crucial to have encryption in transit and at rest, along with data masking features which Snowflake Analytics offers.

Quotes from Members

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

Pros

"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."
"Its structure aids in storing, managing, and analyzing large datasets in the cloud, using various relationships like one-to-one and many-to-one, ensuring consistency in the data structure."
"Snowflake Analytics' predictive analytics elements, which incorporate AI, are good and easy to integrate."
"The most valuable feature of Snowflake Analytics is its performance."
"The performance has been good."
"It is quite a convenient tool."
"The Snowflake features I find most beneficial for data analysis are primarily related to analytics, particularly their features like materialized views and queues, which are especially useful for dashboarding purposes."
"Scaling is very high – there's no problem for scaling purposes. The learning curve is very small. And there are a lot of advanced features like handling duplicates, security, data governance, data sharing, and data cloning."
"Deployment is straightforward as it's a SaaS product. No maintenance required."
 

Cons

"Firebolt's engine takes a long time to start because it needs to make engine calls."
"Integration into different Python and Jupyter notebooks needs to be improved."
"The UI must be improved."
"Machine learning should be improved."
"End-to-end execution of jobs isn't possible with Snowflake, which means we have to do some customization."
"One area that could benefit from enhancement is the user interface for more visual ESM features."
"I cannot comment on the product's stability because we are still struggling with its performance."
"Snowflake could improve in the areas of advanced machine learning AML and generative AI."
"The technical support is not very good."
 

Pricing and Cost Advice

Information not available
"The pricing is on the higher side."
"The solution's pricing is affordable."
"Snowflake Analytics is a little more costly than Azure."
"When using Snowflake, you pay based on your usage. They calculate how much CPU has been used. If you use excess warehouse storage, you are charged one credit per hour. If you are in Asia, you are charged $3 per credit. If you have 10 users running parallel with the same excess, you will be charged $30."
"On a scale of one to ten, where one is a low price, and ten is a high price, I rate the pricing a seven. The solution's pricing is high."
"I rate the product price a seven on a scale of one to ten, where one is low price, and ten is high price."
"I have been using free trial version."
"It is not overly expensive. I would rate the pricing a six out of ten, with ten being expensive."
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Top Industries

By visitors reading reviews
No data available
Computer Software Company
14%
Financial Services Firm
8%
Healthcare Company
7%
Retailer
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business11
Midsize Enterprise12
Large Enterprise21
 

Questions from the Community

Ask a question
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What is your experience regarding pricing and costs for Snowflake Analytics?
Snowflake Analytics is quite economical. It does not appear to incur significant extra expenses beyond the solution's initial cost. However, a complete pricing analysis is still in progress.
What needs improvement with Snowflake Analytics?
In my opinion, Snowflake Analytics can be improved by introducing more features, such as additional integration options. I remember using Snowflake Pro, which allows exporting direct data into the ...
 

Overview

 

Sample Customers

Information Not Available
Lionsgate, Adobe, Sony, Capital One, Akamai, Deliveroo, Snagajob, Logitech, University of Notre Dame, Runkeeper
Find out what your peers are saying about Snowflake Computing, Microsoft, Teradata and others in Cloud Data Warehouse. Updated: January 2026.
881,082 professionals have used our research since 2012.