European Alternatives to Gretel
Looking for a European alternative to Gretel? The gretel.ai address now redirects to an NVIDIA page covering NeMo Data Designer and NeMo Safe Synthesizer, and NVIDIA Corporation lists its privacy contact in Santa Clara, California, so buyers who need an EU contracting party have to look elsewhere.
The European tools below generate synthetic tabular, medical and visual data from vendors registered in France, Spain, the Netherlands, Italy, Switzerland, Austria, Israel and the UK, several of which can run inside your own environment.
How we rank these tools — 4-step process
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1
European ownership, verified
The company is headquartered and incorporated in the EU, EEA, the UK, Switzerland or Israel, and processes customer data in Europe or Israel. A US parent company disqualifies a tool from this page regardless of where its servers are.
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2
Category fit and hands-on review
What the tool actually does, who it suits, and where it falls short — checked against the vendor’s own documentation, changelog and pricing page rather than its marketing copy.
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Compliance and pricing check
GDPR posture, hosting location and the prices quoted on this page are verified against the vendor’s public pricing before publication, and re-checked when we revisit the category.
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Position on this page
Placement on this page can be paid, and that can affect which tools appear here and the order they appear in. It never buys a good review: a tool that fails the checks above is not here at any price, and payment does not change the shortcomings we write about. A vendor can ask us to correct a factual error — not to remove a criticism.
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11 European Alternatives to Gretel
Octopize
Avatar anonymisation with synthetic data and public price tiers
Syntho
Masking, rule-based and AI-generated data in one engine
Dedomena
Madrid platform pairing validation, anonymisation and synthetic data
Aindo
Trieste synthetic data for clinical evidence and public health
MOSTLY AI
Open-source synthetic data SDK plus a managed data platform
Syntheticus
Zurich vendor of generative synthetic data for AI and testing
Sarus
Differential-privacy layer that also produces synthetic data
K2view
Entity-based synthetic data inside a test data platform
Anyverse
Physics-based sensor simulation for in-cabin and driving data
SKY ENGINE AI
Managed synthetic image generation for vision model training
Clearbox AI
Turin team offering synthetic data projects and a consumer panel
Key takeaways
- Opening gretel.ai now ends on an NVIDIA page covering NeMo Data Designer and NeMo Safe Synthesizer, and NVIDIA lists its privacy contact in Santa Clara, California.
- Octopize, the only vendor on this page with a printed price list, charges €15k, €48k or €83k for its Starter, Professional and Expert tiers, with a free first month.
- The MOSTLY AI SDK, described by the vendor as Apache-licensed, trains on your own hardware, so no table has to be uploaded to a hosted service.
- Anyverse and SKY ENGINE AI replace Gretel only for image and sensor data, not for tables or text.
- Aindo holds Europrivacy, ISO 27001 and ISO 9001 certificates, according to its own compliance section, and focuses on healthcare data.
Why people leave Gretel
Gretel is a synthetic data product: you train a generator on real data and receive a dataset that behaves like the original without containing the original rows. That is the job every tool on this page does, and it is the reason eleven European vendors list Gretel as the product they replace on their own pages.
What changed is the address. Opening gretel.ai now sends you, through a permanent redirect, to a page on nvidia.com about synthetic data generation, with NeMo Data Designer and NeMo Safe Synthesizer as the named products. The page itself does not mention Gretel. NVIDIA Corporation, whose privacy policy lists a contact in Santa Clara, California, is therefore the party a European buyer would have to look at when asking who processes the data.
For a privacy team the question is concrete: you are sending customer records, patient tables or transaction logs to a service so that they never have to leave the building again.
A US parent means US law can reach the processor, whichever region the training job runs in. We did not read Gretel's or NVIDIA's contract terms for this page, so the argument here is about jurisdiction and about what the European vendors put in writing, not about any specific clause.
- The product now lives behind a different company name A procurement file that says "Gretel" no longer matches the web address it points to. The redirect lands on NVIDIA pages for NeMo tools, and the legal contact for that site is a Californian one. If your data protection officer approved a vendor under a different name and entity, a renewal or a new project may need a fresh assessment anyway, and that is a natural moment to compare vendors with an EU contracting party.
- US jurisdiction over the processor NVIDIA Corporation is headquartered in Santa Clara, so the company that stands behind the product is subject to US legal process, such as requests under the CLOUD Act, wherever the compute sits. The eleven European tools with a contracting entity in France, Spain, the Netherlands, Italy, Austria, Switzerland, Israel or the United Kingdom answer to different regimes. That does not make them safer in every respect, but it changes who can compel access.
- Several European vendors let the data stay in your network Syntho deploys in the customer's environment, Octopize offers a Docker install, Sarus runs on-premise or in your own cloud, and the MOSTLY AI SDK trains on your own CPU or GPU. When the generator runs inside your perimeter, the contract question shrinks, because no raw table ever leaves for a hosted training service.
- Pricing you can put in a budget We did not verify Gretel's prices, so we make no comparison with them. What we can say is what the European side publishes: Octopize prints a free month and three paid tiers of €15k, €48k and €83k, MOSTLY AI offers a free hosted version, and Dedomena advertises a free start. Most other vendors here sell on quote.
What you have to replace, not just match
Replacing a generator is less about matching an algorithm than about matching a workflow. Write down what your team does with Gretel today: which data types go in (single tables, linked tables, time series, free text), who runs the jobs (data scientists in Python or analysts in a web interface), and what leaves the tool (a file, an API call, a quality report). Each European tool covers a different slice of that list.
Tabular and relational work is the largest group. Octopize, Syntho, Dedomena, MOSTLY AI and Syntheticus generate structured data, and Syntho and K2view add the test-data side with masking and subsetting.
Aindo is the specialist for patient cohorts and Sarus approaches the problem through differential privacy and query control instead of a file of fake rows. If you used Gretel for images or sensor output, only Anyverse and SKY ENGINE AI belong in the comparison; they do not generate tables at all.
Then there is what surrounds the generator: the evaluation report your auditors read, the scripts that call the API, and the approvals you already obtained. Plan to rebuild the scripts and to re-run privacy and utility checks on the new tool's output, because two generators trained on the same table will not produce the same distribution.
The alternatives compared
| Position | Tool | Headquarters | Pricing | Jurisdiction |
|---|---|---|---|---|
| #1 | Octopize | Nantes, France | Free for one month; Starter €15k, Professional €48k, Expert €83k (billing period not stated on the page) | EU (France) |
| #2 | Syntho | Amsterdam, Netherlands | Basic, Standard and Ultimate plans on quote; no consumption charges stated | EU (Netherlands) |
| #3 | Dedomena | Madrid, Spain | Start for free; paid pricing not published | EU (Spain) |
| #4 | Aindo | Trieste, Italy | See website; no prices published | EU (Italy) |
| #5 | MOSTLY AI | Vienna / Amsterdam, Austria | Free hosted version at app.mostly.ai; enterprise pricing on request | EU (Austria) |
| #6 | Syntheticus | Zurich (Dietikon address in terms), Switzerland | See website; no prices published | Switzerland (adequacy decision, outside the EEA) |
| #7 | Sarus | Paris, France | See website; no prices published | EU (France) |
| #8 | K2view | Yokneam, Israel | Pricing on request | Israel (outside the EU/EEA) |
| #9 | Anyverse | Madrid, Spain | Pricing on request; demo and possible trial | EU (Spain) |
| #10 | SKY ENGINE AI | London, United Kingdom | Pricing on request | United Kingdom (adequacy decision, outside the EEA) |
| #11 | Clearbox AI | Turin, Italy | See website; no prices published | EU (Italy) |
How each alternative compares to Gretel
Best for: Regulated organisations that want an EU contract and a budget figure before a sales call
Octopize sells the Avatar method, which turns a confidential table into a synthetic copy, from a Nantes company under French law. Set against Gretel, whose prices we did not verify, the first difference is transparency about money: tiers are printed, with caps on rows, columns, seats and annual support days, so you can see where your table fits before you ask.
Hosting is either its SaaS on Scaleway in France, which the page describes as HDS certified, or a Docker install on your own servers. Reference quotes come from French hospitals, a pharma company and an aerospace group. The CNIL assessment mentioned on the site is a vendor claim, so ask for the document.
What Octopize does better than Gretel
- Contracting entity in France instead of a US parent
- Printed tiers, so budgets can be set up front
- On-premise Docker option for sensitive tables
- Privacy audit built into the workflow
Where Octopize is a step down from Gretel
- Five-figure entry price excludes small projects
- Public site and documentation lead with French
- Annual import caps limit frequent refreshes
Standout against Gretel. It is the only tool here that lets a buyer calculate cost from a public page.
Best for: Software and QA teams copying relational databases into realistic test environments
Syntho of Amsterdam combines masking, rule-based generation and AI-learned generation in one run, aimed at developers who need production-like databases for testing. Compared with Gretel, you get a connector-based product that sits in your environment, and the vendor states it has no access to it.
Pricing is by features and connections with no fee per generation or per database, though no amounts are printed. The customer wall includes KPN, Philips, Nederlandse Spoorwegen and Statistics Netherlands. In June 2026 Syntho also announced the purchase of the MOSTLY AI brand, which may change product roadmaps.
What Syntho does better than Gretel
- Deployment in your own environment
- Test-data features such as subsetting and consistent mapping
- No usage-based fees stated
- Dutch legal entity under EU law
Where Syntho is a step down from Gretel
- No published prices or trial
- No certifications listed on the site
- Less suited to clinical cohort work
Standout against Gretel. Three generation methods can run together in a single job.
Best for: Finance and data teams wanting validation and synthetic generation under one Spanish contract
Dedomena offers synthetic data as one module in a platform that also validates datasets, anonymises them and shares them through a data space. For someone leaving Gretel, the draw is that a quality report on utility and privacy closes every generation, and that four algorithms are selectable, one of them aimed at banking data.
The company is DEDOMENA ARTIFICIAL INTELLIGENCE, S.L., and its terms give an address in Rivas-Vaciamadrid. Multi-table learning with referential integrity is stated on the product page. Hosting regions and paid prices are not published, so residency has to be agreed in writing.
What Dedomena does better than Gretel
- Anonymisation and validation in the same tool
- Free start without a procurement process
- Quality report after each run
- Spanish entity governed by EU law
Where Dedomena is a step down from Gretel
- Paid pricing and hosting region not published
- Broader scope means more modules to learn
Standout against Gretel. Generation, anonymisation and validation share one workspace instead of three products.
Best for: Hospitals, pharma and CROs that need clinical evidence from protected patient data
Aindo grew out of a research lab at SISSA in Trieste in 2018 and now directs its message at healthcare: synthetic cohorts, external control arms and real-world evidence. Against a general-purpose generator such as Gretel, this is a narrower and more clinical product.
The compliance section lists Europrivacy under Article 42 GDPR, ISO 27001 and ISO 9001, and the privacy policy places processing on EU servers. The platform page says it can be employed locally. Speed-up claims on the site are vendor statements, and pricing is not published.
What Aindo does better than Gretel
- Certificates listed for privacy, security and quality
- Clinical focus including control-arm use cases
- EU servers and local use possible
- Italian company under EU law
Where Aindo is a step down from Gretel
- Not aimed at generic software testing
- No pricing or free tier shown
- Newer language-model products are thinly documented
Standout against Gretel. It is the one vendor here built around trial and cohort evidence rather than general data.
Best for: Python-literate data scientists who want to audit and run the generator offline
MOSTLY AI is the closest to Gretel's developer-first feel. The Synthetic Data SDK installs with pip, trains on your CPU or GPU, and, according to the vendor, is Apache-licensed. Docker and air-gapped installs are covered in the documentation, so the table never leaves your machines.
It models single tables, linked tables, time series and text, and differential privacy is an advanced training option. The brand now belongs to Syntho B.V., announced on 9 June 2026, and the platform's operating entity could not be read from public pages, so confirm who signs your order form.
What MOSTLY AI does better than Gretel
- Runs entirely on your own compute
- Differential privacy option in training
- Free hosted version for trying it
- Auditable open-source SDK
Where MOSTLY AI is a step down from Gretel
- Contracting entity not verified
- Recent ownership change may alter terms
- Enterprise prices not public
Standout against Gretel. A data scientist can start with a pip install and no sales call.
Best for: Swiss and wider European enterprises planning synthetic data for models, tests or reporting
Syntheticus AG, founded in 2021 and based in Zurich, groups its offer into AI and LLM projects, software testing, and analytics. Compared with Gretel the main difference is jurisdiction: Switzerland holds an EU adequacy decision but sits outside the EEA, which some buyers prefer and some do not.
A blog post announces ISO 27001, and a trust portal is linked. The vendor claims synthetic data raised model performance by up to 3 percent, up to 5 percent when mixed with real data; treat those as vendor figures. Public pages show no prices, module list or hosting region.
What Syntheticus does better than Gretel
- Swiss legal entity instead of a US parent
- Separate tracks for AI, testing and analytics
- Trust portal for security review
Where Syntheticus is a step down from Gretel
- Pricing, modules and hosting region not public
- Outside the EEA, though adequacy applies
- Performance gains rest on vendor figures
Standout against Gretel. It publishes performance claims for model training, labelled as its own.
Best for: Analytics and security teams that need differential privacy guarantees more than a fake dataset
Sarus works differently from Gretel. Analysts connect to a dataset they cannot read, query rewriting makes each job privacy-safe, and output-level control limits what leaves. Synthetic data is produced as one route, by a generative model trained with differential privacy.
Sarus Technologies SAS is registered in Paris and deploys on-premise or in a public cloud with Docker or Kubernetes. Integrations listed include PostgreSQL, Snowflake, MongoDB, Tableau, SageMaker and Vertex AI. Pricing is not shown, and the licence of its GitHub components was not checked.
What Sarus does better than Gretel
- Differential privacy as the core mechanism
- On-premise or own-cloud deployment
- Access control beyond a one-off file
- French company under EU law
Where Sarus is a step down from Gretel
- Synthetic files are a secondary feature
- Query-based workflow differs from file generators
- No pricing published
Standout against Gretel. Analysts get results from data they never see in raw form.
Best for: Banks and telecoms with many connected systems needing entity-consistent test data
K2view of Yokneam, Israel, builds data by business entity, so a customer's records stay consistent across applications. Compared with single-table generators, this is aimed at organisations that already run test data management across mainframes, databases and cloud stores.
Methods listed are AI generation from masked patterns, rule-based generation, cloning, masking and subsetting. Israel is outside the EU and EEA. The privacy policy also lists a US address for US users, and the terms we read cover the website, so confirm which entity signs the product contract.
What K2view does better than Gretel
- Referential integrity across whole systems
- Masking and subsetting alongside generation
- Wide source and target support
Where K2view is a step down from Gretel
- Heavy platform for small teams
- Deployment model and pricing not stated
- Israeli entity, outside the EU and EEA
Standout against Gretel. Synthetic records follow the complete business entity, not one table.
Best for: Automotive, defence and robotics teams training or validating perception models
Anyverse produces image and sensor data rather than records, so it replaces Gretel only where your project is visual. Its engine simulates light, materials and sensors, with cameras, LiDAR, radar, time-of-flight and thermal among the models listed.
Output layers include RGB, semantic and instance labels, depth, normals and raw 16-bit images, and scenes can include rain, snow, fog and glare. The site lists TISAX certification and alignment with Euro NCAP and UNECE protocols. Pricing is on request and the cloud region is not named.
What Anyverse does better than Gretel
- Physical sensor models for perception work
- TISAX certification stated for automotive buyers
- Spanish entity under EU law
Where Anyverse is a step down from Gretel
- No tabular, text or time-series output
- Pricing and cloud region not published
Standout against Gretel. Scenes can be rendered deterministically and reproduced exactly.
SKY ENGINE AI
London image-data platform with ground truth labels and a domain-gap measuring tool
Best for: Vision AI engineers needing annotated image sets without collecting real footage
SKY ENGINE AI covers pictures, not tables. A comparison with Gretel makes sense only if the data you want to replace is visual, such as detection training sets for drones, security cameras or medical imaging.
The platform renders visible, NIR, thermal and UWB sensors with physically based rendering, and exports segmentation masks, 2D and 3D boxes, depth maps and keypoints. A domain inspector quantifies the gap between real and synthetic images. The operator is SKY ENGINE Limited in London, so the United Kingdom adequacy decision applies, not EU membership.
What SKY ENGINE AI does better than Gretel
- Labelled ground truth for vision models
- Measurement of the synthetic-to-real gap
- PyTorch and TensorFlow integration
Where SKY ENGINE AI is a step down from Gretel
- Image data only
- Pricing and hosting region not stated
- UK company, outside the EEA
Standout against Gretel. It measures how far synthetic images drift from real ones.
Best for: Italian and European organisations commissioning synthetic datasets from a small research-led team
Clearbox AI is a team with machine learning and data privacy roots, founded on 31 July 2019 in Turin, and the site includes an in-house CIPP/E certified privacy professional. No self-serve product, documentation or pricing was visible on the pages reviewed.
Stated uses are protecting sensitive data, augmenting data for ML and LLM work, and correcting bias. Recent content focuses on Replica Italia, a consumer panel built on card transactions, which is a data asset rather than a generator you operate. Expect to begin with a conversation.
What Clearbox AI does better than Gretel
- Privacy and research expertise on the team
- Italian entity under EU law
Where Clearbox AI is a step down from Gretel
- No visible self-serve product or documentation
- No pricing or hosting details
- Content is shifting toward Replica Italia
Standout against Gretel. It works as a project partner rather than a software licence.
What actually breaks when you switch
Moving off a hosted generator starts with the models you already trained. Generators are not portable: a trained model from one vendor cannot be loaded into another, so every dataset has to be regenerated from the source tables. Keep the original configuration, column choices and privacy settings written down, because they are the only thing that carries over.
Next come integrations and approvals. Scripts that call one vendor's API need rewriting, evaluation dashboards tied to its report format need replacing, and any privacy approval granted for the old pipeline has to be repeated for the new one. Contracts may also run on notice periods, and you cannot assume a redirect page tells you the termination terms, so read the agreement you actually signed.
Is Gretel still available as a separate product?
We can only report what we saw. Requesting gretel.ai returns a permanent redirect to an nvidia.com page about synthetic data generation, and that page names NeMo Data Designer and NeMo Safe Synthesizer rather than Gretel. We did not find a Gretel-branded sign-up or pricing page behind the redirect.
If you hold an existing contract, ask your account contact in writing which legal entity is the data processor, which product the service continues as, and how long the old interfaces stay supported. Those three answers decide whether you migrate this quarter or next year.
Can a European tool match what we generate today?
For single and linked tables, yes: Octopize, Syntho, Dedomena, MOSTLY AI and Syntheticus all list tabular generation, and Dedomena and MOSTLY AI state that they handle several related tables. Time series is listed by Octopize and MOSTLY AI. Free text is listed by MOSTLY AI and by Dedomena, which names text among its supported data types.
What you cannot take for granted is parity of quality. Run each candidate on the same table, compare column distributions and correlations, and run your own re-identification test. The vendors' accuracy figures, such as Syntheticus claiming gains of up to 3 percent in model performance, are their own and do not transfer to your data.
Which one to pick
For structured data with a European contract, start with Octopize if you need a price you can plan around, Syntho if you need test databases inside your network, and Dedomena if you want profiling and anonymisation next to generation. Choose the MOSTLY AI SDK when your team writes Python and wants to see the code that runs on its own hardware.
Choose Aindo for patient data, Sarus when query control matters more than a file of synthetic rows, and K2view when many linked systems must stay consistent. Pick Anyverse or SKY ENGINE AI only for image and sensor work, Syntheticus if Swiss law is a requirement, and Clearbox AI if you want a team to do the project with you. Stay with your current tool if nothing forces a move and your assessment still holds.
Frequently Asked Questions
It depends on the data. For tables with a printed price list, Octopize from Nantes. For relational test data deployed in your own network, Syntho from Amsterdam. For banking-style multi-table data with a free start, Dedomena from Madrid. For Python users who want an open SDK, MOSTLY AI. For patient data, Aindo from Trieste. For images, Anyverse or SKY ENGINE AI.
No law says so. Synthetic data that cannot be linked back to a person may fall outside the regulation, but whether yours does is a judgement about the output and the method. A European vendor removes the transfer question from the training step, and tools you run yourself remove it altogether.
Syntho deploys in the customer's environment and says it has no access. Octopize offers Docker and on-premise installs, Sarus runs on-premise or in your cloud with Docker or Kubernetes, and MOSTLY AI offers a local SDK plus a Kubernetes or OpenShift platform. Aindo says its platform can be used locally. For the others, deployment is not stated.
The licence is the smaller part. Rebuilding scripts, re-validating privacy and utility, and renegotiating internal approvals take most of the time. Octopize lists entry pricing from €15k, MOSTLY AI and Dedomena have free starting points, and the rest quote after a demo, so budget a proof of concept first.
No. Eight are registered in the EU (France, Netherlands, Spain, Italy, Austria). Syntheticus is Swiss, SKY ENGINE AI is British, and K2view is Israeli. Switzerland and the United Kingdom have EU adequacy decisions; Israel is accepted by EP but is not in the EU or EEA.
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