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Gretel.ai vs Snowflake comparison

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Comparison Buyer's Guide

Executive SummaryUpdated on Feb 1, 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

Gretel.ai
Ranking in AI Synthetic Data
4th
Average Rating
8.0
Reviews Sentiment
3.5
Number of Reviews
1
Ranking in other categories
No ranking in other categories
Snowflake
Ranking in AI Synthetic Data
1st
Average Rating
8.4
Reviews Sentiment
6.9
Number of Reviews
108
Ranking in other categories
Data Warehouse (1st), Cloud Data Warehouse (1st), Database Management Systems (DBMS) (6th), AI Software Development (8th)
 

Mindshare comparison

As of November 2026, in the AI Synthetic Data category, the mindshare of Gretel.ai is 6.8%, down from 12.4% compared to the previous year. The mindshare of Snowflake is 6.0%, down from 6.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Synthetic Data Mindshare Distribution
ProductMindshare (%)
Snowflake6.0%
Gretel.ai6.8%
Other87.2%
AI Synthetic Data
 

Featured Reviews

Gouthami  - PeerSpot reviewer
senior software engineer at a tech vendor with 10,001+ employees
Synthetic test data has accelerated compliant development and reduces manual masking work
In my opinion, the best features that Gretel.ai offers are faster performance, better accuracy, and lower computing costs. When I mention better accuracy and performance, I refer to improved model accuracy. Whatever data we are getting has rare scenarios. Gretel.ai can detect those scenarios and generate more examples for training, which reduces manual effort from that perspective, meaning it has better model accuracy and performance. Besides that, I want to add that it has better testing and is faster. Compliance with regulatory actions is also a benefit because data sharing is advantageous. Since using Gretel.ai, the positive impact on my organization includes a reduction in manual effort and infrastructural cost in training the AI model due to compliance issues, resulting in faster development and reduced waiting time.
SunilPatil1 - PeerSpot reviewer
Asset Builder at Genpact - Headstrong
Have prioritized security while managing multi-agent data migration and cloud adoption
We utilize Time Travel with Snowflake because this is a very useful feature. Everyone finds it crucial because in conventional data platforms, it's very difficult to handle these kinds of things. This feature is essential, though I don't have the use cases currently; it is just there for implementation. Regarding Snowflake's automated scaling and suspension features, this auto-scaling is very significant. We had a comparison with Databricks and Snowflake a few months back, and this auto-scaling takes an edge within Snowflake; that's what our observation reflects.

Quotes from Members

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

Pros

"Since using Gretel.ai, the positive impact on my organization includes a reduction in manual effort and infrastructural cost in training the AI model due to compliance issues, resulting in faster development and reduced waiting time."
"I don't think it is difficult to maintain."
"I like the idea that you can assign roles and responsibilities, limiting access to data."
"We utilize Time Travel with Snowflake because this is a very useful feature; everyone finds it crucial because in conventional data platforms, it's very difficult to handle these kinds of things."
"The integration capabilities of the product are good and you get what you pay for when it comes to Snowflake."
"However, I would highly recommend it in terms of setting up data warehousing internally over an Azure solution, such as Synapse, or something else."
"The initial setup is straightforward. You just need to follow the documentation."
"The product's most important feature is unloading data to S3."
"The way it is built and designed is valuable. The way the shared model is built and the way it exploits the power of the cloud is very good. Certain features related to administration and management, akin to Oracle Flashback and all that, are very important for modern-day administration and management. It is also good in terms of managing and improving performance, indexing, and partitioning. It is sort of completely automated. Everything is essentially under the hood, and the engine takes care of it all. As a data warehouse on the cloud, Snowflake stands strong on its ground even though each of the cloud providers has its own data warehouse, such as Redshift for AWS or Synapse for Azure."
 

Cons

"Before concluding, I believe it is not for every organization due to costs."
"They do have a native connector to connect with integration tools for loading data, but it would be much better to have the functionality built-in."
"There are three things that came to my notice. I am not very sure whether they have already done it. The first one is very specific to the virtual data warehouse. Snowflake might want to offer industry-specific models for the data warehouse. Snowflake is a very strong product with credit. For a typical retail industry, such as the pharma industry, if it can get into the functional space as well, it will be a big shot in their arm. The second thing is related to the migration from other data warehouses to Snowflake. They can make the migration a little bit more seamless and easy. It should be compatible, well-structured, and well-governed. Many enterprises have huge impetus and urgency to move to Snowflake from their existing data warehouse, so, naturally, this is an area that is critical. The third thing is related to the capability of dealing with relational and dimensional structures. It is not that friendly with relational structures. Snowflake is more friendly with the dimensional structure or the data masks, which is characteristic of a Kimball model. It is very difficult to be savvy and friendly with both structures because these structures are different and address different kinds of needs. One is manipulation-heavy, and the other one is read-heavy or analysis-heavy. One is for heavy or frequent changes and amendments, and the other one is for frequent reads. One is flat, and the other one is distributed. There are fundamental differences between these two structures. If I were to consider Snowflake as a silver bullet, it should be equally savvy on both ends, which I don't think is the case. Maybe the product has grown and scaled up from where it was."
"Users considering adding the solution should understand that Snowflake can be used only for transactional processing, not for analytical processing."
"More data governance and access control features would be a welcome addition."
"Sometimes, I face some issues like not having permissions. These aren't as crucial, but there are common errors sometimes where the database is down, or a table is nullified and a new table is added and you are not given access to that."
"Snowflake needs transparency over costs and pricing."
"This solution could be improved by offering machine learning apps."
"Maybe there could be some more connectors to other systems, but this is what they are constantly developing anyway."
 

Pricing and Cost Advice

Information not available
"It is on a monthly basis. It is based on your usage. There are no additional costs from the point of the licensing fee. We do give some kind of evaluation to the customers about how much it is going to be. You can decide in Snowflake the virtual machine that you are using for customers. There are several kinds of virtual machines that you can use. It is similar to the clothing sizes: small to extra large. If you need more power in the coming month, you can decide in advance and take a more powerful machine. You can just select it from the platform. You can also decide which machine you want to take for extracting data."
"Snowflake is a cost-effective solution."
"I have worked with multiple clouds, and cost-wise, it is a bit costlier than others, such as Redshift. Its price should be reduced."
"I have not been billed yet, but it should be less. I'm still running the trial version, but it seems to be less than Databricks."
"Snowflake goes by credits. For a financial institution where you have 5,000 employees, monthly costs may run up to maybe $5,000 to $6,000. This is actually based on the usage. It is mostly the compute cost. Your computing cost is the variable that is actually based on your usage. It is pay-per-use. In a pay-per-use case, you won't be spending more than $6,000 to $7,000 a month. It is not more than that for a small or medium enterprise, and it may come down to $100K per year. Storage is very standard, which is $23 a terabyte. It is not much for any enterprise. If you have even 20 terabytes, you are not spending more than $400 per month, which may turn out to be $2,000 to $3,000 per annum."
"The whole licensing system is based on credit points. You can also make a license agreement with the company so that you buy credit points and then you use them. What you do not use in one year can be carried over to the next year."
"Users have to pay a licensing fee for the solution, which is expensive."
"It is hard to say because we're usually engaged in the transition as opposed to the long term. Their storage costs are easily within pennies of what AWS S3 would normally cost. Most of the clients I've been working with are in the financial sector, and they're relatively small. I would put them in an SMB connection. The first thing we have to bring up for people is that they're going to build this. They shouldn't store their data in S3. They should pipeline directly into Snowflake and use it on their storage. So, the cost is a big issue because these are small to medium size companies, and that is the biggest thing we had to price point for them."
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Top Industries

By visitors reading reviews
Construction Company
33%
Comms Service Provider
11%
Transportation Company
9%
Financial Services Firm
7%
Financial Services Firm
17%
Manufacturing Company
11%
Outsourcing Company
9%
Comms Service Provider
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise20
Large Enterprise61
 

Questions from the Community

What needs improvement with Gretel.ai?
Gretel.ai might need to enhance security purposes and provide better explainability on the use cases we are developing, including the addition of domain-specific templates and risk metrics. Regardi...
What is your primary use case for Gretel.ai?
My main use case for Gretel.ai is data generation and dataset generation. Additionally, my main use case with Gretel.ai involves a production database that contains millions of data points. We need...
What advice do you have for others considering Gretel.ai?
Gretel.ai is not for everyone. If you are handling sensitive data that can be used for AI and ML models, that is where you can use it. QA teams struggle with frequent data sharing with external ven...
What is your experience regarding pricing and costs for Snowflake?
We find the metering and billing experience for Snowflake to be expensive because there are many limitations to what it can do. When we compared performance-wise and cost-wise to Bedrock for some o...
What needs improvement with Snowflake?
Lessons learned about our main use case or how Snowflake performed in those areas include that the sales representatives do not really understand the product, and that the solutions consultants bas...
What is your primary use case for Snowflake?
Our main use case for Snowflake was to ingest data, use it for search, analytics, and data sharing with our customers; however, in reality, those use cases did not work. When it came to our challen...
 

Comparisons

 

Also Known As

No data available
Snowflake Computing, Snowflake Data Cloud
 

Overview

 

Sample Customers

Information Not Available
Accordant Media, Adobe, Kixeye Inc., Revana, SOASTA, White Ops
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