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Gretel.ai vs Tonic.ai comparison

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
Tonic.ai
Ranking in AI Synthetic Data
2nd
Average Rating
6.0
Reviews Sentiment
7.5
Number of Reviews
1
Ranking in other categories
Data Masking (20th), AI Software Development (36th)
 

Mindshare comparison

As of July 2026, in the AI Synthetic Data category, the mindshare of Gretel.ai is 7.5%, down from 11.7% compared to the previous year. The mindshare of Tonic.ai is 14.4%, up from 11.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Synthetic Data Mindshare Distribution
ProductMindshare (%)
Tonic.ai14.4%
Gretel.ai7.5%
Other78.1%
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.
Dev Sahu - PeerSpot reviewer
Senior Software Engineer at a tech vendor with 10,001+ employees
Automated realistic test data has improved delivery speed but still needs better cost and large-db support
There are areas where we can definitely improve Tonic.ai overall. It meets our requirements, but for very large databases, masking and synthetic data generation can take longer than expected. The initial configuration requires careful setup to define masking rules and preserve business logic. More out-of-the-box templates, deeper cloud integration, and AI-assisted rule recommendations would make it easier to use. I suggest improvements for better performance for large databases, more AI-driven automation, and improved CI/CD integration based on built-in plugins for common DevOps platforms. Easier pipeline configuration, monitoring, and better reporting can also be improved. Lastly, cost optimization with more flexible licensing options for smaller teams or development environments is required. Cost optimization is a primary concern, so more flexible licensing options for a smaller team or business environment can be improved. Additionally, support for broader data storage, such as NoSQL databases, data lakes, and cloud-native storage services, would be beneficial.

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."
"We see benefits such as an eighty to ninety percent reduction in manual effort, faster release cycles due to quicker environment setup, reduced DBA workload, better quality testing on realistic data, and fewer compliance violations and potential penalties."
 

Cons

"Before concluding, I believe it is not for every organization due to costs."
"I recommend Tonic.ai for organizations that frequently need it; if you are using a small set of data, it is good, but for large datasets, it can be costly, so use your data accurately."
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Top Industries

By visitors reading reviews
Construction Company
36%
Transportation Company
10%
Comms Service Provider
9%
Financial Services Firm
8%
Computer Software Company
20%
Construction Company
18%
Financial Services Firm
11%
Manufacturing Company
10%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
No data available
 

Comparisons

 

Interactive Demo

Demo not available
 

Overview

 

Sample Customers

Information Not Available
UnitedHealth Group, Texas Capital Bank, Avant, Philips, Walgreens, CVSHealth, Cigna, Volvo, eBay, Cityblock, Migros, the NHL