Software advice
Best AI Synthetic Data Platforms in 2026
AI synthetic data platforms are software products that learn the patterns in a real dataset, or follow a written schema, and then generate new records that behave like the original without copying any one person. Teams use the output for software testing, model training and data sharing. The category excludes plain random-value generators and basic masking scripts, which contain no learned model.
Quick answer
Tonic.ai ranks first for most engineering teams because its cheapest tier is public and it covers databases and text. MOSTLY AI suits data scientists who want an open SDK. Octopize fits European health and industry buyers who need published tiers and regulator-facing anonymization evidence.
Buying synthetic data starts with a question vendors blur: do you need a faithful statistical copy of a sensitive table, a safe test database that keeps foreign keys intact, or fresh text for training a language model? The tools differ sharply on each. This ranking covers a dozen products and notes which job each does best. We read vendor pricing, documentation and trust pages in November 2026. We did not generate data or run fidelity or privacy tests, so quality statements describe design and published claims. Before paying, train two shortlisted tools on one of your own tables and compare the results.
The 11 best AI synthetic data platforms in 2026, ranked
There is no score. Vendors can pay for more visibility on this website, for example a higher place in a ranking or more room on a page. Commercial relationships can also affect which products we feature and how prominently. How we rank.
| # | Tool | Best for | Free plan | Entry price |
|---|---|---|---|---|
| 1 | Tonic.ai | Engineering teams needing realistic test databases or LLM-generated mock data, from self-serve start to enterprise contract | Yes | $29 per month (Fabricate Plus) |
| 2 | MOSTLY AI | Data science teams training generators on tabular, multi-table or time-series data inside their own Python environment | Yes | Quoted |
| 3 | Syntho | Companies that want masking, rule-based mock data and AI-generated synthetic data in one engine for test environments | No | Quoted |
| 4 | NVIDIA NeMo Data Designer | ML engineers who need synthetic text, conversation and tool-use data to train or evaluate language models and agents | Yes | Quoted |
| 5 | DataCebo SDV | Python teams wanting an open-core library for relational synthetic data, with a paid tier for constraints | Yes | $500 per month plus usage (SDV Enterprise Base) |
| 6 | YData | Data science groups wanting profiling, tabular synthesis and synthetic documents in one workbench hosted on AWS or Azure | Yes | $1.00 per credit, pay as you go |
| 7 | Octopize | French and European health or industry groups that must justify anonymization to a regulator and want published pricing | Yes | €15,000 for the Starter tier |
| 8 | Aindo | Hospitals, health authorities and life-science teams generating synthetic patient cohorts or external control arms for studies | No | Quoted |
| 9 | Syntheticus | Swiss and European banks, insurers and healthcare groups wanting generative synthetic data for AI training, testing and analytics | No | Quoted |
| 10 | Betterdata | Large banks and public agencies that need synthetic data generated fully on-premise or in air-gapped environments | No | Quoted |
| 11 | Synthesized | SAP and large-enterprise QA teams needing masked, subsetted test data and agent test environments, bought annually | No | Quoted |
- 1
Tonic.ai
Tonic.ai takes first place because three products cover most synthetic data jobs and the cheapest one is priced openly. Fabricate uses a chat-driven Data Agent to build datasets from scratch, Structural de-identifies production databases, and Textual handles free text. It loses points because Structural pricing needs a sales call and Fabricate usage is metered per conversation turn.
Strong on
- Fabricate lists a free tier and a $29 Plus tier, so developers can try the agent before any sales call
- Structural covers PostgreSQL, MySQL, SQL Server, MongoDB, Snowflake, BigQuery and Databricks, with subsetting that keeps referential integrity
- Standard terms include a data processing agreement, and a SOC 2 Type II report is available through its trust center
Weak on
- Fabricate bills by LLM tokens, with a complex multi-table turn at roughly $0.37, so long sessions add up
- Tonic.ai may use Fabricate prompts and feedback to improve its agent, and the opt-out is a listed feature
Skip it if: Researchers who need statistically faithful copies of one sensitive table for publication, where a model-based generator with privacy reports fits better. Fabricate has a $0 tier with $5 monthly credits, and Plus adds $25. Structural and Textual are priced separately.
- 2
MOSTLY AI
MOSTLY AI ranks second because its open SDK lets a team train a generator locally and measure quality before paying anyone. The vendor describes a TabularARGN architecture, optional differential privacy and automated quality reports. The platform adds natural-language data analysis. It sits below Tonic.ai because the pricing page returned an error, and the brand is now operated by Syntho, so the roadmap is unsettled.
Strong on
- The Apache 2.0 SDK runs on your own CPU or GPU, so training data stays in your environment
- Handles single-table, multi-table and time-series generation, with differential privacy as a training option
Weak on
- The vendor's pricing page returned a not-found error when read in November 2026, so platform cost is unknown
- The home page now reads powered by Syntho, and what that changes for existing contracts is not documented there
Skip it if: Teams that want a managed test-data service with database connectors and subsetting, rather than a library they operate themselves. The Synthetic Data SDK is free under the Apache 2.0 license. The hosted platform shows no public price list.
- 3
Syntho
Syntho ranks third for buyers who dislike metered bills. Its pricing page describes feature-based tiers with no consumption charges, unlimited deployments and support included. One engine mixes masking, rule-based mockers, a sensitivity scanner and AI-generated synthetic data. Without a published number, comparison needs a quote, and the self-hosted engine means your team operates the deployment.
Strong on
- Price depends on chosen features and connections, not on how many rows or databases you generate
- Combines over 200 mockers, a sensitive-column scanner, subsetting and AI-generated data with a quality assurance report
Weak on
- No price appears publicly, so all three tiers need a quote before a budget exists
- The engine is self-hosted or on-premise, which adds an infrastructure task for the buyer
Skip it if: Solo developers and small teams who want to start on a free tier or a credit card this afternoon. Pricing is by feature set (Basic, Standard, Ultimate) on request, with no per-generation or per-database charges.
- 4
NVIDIA NeMo Data Designer
NVIDIA NeMo Data Designer ranks fourth because it targets a different job from the database tools above: building LLM training and evaluation datasets from a schema, seed data and model-driven columns. NVIDIA has also absorbed Gretel, whose address now redirects to an NVIDIA page. The tool is free to start, but it does not copy a production database.
Strong on
- Open source under Apache 2.0, with a pip install and a hosted trial that needs only an API key
- Defines datasets as columns with samplers, LLM-generated fields and validation, then previews before scaling up
Weak on
- Aimed at language-model data, so relational test data with foreign keys is outside its design
- NVIDIA's older NeMo Microservices docs announce a sunset on October 1, 2026, so product names are still moving
Skip it if: Software testers who need masked copies of production tables, since Data Designer builds new text datasets and does not subset databases. The Python library is Apache 2.0 licensed, and NVIDIA offers a hosted free trial with an API key.
- 5
DataCebo SDV
DataCebo's SDV sits fifth because it publishes a full price ladder yet asks for engineering time. The free Community edition suits experiments, while Enterprise adds more models, connectors and bundles for multi-table constraints, targeted sampling and differential privacy. The same company maintains the open SDMetrics and SDGym evaluation tools. It ranks below the hosted products because everything runs through code.
Strong on
- Enterprise Base at $500 a month with usage-based charges, plus $250 bundles for constraints or differential privacy
- Open SDMetrics and SDGym projects let a buyer score generators on their own data
Weak on
- Community edition allows only limited commercial use under its Business Source License
- Database connectors are a paid add-on bundle, so a no-code pipeline costs more than the base plan
Skip it if: Analysts without Python skills, who will find a point-and-click service such as Tonic.ai or Syntho easier to adopt. SDV Community costs nothing, with limited commercial use under a Business Source License. Bundles add $250 monthly each.
- 6
YData
YData ranks sixth because its credit price is simple and its free plan opens every feature. Fabric adds data profiling, a catalog, synthetic databases, question-answer pairs and synthetic documents, and it can be self-hosted from the AWS and Azure marketplaces. Below the leaders it sits there for lack of public security details and a thin trust page.
Strong on
- Free plan includes synthetic data generation, synthetic databases and automated profiling with a monthly credit
- Pay-as-you-go credits are $1.00 each and also pay for synthetic document and question-answer generation
Weak on
- Credit math (data points or tokens) is hard to turn into a monthly budget before testing real tables
- The pages read for this ranking list no certifications, so the vendor's policy was not checked for this
Skip it if: Regulated buyers who need audited certifications and a signed data processing agreement from the first call. A free plan gives a monthly credit. One credit covers 1 million data points or 10,000 tokens.
- 7
Octopize
Octopize lands seventh because its Avatar method is built around anonymization evidence, and its homepage says the CNIL, the French data protection authority, evaluated it successfully. Tier limits are public, from 10,000 lines on the trial up to over two million lines and five users on Expert. The prices are steep for small teams, and the pages read give no feature list for free-text data.
Strong on
- Pricing tiers are public, with line, column and user limits spelled out for each
- Includes a privacy audit module alongside generation and data augmentation on paid tiers
Weak on
- Entry paid tier of €15,000 is high against self-serve rivals, and the period it covers is unstated
- Page content focuses on structured data, with multi-table and time series only on paid tiers
Skip it if: Developers who want a free library or a monthly card plan, since Octopize sells annual-scale licenses. Free for one month, then €15,000, €48,000 and €83,000 tiers. The billing period is not stated beside the prices.
- 8
Aindo
Aindo ranks eighth because it has narrowed to healthcare evidence work. The site lists synthetic cohorts, control arms for trials, public health planning and clinical language models, plus Europrivacy, ISO 27001 and ISO 9001 certificates. That focus helps clinical buyers and hurts everyone else, and with no price page a budget cannot be set before a sales conversation.
Strong on
- Lists Europrivacy, ISO 27001 and ISO 9001 certification on its own homepage
- Offers synthetic control arms and real-world-evidence cohorts, a use case most generic platforms ignore
Weak on
- No pricing or self-serve trial appears on the pages read
- The product story is clinical, so a retail or banking test-data job gets little guidance
Skip it if: Software teams that need masked test databases, because Aindo's pages center on patient data and trial evidence. No public price list. The vendor sells to health systems, researchers and life-science teams through direct contact.
- 9
Syntheticus
Syntheticus is ninth because its Suite is described in general terms: GenAI-built samples for AI and LLM work, software testing, and analytics. The vendor cites ISO 27001 certification and a Gartner sample-vendor listing, which helps a procurement file. The product pages read give few connector, deployment or cost details, so a buyer learns the rest only in a demo.
Strong on
- States ISO 27001 certification and runs a public trust center
- Positions the Suite across AI and LLM training, software testing and BI in finance, insurance and healthcare
Weak on
- No pricing, plan names or free trial appear on the pages read
- Technical detail on supported databases and deployment options is thin before a demo
Skip it if: Teams that want to compare prices and test generators this week without booking a vendor call. Demo-led sales with no public price list. The vendor says it holds ISO 27001 certification.
- 10
Betterdata
Betterdata ranks tenth for a narrow buyer: organizations that must keep generation inside an isolated network. Its engine combines a tabular foundation model, GANs, tree-based models and transformers, with differential privacy and PII redaction. The case studies on its site are written by the vendor and name no figures it documents. Absent a price page or trial, evaluation depends on a sales process.
Strong on
- Ships as a hardened package deployable on-premise, air-gapped or on any cloud
- Offers several model families plus differential privacy guarantees and audit reports
Weak on
- No pricing, free tier or self-serve trial on the pages read
- Customer results on the homepage are vendor claims, and several customers are unnamed
Skip it if: Startups and small teams who need a free tier, public pricing or an open-source library. Sales-led with no public prices. The vendor describes one-VM to large cluster packages for on-premise, air-gapped or cloud use.
- 11
Synthesized
Synthesized closes the list because its public pages now center on SAP test data and AI agent environments, not general synthetic data. The packages are sized by data in scope, from 500 GB with one source up to 30 TB with twenty sources, and a fixed 10-day validation lets a buyer test on its own data. All prices need a sales call.
Strong on
- Sells a fixed-fee 10-day validation on your own SAP data before a subscription starts
- Also offers unstructured data masking, with a 48-hour proof on 50 of your documents
Weak on
- All package prices are contact-sales only, with no public rate card
- Messaging now leans toward SAP and agent testing, so general tabular synthesis gets less attention
Skip it if: Product teams that want a self-serve generator for tabular training data outside SAP and large enterprise stacks. SAP test-data packages run on annual subscriptions, with Starter covering 500 GB of production data. All prices are on request.
How these AI synthetic data platforms were judged
How do I know the synthetic data is realistic enough to use?
You only know by testing, because no vendor publishes a score that applies to your tables. Look for built-in quality reports and open evaluation tooling. MOSTLY AI's SDK produces automated quality reports during training, DataCebo maintains the open SDMetrics and SDGym projects, and Syntho provides a quality assurance report. Train a candidate on a table where you already know the answers, such as a churn model, and compare a model fitted on the synthetic copy against one fitted on the original. For relational data, check that join counts and foreign keys survive. For LLM-built data, read a sample by hand, since a language model can repeat itself.
What does synthetic data software cost, and why are so few prices public?
Published prices cluster at the developer end and vanish above it. As of November 2026, Tonic Fabricate lists a free tier with $5 of credits and a $29 monthly Plus plan, YData charges $1.00 per credit, and DataCebo lists SDV Enterprise Base at $500 a month plus usage. Octopize publishes tiers of €15,000, €48,000 and €83,000, though its page does not say the billing period. Syntho, Aindo, Syntheticus, Betterdata and Synthesized quote on request. Ask every vendor whether the fee scales with rows generated, source data size or features, because that choice decides your bill at ten times the volume.
Does the vendor see my real data, and can I generate it on my own servers?
This matters more here than in most software, because the generator is trained on your most sensitive records. Local operation settles it: MOSTLY AI's SDK runs in your Python environment, NeMo Data Designer is open source, Syntho's engine is self-hosted, and Betterdata describes air-gapped packages. For cloud use, read the contract. Tonic.ai includes a data processing agreement in its standard terms and offers a SOC 2 Type II report, yet says it may use Fabricate prompts to improve its agent. Aindo and Syntheticus cite ISO 27001. For YData, the vendor's policy was not checked for this.
How hard is it to leave a synthetic data vendor later?
Generated datasets are portable, so the real lock-in sits in the trained generators, rules and configuration. A generator you export as a file can be reused, which MOSTLY AI's SDK supports, and open-source libraries such as SDV and NeMo Data Designer keep your definitions in code you hold. Platform products that store masking rules, subsetting recipes and schema mappings inside their own interface are slower to replace. Ask whether you can export generator settings, whether the library license permits commercial use, and what happens to your tiers if the brand changes hands, as the MOSTLY AI and Gretel pages now show.
Which synthetic data platform fits a test team, a data science team or a hospital?
Match the tool to the job before the budget. A QA team refreshing staging databases should start with Tonic.ai, Syntho or Synthesized, which handle connectors, masking and subsetting. A data science team that wants a generator it controls should try MOSTLY AI, DataCebo SDV or YData. Builders of language-model datasets belong with NeMo Data Designer or Tonic Fabricate. Clinical researchers should talk to Aindo, and European organizations needing documented anonymization may prefer Octopize. If your data stays on one laptop, an open-source library may be all you need.
How to choose an AI synthetic data platform in three steps
- Name the job and the data type first. Write one sentence saying what the synthetic data will do. A refreshed staging database, a shareable patient cohort and a set of agent test conversations are three different purchases. The first needs connectors and referential integrity, the second needs privacy evidence, and the third needs a schema and a capable language model. Also list your source format: relational tables, one wide file, time series or free text. Drop any vendor whose pages do not mention your format, since a platform built for SAP tables will not suit a spreadsheet of events.
- Run the same real table through two shortlisted tools. Choose one table you understand, with a known outcome, and train two products on it. Every open-source option and most free tiers allow this: MOSTLY AI's SDK, SDV Community, NeMo Data Designer, Tonic Fabricate's free tier and YData's free plan. Compare the output on column distributions, correlations and a downstream model. Then run a privacy check, searching the synthetic rows for exact copies of real records. Keep the results in a short table so the decision rests on your data and not on a demo.
- Get the deployment and contract answers in writing. Before a sales call, list where training happens, who can see the source rows, what the data processing agreement covers, and which certificates exist. Ask for the trust center or report, not a verbal yes. Request the price in your own units, such as terabytes of source data, rows per month or seats, and ask how it changes at ten times the volume. Where a brand has been sold, as with MOSTLY AI, ask which legal entity signs the contract and who will support the product in two years.
Frequently asked questions
What is the best AI synthetic data platform in 2026?
Tonic.ai ranks first here because it covers databases, free text and prompt-built datasets, and its cheapest tier has a public price. MOSTLY AI ranks second for data scientists who want an open SDK, and Syntho third for teams that prefer feature-based pricing without consumption charges. The right pick still depends on whether you need test data, shareable data or training data.
What is the difference between synthetic data and masked data?
Masked data keeps the original rows and replaces or scrambles sensitive fields, so it can still be linked back if too much remains. Synthetic data is newly generated from a learned model or a rule set, and the records do not correspond to real people. Many suites, including Tonic.ai and Syntho, offer both, so ask which method each table uses.
Is there a free AI synthetic data tool?
Yes, several. MOSTLY AI's Synthetic Data SDK and NVIDIA's Data Designer library are Apache 2.0 licensed, DataCebo's SDV Community edition costs nothing with limited commercial use, YData has a free plan with a monthly credit, and Tonic Fabricate gives $5 of monthly credits. Free options suit evaluation and small jobs more than regulated production use.
Can synthetic data still expose real people?
It can if the generator memorizes rows or the dataset has rare combinations of values. Vendors reduce the risk with differential privacy, which MOSTLY AI, DataCebo and Betterdata mention, and with privacy audits, which Octopize sells as a module. No tool removes the need to test your own output for near-copies of real records.
How much does synthetic data software cost?
As of November 2026, entry prices run from free to $29 per month for Tonic Fabricate Plus, $500 per month plus usage for SDV Enterprise Base, and €15,000 for Octopize Starter. Syntho, Aindo, Syntheticus, Betterdata and Synthesized quote on request. Enterprise suites are usually priced on data volume or features.
What happened to Gretel?
Gretel's web address now redirects to an NVIDIA page about synthetic data for agentic AI, and NVIDIA's NeMo Data Designer and Safe Synthesizer carry that work forward. News reports from March 2025 described NVIDIA buying the company. This ranking lists NeMo Data Designer instead of a standalone Gretel product, and a current Gretel customer should confirm contract terms with NVIDIA.
Do I need synthetic data for software testing or only for AI training?
Testing is the older use. A test team needs realistic rows with intact relationships, without real customer data in staging, which is what Tonic Structural, Syntho and Synthesized target. AI teams use synthetic data to add rare cases or to share datasets. Many buyers need both, so check each tool covers each job.
Which synthetic data platforms are European?
Syntho is based in Amsterdam, Octopize in France, Aindo in Trieste, Italy, Syntheticus in Switzerland and Synthesized in London. MOSTLY AI began in Vienna and now says it is powered by Syntho. Tonic.ai, DataCebo, YData and NVIDIA are US companies, and Betterdata is in Singapore. Hosting region still needs checking per contract.