Who are the leading providers of synthetic data… | Parse
Who are the leading providers of synthetic data generation for training AI models without compromising user privacy?
Data as of Sep 26, 2026 · Based on 285 AI responses from ChatGPT Search and Google AI Mode · See how Parse measures this
Leading providers include MOSTLY AI, Hazy, and K2view for enterprise-grade structured and tabular data needs, frequently used in highly regulated sectors like banking and insurance. For developers and AI engineers, .ai provides an API-first approach for tabular and text data. For computer vision, robotics, and autonomous driving, DataGen, Parallel Domain, and specialize in high-fidelity, photorealistic visual data generation.
Best for high-fidelity tabular and text data in banking and healthcare. Recognized for automated privacy-compliant generation, it is a primary choice for teams needing synthetic twins of structured data.
Top 10: Synthetic Data Tools | AI Magazinehttps://aimagazine.com/news/top-10-synthetic-data-tools
5%
Best synthetic data generation tools for 2026https://www.k2view.com/blog/best-synthetic-data-generation-tools/
4%
Top 7 Synthetic Data Companies: How to Choose the One?https://www.syntho.ai/top-7-synthetic-data-companies-how-to-choose-the-one/
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Synthetic data companies: Which are best, and which are best for you?https://www.k2view.com/blog/synthetic-data-companies/
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Top 6 Synthetic Data Generation Tools [2026]https://averroes.ai/blog/synthetic-data-generation-tools
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Top 14 Synthetic Data Generation Tools in 2024 | JFrog MLhttps://www.qwak.com/post/top-synthetic-data-generation-tools
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Best Synthetic Data Providers & Companies 2026 | Dataradehttps://datarade.ai/data-categories/synthetic-data/providers
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Leading Synthetic Data Solutions for AI Development and Testing in 2025https://linuxsecurity.com/news/security-trends/best-synthetic-data-generation-tools
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Best Synthetic Data Startups (2026) | Seedtablehttps://www.seedtable.com/best-synthetic-data-startups
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Top Synthetic Data Generation Companies Powering AI Innovationhttps://aisuperior.com/synthetic-data-generation-for-ai-companies/
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Question: Who are the leading providers of synthetic data generation for training AI models without compromising user privacy?
If you mean commercial platforms that generate realistic training data while providing explicit privacy controls, the leading names to evaluate in 2026 include:
Provider
Strongest fit
Privacy approach
Data types
MOSTLY AI / Syntho
High-fidelity enterprise ML training
Differential privacy, privacy-risk evaluation, local deployment
The ones I'd put on a serious enterprise shortlist
1. MOSTLY AI / Syntho — particularly strong if your objective is high-fidelity tabular data for ML training. Its current SDK supports differential privacy and reports on generator quality; its documentation says DP can be enabled during training so generated data inherits the DP guarantee.
2. Gretel, now part of NVIDIA — particularly interesting for AI-model training, text, and multimodal data. Gretel positions its platform around generating artificial datasets with the characteristics of real data while protecting privacy, and NVIDIA's current materials confirm that Gretel's team joined NVIDIA through its 2025 acquisition.
3. Tonic.ai — a strong option when you need both synthetic data and de-identification of existing enterprise data. It supports rules-based and AI/statistical synthesis, masking, encryption, differential privacy, and PII detection; it also offers datasets specifically intended for AI training and evaluation.
4. SDV / DataCebo — the option I'd examine if you want maximum control or an open-source foundation. SDV provides generators for tabular and relational data, while its SDMetrics ecosystem can explicitly measure disclosure risk, overfitting, and other privacy properties. Its Enterprise offering also includes differential-privacy capabilities.
An important caveat about "privacy"
Synthetic data isn't automatically anonymous. A generator can memorize unusual records or reproduce information that allows an individual to be identified. Research comparing synthetic-data vendors has specifically cautioned that regulatory compliance claims aren't equivalent to a formal privacy guarantee.
For genuinely privacy-sensitive training, I'd therefore look for three things:
Differential privacy (DP): preferably a documented mathematical privacy budget (ε), rather than simply a vendor saying the data is "anonymous."
Disclosure/re-identification testing: e.g. nearest-neighbor, membership-inference, or disclosure-protection tests.
Utility testing: demonstrate that models trained on the synthetic data actually perform comparably on the intended downstream task. SDMetrics, for example, explicitly separates privacy/quality metrics from downstream ML efficacy.
Bottom line: for a shortlist, I'd investigate MOSTLY AI/Syntho, Gretel/NVIDIA, Tonic.ai, and SDV/DataCebo first, then narrow based on whether you're generating tabular enterprise data, LLM/text data, healthcare data, or multimodal data. I would not choose solely from a generic "best synthetic data" ranking because privacy guarantees and data modality matter more than overall market prominence.
1. MOSTLY AI / Syntho — particularly strong if your objective is high-fidelity tabular data for ML training. Its current SDK supports differential privacy and reports on generator quality; its documentation says DP can be enabled during training so generated data inherits the DP guarantee.
2. Gretel, now part of NVIDIA — particularly interesting for AI-model training, text, and multimodal data. Gretel positions its platform around generating artificial datasets with the characteristics of real data while protecting privacy, and NVIDIA's current materials confirm that Gretel's team joined NVIDIA through its 2025 acquisition.
3. Tonic.ai — a strong option when you need both synthetic data and de-identification of existing enterprise data. It supports rules-based and AI/statistical synthesis, masking, encryption, differential privacy, and PII detection; it also offers datasets specifically intended for AI training and evaluation.
4. SDV / DataCebo — the option I'd examine if you want maximum control or an open-source foundation. SDV provides generators for tabular and relational data, while its SDMetrics ecosystem can explicitly measure disclosure risk, overfitting, and other privacy properties. Its Enterprise offering also includes differential-privacy capabilities.
An important caveat about "privacy"
Synthetic data isn't automatically anonymous. A generator can memorize unusual records or reproduce information that allows an individual to be identified. Research comparing synthetic-data vendors has specifically cautioned that regulatory compliance claims aren't equivalent to a formal privacy guarantee.
The market for synthetic data generation tailored for AI training and strict privacy compliance features several leading providers, categorized by the type of data they specialize in (tabular/structured data vs. computer vision/visual data):
Tabular, Structured, and Enterprise Data Providers
MOSTLY AI : A prominent leader in generative AI-powered synthetic data for tabular and structured customer data. It uses a privacy-by-design approach with built-in differential privacy guarantees, ensuring generated datasets retain statistical properties without 1:1 links to original records.
Tonic.ai : Known for Tonic Fabricate and enterprise test data management, Tonic enables developers and AI engineers to mimic production databases safely, producing mathematically representative, privacy-safe data.
K2view : Combines AI- and rules-based generation with entity-based data management. It provides real-time, relationship-preserving synthetic data generation targeted at heavily regulated industries like banking, telecom, and healthcare while maintaining compliance with frameworks like GDPR and HIPAA.
Synthesized : A platform that accelerates ML development and testing by automating the creation of high-quality, privacy-compliant tabular datasets while bypassing legacy bottlenecks of manual anonymization.
Protegrity : Offers specialized privacy-safe synthetic data capabilities integrated into an overarching enterprise data security governance platform, aimed at reducing re-identification risks during AI training and analytics sharing.
Visual and Human-Centric Computer Vision Providers
Datagen : Specializes in high-performance, photorealistic synthetic visual data. It is widely adopted by major tech companies to train human-centric computer vision models (such as AR/VR, robotics, and automotive monitoring) without needing to capture or compromise real-world human biometric privacy.
Synthesis AI : A pioneer in generating photorealistic, fully-labeled 3D facial and body data. Their on-demand platform eliminates the need for sensitive real-world human data collection, allowing teams to build unbiased computer vision and perception models securely.
Parallel Domain : Focuses on generating hyper-realistic 3D simulated environments and sensor data primarily utilized for training and validating autonomous vehicle and robotics AI safely.
Are you looking to generate tabular/customer data or computer vision/visual data , and do you have a specific privacy regulation (like GDPR or HIPAA) you need to comply with?
The landscape for privacy-first synthetic data generation is categorized by specialized platforms focused strictly on tabular, relational, or structured data privacy, alongside vision-focused pioneers for computer vision and robotics.
The leading providers specializing in training AI models without compromising user privacy include:
Dedicated Privacy-First & Tabular Synthetic Data Platforms
MOSTLY AI
Focus: Widely recognized as a market leader for privacy-safe tabular, customer, and time-series data generation.
Privacy Approach: Uses generative AI models to learn statistical patterns, correlations, and distributions from real data, then synthesizes completely new data from scratch with built-in differential privacy guarantees and strict anti-overfitting mechanisms to block data memorization.
Tonic.ai
Focus: Enterprise test data management and advanced database synthesis (including their Fabricate tool for building realistic relational data from scratch).
Privacy Approach: Transforms and masks sensitive production environments or builds mathematically guaranteed safe datasets while preserving referential integrity across complex multi-table databases.
Hazy
Focus: Enterprise-grade synthetic data engineered heavily for heavily regulated sectors like banking, insurance, and telecommunications.
Privacy Approach: Embeds differential privacy metrics directly into the generation pipeline, allowing data teams to quantitatively dial in privacy thresholds before training downstream machine learning models.
Gretel.ai / NVIDIA NeMo Data Designer
Focus: Developer-first, API-driven synthetic data generation.
Privacy Approach: Provides developer-friendly Python frameworks and differential privacy primitives designed to inspect, validate, and purge privacy risks (like PII leakage) from datasets designated for LLM and predictive model fine-tuning.
Syntho
Focus: AI-generated synthetic privacy-compliant data targeted at replacing production data for secure analytics and AI training.
Privacy Approach: Specializes in rapid, high-utility privacy-preserving synthesis tailor-made to comply with stringent European and global privacy laws (GDPR/HIPAA).
Computer Vision, Autonomy, and Specialized Data Providers
Datagen
Focus: Human-centric computer vision, AR/VR, and biometrics.
Privacy Approach: Instead of scraping real human faces or sensitive biometric streams, Datagen simulates photorealistic 3D human environments and anatomical variations from the ground up, entirely bypassing real-world privacy and consent issues.
Synthesis AI
Focus: High-resolution photorealistic synthetic data for computer vision, robotics, and smart devices.
Privacy Approach: Generates completely synthetic human avatars and 3D simulation scenarios on demand, eliminating reliance on real human imagery or surveillance data.
Are you looking to generate synthetic data for tabular/financial records, healthcare data , or computer vision/images ? Let me know so I can suggest the best platform fit and technical approach.
The market for privacy-preserving synthetic data generation (SDG) features specialized pure-play startups, enterprise data governance giants, and hardware-software ecosystems. These providers use techniques like differential privacy, generative adversarial networks (GANs), and photorealistic simulations to build AI training sets without exposing real-world personal identifiable information (PII).
The leading providers are organized by their primary data focus and enterprise adoption:
Specialized Privacy & Tabular/Text Data Platforms
MOSTLY AI – Widely recognized as a market leader for privacy-safe tabular and relational data synthesis. It is heavily adopted by European financial institutions and telecom operators, offering mathematically proven differential privacy guarantees alongside automated fidelity and utility reporting.
Gretel.ai – Provides developer-first APIs and SDKs for generating privacy-preserving synthetic data. Gretel specializes in balancing privacy and utility, allowing developers to test and train machine learning models while embedding strict privacy filters and differential privacy mechanisms.
Tonic.ai – Well-known for enterprise data de-identification and synthesis, Tonic expanded aggressively into unstructured text data (via Tonic Textual) to help organizations safely sanitize and prepare sensitive documents, customer logs, and transcripts for AI training.
Hazy – Focuses heavily on highly regulated verticals like financial services. Hazy's platform specializes in generating complex statistical synthetic data while ensuring that original customer records cannot be reverse-engineered or re-identified.
K2view – Recognized as an enterprise leader in end-to-end synthetic data lifecycle management. It combines AI-driven rule-based generation with an entity-based micro-database approach to provision real-time, compliant test data and ML training data at scale.
Datagen – A dominant player in human-centric computer vision. Datagen builds high-fidelity, photorealistic 3D simulated data used to train AI models for in-cabin vehicle safety, AR/VR, and robotics without needing real-world human surveillance or imagery.
Synthesis AI – Specializes in generating photorealistic synthetic human avatars and 3D labeled environments for computer vision, autonomy, and metaverse applications, bypassing real-world privacy and data-labeling bottlenecks.
Enterprise Cloud & Infrastructure Giants
NVIDIA – Leads through infrastructure frameworks like NVIDIA Omniverse and NeMo , providing GPU-accelerated simulation environments and synthetic data pipelines primarily tailored for robotics, autonomous driving, and industrial AI.
IBM – Integrates enterprise-grade synthetic data generation and ethical governance tools directly into broad ecosystems like IBM Cloud Pak for Data , targeting highly regulated corporate users needing strict compliance enforcement.
If you have a specific use case in mind, tell me:
What type of data you are working with (tabular, text, or computer vision/images)
Your primary regulatory framework (e.g., HIPAA, GDPR)
I can help you narrow down which platform fits your technical architecture best.
Yes. The synthetic-data market has consolidated considerably, and the strongest providers depend on whether you're training tabular ML models, LLMs, or computer-vision models. Importantly, synthetic data is not automatically privacy-safe—a generator can memorize or reproduce unusual records—so I would prioritize vendors that provide measurable privacy testing, controls, and private/on-prem deployment.
Generate data within controlled environments; quality/privacy evaluation
Strong for AI/LLM development at scale
Tonic.ai
Enterprise databases & LLM training
De-identification plus synthesis; strong relational-data handling; HIPAA-oriented workflows
Excellent for healthcare and enterprise data
Hazy / SAS Data Maker
Financial services & regulated data
Synthetic data designed for privacy-preserving data sharing
Worth considering for banks/insurers
YData
Data quality + synthetic data workflows
Data-centric tooling and privacy-oriented synthetic generation
Good for data-science teams
DataCebo / SDV
Open-source synthetic data
Local generation; avoids sending source data to a SaaS vendor
Best starting point for developers/researchers
A current 2026 market review identifies MOSTLY AI, Tonic.ai, NVIDIA's NeMo Data Designer, YData, Syntho, and Hazy among the leading platforms.
Particularly interesting choices
MOSTLY AI is probably the strongest choice if your primary requirement is high-fidelity tabular data without exposing individual customer records. Its platform specifically addresses rare categories, extreme values, and long sequences—areas where synthetic generators can otherwise inadvertently leak information. It also supports private-cloud and air-gapped deployments.
NVIDIA/Gretel is especially compelling if you're generating data for generative AI, LLM fine-tuning, or multimodal applications. Gretel was acquired by NVIDIA, and its technology is being incorporated into NVIDIA's data-generation ecosystem.
Tonic.ai is particularly strong when your starting point is sensitive production data rather than an empty dataset. Its Fabricate product can generate synthetic training data, while Tonic Textual can detect and replace sensitive entities in unstructured text—useful for LLM development.
I wouldn't evaluate these companies solely on the claim that their output is "anonymous." Synthetic data can still leak information through memorization, rare records, or membership/re-identification attacks. A serious procurement evaluation should ask for:
Differential privacy, if appropriate for your threat model
Membership-inference and record-linkage testing
Quantitative privacy-risk scores
Utility/fidelity measurements
Ability to run inside your VPC, private cloud, or on-premises
Controls preventing raw customer data from leaving your environment
Audit logs and lineage
Independent security/compliance audits
Evidence that synthetic records cannot simply reproduce training records
For a regulated enterprise, my shortlist would be MOSTLY AI, NVIDIA/Gretel, and Tonic.ai. For a developer/research team wanting local/open tooling, I'd start with SDV. For LLM-specific synthetic training data, I'd put NVIDIA/Gretel and Tonic.ai toward the top.
If you tell me whether you're interested in healthcare, finance, customer data, LLMs, computer vision, or general ML, I can narrow this to the 3 best vendors and compare privacy guarantees, deployment options, pricing, and enterprise certifications.
MOSTLY AI is probably the strongest choice if your primary requirement is high-fidelity tabular data without exposing individual customer records. Its platform specifically addresses rare categories, extreme values, and long sequences—areas where synthetic generators can otherwise inadvertently leak information. It also supports private-cloud and air-gapped deployments.
NVIDIA/Gretel is especially compelling if you're generating data for generative AI, LLM fine-tuning, or multimodal applications. Gretel was acquired by NVIDIA, and its technology is being incorporated into NVIDIA's data-generation ecosystem.
If you’re evaluating vendors specifically for privacy-preserving synthetic data for AI/ML training, the leading options in 2026 include:
Provider
Particularly strong for
Privacy approach
MOSTLY AI
High-fidelity tabular, relational and time-series data
Privacy controls, privacy metrics, differential privacy options, and local deployment
Tonic.ai
LLM training, sensitive text, healthcare and enterprise databases
PII detection/de-identification plus synthetic replacement; supports HIPAA-oriented workflows
NVIDIA (Gretel technology)
Generative AI, tabular/text/time-series synthetic data
Synthetic-data generation with privacy and quality evaluation; enterprise/local deployment options
DataCebo / SDV
Developers wanting an open-source framework
Programmable synthetic-data models and privacy evaluation; useful for building your own pipeline
YData
Data preparation, tabular synthesis and ML workflows
Synthetic data plus data-quality/privacy tooling
Syntho
Enterprise synthetic data and regulated industries
Privacy-preserving generation with governance and compliance capabilities
These vendors are repeatedly identified among the leading platforms in current 2026 comparisons.
My shortlist
1. MOSTLY AI — best for privacy-conscious tabular ML
MOSTLY AI is particularly compelling when you have sensitive customer, financial, healthcare, or telecom data and want synthetic data that retains complex statistical relationships. Its platform includes privacy protections for rare categories, extreme values and long sequences, and supports on-premises/air-gapped deployment.
2. Tonic.ai — best for sensitive text and LLM training
Tonic has become particularly interesting for AI teams because Tonic Textual can detect sensitive entities in unstructured data and replace them with realistic synthetic values, while preserving context for LLM fine-tuning. It also handles structured relational data.
3. NVIDIA/Gretel — best for broad generative-AI workloads
Gretel's technology is now part of NVIDIA's synthetic-data ecosystem. It covers tabular, text and other modalities and was designed specifically around generating training data while preserving privacy.
“Synthetic” does not automatically mean “private.” A generative model can potentially memorize unusual or identifiable records from its training data. For sensitive applications, I'd therefore look for vendors that provide measurable privacy testing, not merely claims that the output contains no PII. Privacy risk, utility/fidelity, membership-inference risk, and re-identification risk should all be evaluated.
For a regulated organization, I'd prioritize MOSTLY AI or Tonic.ai; for an AI engineering team wanting a broader generative-data platform, NVIDIA/Gretel is worth serious consideration.
If you tell me whether your data is text/LLMs, healthcare, financial, customer/CRM, images/video, or tabular, I can narrow this to the 3 best vendors and compare their privacy guarantees, deployment model, pricing, and APIs.
The landscape of privacy-preserving synthetic data generation has matured rapidly, shifting from basic statistical masking to advanced, AI-driven generation that mimics real-world distributions while containing zero real Personally Identifiable Information (PII).
Leading providers in this space span enterprise tabular data, unstructured text, and high-fidelity computer vision simulation:
Tabular, Relational, and Enterprise Data
K2view : Recognized for enterprise-scale data operations, K2view uses an entity-based approach with micro-databases to deliver real-time, policy-driven data masking and AI-enabled synthetic data generation that preserves referential integrity for banking, telecom, and healthcare.
Tonic.ai : A developer favorite for privacy-compliant synthetic data, Tonic specializes in transforming production data into safe, high-fidelity mock datasets. Its suite includes Tonic Fabricate (AI-generated from scratch), Tonic Structural (database subsetting/masking), and Tonic Textual (unstructured text redaction and synthesis).
MOSTLY AI : A prominent European leader focusing heavily on high-fidelity, privacy-safe synthetic data generation backed by differential privacy guarantees. It is widely used across heavily regulated sectors like banking, insurance, and customer analytics.
Developer & Open-Source Ecosystems
NVIDIA NeMo Data Designer (formerly Gretel) : Following its integration into the NVIDIA ecosystem, this developer-first framework (open-sourced under Apache 2.0) leverages statistical samplers and LLMs to construct dependency-aware synthetic data pipelines programmatically.
Computer Vision & Human Simulation
Datagen : A market leader in human-centric computer vision training data. Datagen generates granular, pixel-perfect 3D simulated visual data (facial analysis, body poses, and AR/VR environments) entirely free of real-world PII.
Synthesis AI : Combines generative neural networks with cinematic CGI pipelines to produce photorealistic, on-demand 3D labeled data for autonomous systems, robotics, and smart devices without human-labeling bottlenecks or privacy exposure.
If you are looking to narrow down a provider, tell me:
What type of data are you working with (tabular/structured, unstructured text, or images/video)?
What compliance standards do you need to meet (e.g., HIPAA, GDPR)?
I can help recommend the best-fit platform for your workflow.
If you're evaluating vendors for privacy-preserving synthetic data to train or fine-tune AI models, the leading options I’d shortlist are:
gretel.ai — One of the strongest AI-focused platforms. It generates synthetic tabular, text, and other data, with tools for training, fine-tuning, evaluation, and privacy/quality assessment. It can also run inside a customer's environment so sensitive source data doesn't have to leave it.
mostly.ai — Particularly strong for structured/tabular enterprise data. Its models learn distributions and relationships rather than copying individual records, and its platform includes privacy protections such as differential privacy. It supports local execution and on-prem/private environments.
tonic.ai — A strong choice when you need to turn sensitive production data—including free text—into training data. Tonic Textual detects and synthesizes sensitive entities, while Fabricate can generate new structured and unstructured training data from scratch.
genrocket.com — Particularly established for enterprise synthetic/test data. Its approach can generate data entirely from specifications rather than production records, which is attractive when you want to avoid exposing personal data at the source.
Synthetic Data Vault (SDV) — An important open-source option rather than a conventional enterprise vendor. It's worth considering if your data-science team wants control over the generation pipeline and infrastructure.
Privacy-by-design, differential privacy, local deployment
One important caveat: "synthetic" doesn't automatically mean private. A poorly configured generator can memorize or reproduce information from its training data. For a serious privacy-sensitive deployment, I'd specifically evaluate membership-inference/re-identification testing, differential privacy where appropriate, memorization controls, deployment location, audit logs, and whether the vendor can access your source data.
If you're choosing a provider for a specific environment—e.g. healthcare/PHI, financial data, customer conversations/LLM fine-tuning, or autonomous-agent training—the ranking changes considerably.
The landscape of privacy-preserving synthetic data generation for AI training features several prominent providers, categorized by their core focus areas (tabular/enterprise data, computer vision/simulation, and developer privacy workflows).
Tabular, Financial, and Enterprise Data
MOSTLY AI: Widely recognized as a market leader in privacy-safe, generative AI-driven tabular data synthesis. It excels at maintaining complex relational structures, time-series data, and statistical distributions while offering deep mathematical privacy guarantees (like differential privacy) to ensure zero risk of re-identification.
Hazy: A UK-based pioneer specializing in synthetic data for enterprise machine learning and analytics. Hazy focuses heavily on automated privacy metrics, allowing highly regulated banking, insurance, and telecommunications firms to train models safely.
Gretel.ai: Known for developer-first APIs and privacy primitives that inject differential privacy guarantees directly into synthetic data streams. Gretel makes it seamless to spin up safe datasets for large language model (LLM) fine-tuning and tabular predictive modeling.
K2view: Combines entity-centric data management with AI- and rules-based synthetic data generation. It is heavily adopted in high-throughput enterprise environments (telecom and finance) needing real-time data masking and generation that respects cross-system data hierarchies.
Developer-Focused Masking and Testing Workflows
Tonic.ai: A premier developer-focused platform that creates mimicked and synthetic database environments. While initially famous for safe database anonymization for staging and testing, Tonic expanded its capabilities to build robust, from-scratch relational data configurations ideal for secure AI engineering pipelines.
Computer Vision, Robotics, and Spatial Simulation
Datagen: A dominant force in human-centric computer vision. Datagen creates photorealistic, statistically accurate 3D simulated visual data, allowing companies to train facial recognition, AR/VR, and human-monitoring AI models with zero real-world human privacy compromise.
Synthesis AI: Pioneers an on-demand synthetic data platform leveraging cinematic CGI and generative AI pipelines to produce perfectly labeled 3D facial, body, and object data for computer vision and automotive applications.
NVIDIA (Omniverse / Isaac Sim): Essential for autonomous systems, robotics, and edge-case perception modeling. NVIDIA’s simulation stack allows developers to generate infinite, physically accurate synthetic environments (like rare driving hazards) without using real-world sensitive sensor logs.
If you'd like to narrow this down, let me know:
What type of data you are working with (tabular/financial, text/LLMs, or computer vision/images)
Your primary regulatory framework (GDPR, HIPAA, CCPA)
Whether you need an API-first tool or a full enterprise platform
I can help recommend the best-fit provider for your project.
Focus: Real-time data fabric and micro-database approach to data masking and synthetic data operations.
Privacy Approach: Executes dynamic query-level anonymization and entity-level tracking to serve privacy-compliant data instantly without sluggish bulk data processing lags.
⭐⭐⭐⭐⭐
Tonic.ai
Sensitive enterprise + unstructured data
De-identification + synthesis
⭐⭐⭐⭐⭐
GenRocket
Controlled enterprise/test datasets
Generate without using real PII
⭐⭐⭐
SDV
Data-science teams wanting open source
Configurable synthetic-generation approaches
⭐⭐⭐⭐
Follow how AI answers questions like this in Synthetic Data Generation Platforms.
MOSTLY AI / 
Syntho
