AIMultiple ResearchAIMultiple Research

Synthetic Data Tools Selection Guide & Top 7 Vendors in 2024

As data-centric approaches gain prominence in AI/ML development, the use of synthetic data tools is expected to become more common1. A survey of 300 computer vision specialists has shown %96 of them already using synthetic data2.

With the growing market size of synthetic data3, it can be difficult for businesses to choose the most suitable synthetic data vendor. To support a data-driven vendor selection process, we have:

  • Provided a step by step guide to identify the right synthetic data vendor for your business
  • Selected the top synthetic data vendors based on market presence
  • Categorized them according to these criteria:  
    • Source code (i.e. open vs closed) 
    • Supported data types
    • Market presence
    • Use cases
    • Industries

Verify that your business requires synthetic data

Synthetic data is the future of machine learning and will transform testing, but is not necessary in every machine learning use case.

Before checking synthetic data vendors, you should verify that one of these are true:

  • Testing: Privacy requirements prevent you from using actual data in testing. For example in banking,
    • Customers’ data are prohibited to be used in testing
    • Data not containing sensitive information, like hardware-resource-usage, can be used without concern
  • In all use cases: Having more data will improve your business outcomes

Identify your business’ synthetic data use case

Industries that rely on big data can benefit from synthetic data for:

  • AI model training 
  • Product development 
  • Testing

Given the broad applicability of synthetic data, not every vendor can support every business case. Therefore, it is important to first identify important synthetic data use cases for your business.

Identify the types of synthetic data your business needs

Your use case determines the type of synthetic data that is required. For example, a company building autonomous vehicles will require synthetic videos; a bank using synthetic data for testing will require synthetic tabular data.

After checking that the vendor supports your use case, check that they also support the specific data types that you require. For example, a vendor may claim to support synthetic data generation for banks’ end-user information with tabular data. However, your bank may also require users’ photos during testing, thereby needing facial images to be synthesized, too.

Common data types for synthetic data include

Structured data

  • Quantitative, machine readable, and tabular (i.e. possible to be represented in a table)
  • Records such as credit card information, inventory counts, patients’ age, etc. 
  • Easily interpretable and sortable

Unstructured data

  • Qualitative, or at least includes qualitative aspects. Therefore it is not machine-readable
  • Data such as social media posts, images, videos, free text, etc. 
  • Possible to create structured metadata from unstructured data. For example, image metadata can include the elements in the image, which would allow users to sort those images that include certain elements (e.g. cats) 
  • Not sortable without use of metadata
  • Used in all industries and business functions. But domains like autonomous vehicles and video platforms use higher volumes of unstructured data than others

Decide whether to use open source synthetic data

Closed-source synthetic data companies claim that their solutions are more preferable in cases where sensitive data is involved or when speed and ease of use are important4.

Advantages of closed-source solutions include:

  • Ease of implementation, since they can provide consulting services along with their software.

Advantages of open-source solutions include the typical advantages of open-source, such as: 

  • Easier initial adoption without waiting for the sales cycle
  • Increased transparency
  • Increased control regarding customizing the solution

This is a fast evolving market. Capabilities of both open and closed-source tools are quickly evolving, and it is hard to generalize. We recommend testing a few open and closed-source alternatives to see if they serve the specific needs of your project.

Prepare a short list of vendors

Below is all the relevant synthetic data vendors, categorized and selected for your short based on the criteria we outlined.

To identify the companies to include in this list, we used a verifiable, measurable and relevant metric: The list includes all vendors with more than 40 employees. 40 is an arbitrarily selected number, but employee count is correlated with a company’s market presence, which is correlated to the success of its products. Therefore, we can’t be sure if we set the right limit. But there needs to be a limit for the list to focus on vendors that can successfully serve enterprises.

Note: This table is in descending order of employee count. We might have missed some companies. For the most up-to-date version of this list, check out our data-driven list of synthetic data generators.

VendorSource codeData types# of EmployeesUse casesIndustries
Tabular139- Anonymous patient data analytics- Healthcare
DatagenClosedImages & videos of faces & objects121
- Smart homes - Smart fitness - In-cabin driving - In-meeting behavior - AR/VR/Metaverse
- Tech
- Real-estate
Tonic.AIClosedTabular109- Staging environment and production data

- Healthcare
- Retail
- Education
- Insurance
BizDataX (A subsidiary of Ekobit)ClosedTabular80- Product testing
- Data testing
- Tech
- Finance
- Banking
- Railway
- Logistics
Mostly.AIClosedTabular58- Data collaboration
- Data testing
- Banking
- Insurance
- Telecommunications
Gretel AIOpenTabular54- Data testing- Education
- Finance
- Banking
- Genomics
HazySome repositories are available as open sourceTabular48- Fraud detection
- Risk modelling
- Data monetization
- Banking
- Finance

Number of reviews on review platforms is another metric for market presence of tech firms. We are preparing a video that shows the evolution of number of reviews of synthetic data vendors. We included the products with the highest number of reviews, regardless of company size. This list excludes companies that did not claim to offer synthetic data solutions on their website but were listed on review platforms under synthetic data category.

Please check back here next week to see the video of B2B review evolution in synthetic data.

Choose your synthetic data provider

In addition to following B2B emerging tech procurement best practices, we recommend synthetic data providers to run short PoCs to see the synthetic data solutions at work. For more, please read our recommendations on buying innovative tech solutions which is quite applicable to synthetic data.

Finally, buyers need to measure or estimate the impact thanks to synthetic data on their organization. No procurement process should be complete without an ROI estimate of the purchase.

For more on synthetic data

To learn more about synthetic data and its use cases, read:

And if you believe you would benefit from using synthetic data in your business, we have a data-driven of synthetic data vendors.

We can support you through the vendor selection process:

Find the Right Vendors

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
Follow on

Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

To stay up-to-date on B2B tech & accelerate your enterprise:

Follow on

Next to Read


Your email address will not be published. All fields are required.