Alternatives to Gretel
Compare Gretel alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Gretel in 2026. Compare features, ratings, user reviews, pricing, and more from Gretel competitors and alternatives in order to make an informed decision for your business.
-
1
Windocks
Windocks
Windocks is a leader in cloud native database DevOps, recognized by Gartner as a Cool Vendor, and as an innovator by Bloor research in Test Data Management. Novartis, DriveTime, American Family Insurance, and other enterprises rely on Windocks for on-demand database environments for development, testing, and DevOps. Windocks software is easily downloaded for evaluation on standard Linux and Windows servers, for use on-premises or cloud, and for data delivery of SQL Server, Oracle, PostgreSQL, and MySQL to Docker containers or conventional database instances. Windocks database orchestration allows for code-free end to end automated delivery. This includes masking, synthetic data, Git operations and access controls, as well as secrets management. Windocks can be installed on standard Linux or Windows servers in minutes. It can also run on any public cloud infrastructure or on-premise infrastructure. One VM can host up 50 concurrent database environments. -
2
DATPROF
DATPROF
Test Data Management solutions like data masking, synthetic data generation, data subsetting, data discovery, database virtualization, data automation are our core business. We see and understand the struggles of software development teams with test data. Personally Identifiable Information? Too large environments? Long waiting times for a test data refresh? We envision to solve these issues: - Obfuscating, generating or masking databases and flat files; - Extracting or filtering specific data content with data subsetting; - Discovering, profiling and analysing solutions for understanding your test data, - Automating, integrating and orchestrating test data provisioning into your CI/CD pipelines and - Cloning, snapshotting and timetraveling throug your test data with database virtualization. We improve and innovate our test data software with the latest technologies every single day to support medium to large size organizations in their Test Data Management. -
3
Amazon SageMaker
Amazon
Amazon SageMaker is an advanced machine learning service that provides an integrated environment for building, training, and deploying machine learning (ML) models. It combines tools for model development, data processing, and AI capabilities in a unified studio, enabling users to collaborate and work faster. SageMaker supports various data sources, such as Amazon S3 data lakes and Amazon Redshift data warehouses, while ensuring enterprise security and governance through its built-in features. The service also offers tools for generative AI applications, making it easier for users to customize and scale AI use cases. SageMaker’s architecture simplifies the AI lifecycle, from data discovery to model deployment, providing a seamless experience for developers. -
4
AI Verse
AI Verse
When real-life data capture is challenging, we generate diverse, fully labeled image datasets. Our procedural technology ensures the highest quality, unbiased, labeled synthetic datasets that will improve your computer vision model’s accuracy. AI Verse empowers users with full control over scene parameters, ensuring you can fine-tune the environments for unlimited image generation, giving you an edge in the competitive landscape of computer vision development. -
5
Synthesis AI
Synthesis AI
A synthetic data platform for ML engineers to enable the development of more capable AI models. Simple APIs provide on-demand generation of perfectly-labeled, diverse, and photoreal images. Highly-scalable cloud-based generation platform delivers millions of perfectly labeled images. On-demand data enables new data-centric approaches to develop more performant models. An expanded set of pixel-perfect labels including segmentation maps, dense 2D/3D landmarks, depth maps, surface normals, and much more. Rapidly design, test, and refine your products before building hardware. Prototype different imaging modalities, camera placements, and lens types to optimize your system. Reduce bias in your models associated with misbalanced data sets while preserving privacy. Ensure equal representation across identities, facial attributes, pose, camera, lighting, and much more. We have worked with world-class customers across many use cases. -
6
MOSTLY AI
MOSTLY AI
As physical customer interactions shift into digital, we can no longer rely on real-life conversations. Customers express their intents, share their needs through data. Understanding customers and testing our assumptions about them also happens through data. And privacy regulations such as GDPR and CCPA make a deep understanding even harder. The MOSTLY AI synthetic data platform bridges this ever-growing gap in customer understanding. A reliable, high-quality synthetic data generator can serve businesses in various use cases. Providing privacy-safe data alternatives is just the beginning of the story. In terms of versatility, MOSTLY AI's synthetic data platform goes further than any other synthetic data generator. MOSTLY AI's versatility and use case flexibility make it a must-have AI tool and a game-changing solution for software development and testing. From AI training to explainability, bias mitigation and governance to realistic test data with subsetting, referential integrity. -
7
Syntho
Syntho
Syntho typically deploys in the safe environment of our customers so that (sensitive) data never leaves the safe and trusted environment of the customer. Connect to the source data and target environment with our out-of-the-box connectors. Syntho can connect with every leading database & filesystem and supports 20+ database connectors and 5+ filesystem connectors. Define the type of synthetization you would like to run, realistically mask or synthesize new values, automatically detect sensitive data types. Utilize and share the protected data securely, ensuring compliance and privacy are maintained throughout its usage. -
8
Sixpack
PumpITup
Sixpack is a data management platform designed to streamline synthetic data for testing purposes. Unlike traditional test data generation, Sixpack provides an endless supply of synthetic data, helping testers and automated tests avoid conflicts and resource bottlenecks. It focuses on flexibility by enabling allocation, pooling, and instant data generation while keeping data quality high and privacy intact. Key features include easy setup, seamless API integration, and the ability to support complex test environments. Sixpack integrates directly with QA processes, so teams save time on managing data dependencies, minimize data overlap, and prevent test interference. Its dashboard offers a clear view of active data sets, and testers can allocate or pool data according to project needs.Starting Price: $0 -
9
CloudTDMS
Cloud Innovation Partners
CloudTDMS solution is a No-Code platform having all necessary functionalities required for Realistic Data Generation. CloudTDMS, your one stop for Test Data Management. Discover & Profile your Data, Define & Generate Test Data for all your team members : Architects, Developers, Testers, DevOPs, BAs, Data engineers, and more ... CloudTDMS automates the process of creating test data for non-production purposes such as development, testing, training, upgrading or profiling. While at the same time ensuring compliance to regulatory and organisational policies & standards. CloudTDMS involves manufacturing and provisioning data for multiple testing environments by Synthetic Test Data Generation as well as Data Discovery & Profiling. Benefit from CloudTDMS No-Code platform to define your data models and generate your synthetic data quickly in order to get faster return on your “Test Data Management” investments. CloudTDMS solves the following challenges : -Regulatory ComplianceStarting Price: Starter Plan : Always free -
10
GenRocket
GenRocket
Enterprise synthetic test data solutions. In order to generate test data that accurately reflects the structure of your application or database, it must be easy to model and maintain each test data project as changes to the data model occur throughout the lifecycle of the application. Maintain referential integrity of parent/child/sibling relationships across the data domains within an application database or across multiple databases used by multiple applications. Ensure the consistency and integrity of synthetic data attributes across applications, data sources and targets. For example, a customer name must always match the same customer ID across multiple transactions simulated by real-time synthetic data generation. Customers want to quickly and accurately create their data model as a test data project. GenRocket offers 10 methods for data model setup. XTS, DDL, Scratchpad, Presets, XSD, CSV, YAML, JSON, Spark Schema, Salesforce. -
11
Tonic
Tonic
Tonic automatically creates mock data that preserves key characteristics of secure datasets so that developers, data scientists, and salespeople can work conveniently without breaching privacy. Tonic mimics your production data to create de-identified, realistic, and safe data for your test environments. With Tonic, your data is modeled from your production data to help you tell an identical story in your testing environments. Safe, useful data created to mimic your real-world data, at scale. Generate data that looks, acts, and feels just like your production data and safely share it across teams, businesses, and international borders. PII/PHI identification, obfuscation, and transformation. Proactively protect your sensitive data with automatic scanning, alerts, de-identification, and mathematical guarantees of data privacy. Advanced sub setting across diverse database types. Collaboration, compliance, and data workflows — perfectly automated. -
12
Statice
Statice
We offer data anonymization software that generates entirely anonymous synthetic datasets for our customers. The synthetic data generated by Statice contains statistical properties similar to real data but irreversibly breaks any relationships with actual individuals, making it a valuable and safe to use asset. It can be used for behavior, predictive, or transactional analysis, allowing companies to leverage data safely while complying with data regulations. Statice’s solution is built for enterprise environments with flexibility and security in mind. It integrates features to guarantee the utility and privacy of the data while maintaining usability and scalability. It supports common data types: Generate synthetic data from structured data such as transactions, customer data, churn data, digital user data, geodata, market data, etc We help your technical and compliance teams validate the robustness of our anonymization method and the privacy of your synthetic dataStarting Price: Licence starting at 3,990€ / m -
13
Mimic
Facteus
Advanced technology and services to safely transform and enhance sensitive data into actionable insights, help drive innovation, and open new revenue streams. Using the Mimic synthetic data engine, companies can safely synthesize their data assets, protecting consumer privacy information from being exposed, while still maintaining the statistical relevancy of the data. The synthetic data can then be used for internal initiatives like analytics, machine learning and AI, marketing and segmentation activities, and new revenue streams through external data monetization. Mimic enables you to safely move statistically-relevant synthetic data to the cloud ecosystem of your choice to get the most out of your data. Analytics, insights, product development, testing, and third-party data sharing can all be done in the cloud with the enhanced synthetic data, which has been certified to be compliant with regulatory and privacy laws. -
14
Protecto
Protecto
While enterprise data is exploding and scattered across various systems, oversight of driving privacy, data security, and governance has become very challenging. As a result, businesses hold significant risks in the form of data breaches, privacy lawsuits, and penalties. Finding data privacy risks in an enterprise is a complex, and time-consuming effort that takes months involving a team of data engineers. Data breaches and privacy laws are requiring companies to have a better grip on which users have access to the data, and how the data is used. But enterprise data is complex, so even if a team of engineers works for months, they will have a tough time isolating data privacy risks or quickly finding ways to reduce them.Starting Price: Usage based -
15
LinkedAI
LinkedAi
We label your data with the higher quality standards to fulfill the needs of the most complex AI projects, using our proprietary labeling platform. Now you can get back to creating the products your customers love. We provide an end-to-end solution for image annotation with fast labeling tools, synthetic data generation, data management, automation features and annotation services on-demand with integrated tooling to accelerate and finish computer vision projects. When every pixel matters, you need accurate, AI-powered intuitive image annotation tools to support your specific use case, including instances, attributes and much more. Our in-house highly trained data labelers are able to deal with any data challenge. As your data labeling needs grow over time, you can count on us to scale the workforce necessary to meet your goals, and in contrast to crowdsourcing platforms your data quality will not suffer. -
16
syntheticAIdata
syntheticAIdata
syntheticAIdata is your partner in creating synthetic data that enables you to craft diverse datasets effortlessly and at scale. Utilizing our solution doesn’t just mean significant cost reductions; it means ensuring privacy, regulatory compliance, and expediting your AI products' journey to the market. Let syntheticAIdata be the catalyst that transforms your AI aspirations into achievements. Synthetic data is generated on a large scale and can cover many scenarios when real data is insufficient. A variety of annotations can be automatically generated. This greatly shortens the time for data collection and tagging. Minimize costs for data collection and tagging by generating synthetic data on a large scale. Our user-friendly and no-code solution empowers even those without technical expertise to easily generate synthetic data. With seamless one-click integration with leading cloud platforms, our solution is the most convenient to use on the market. -
17
K2View
K2View
At K2View, we believe that every enterprise should be able to leverage its data to become as disruptive and agile as the best companies in its industry. We make this possible through our patented Data Product Platform, which creates and manages a complete and compliant dataset for every business entity – on demand, and in real time. The dataset is always in sync with its underlying sources, adapts to changes in the source structures, and is instantly accessible to any authorized data consumer. Data Product Platform fuels many operational use cases, including customer 360, data masking and tokenization, test data management, data migration, legacy application modernization, data pipelining and more – to deliver business outcomes in less than half the time, and at half the cost, of any other alternative. The platform inherently supports modern data architectures – data mesh, data fabric, and data hub – and deploys in cloud, on-premise, or hybrid environments. -
18
Syntheticus
Syntheticus
Syntheticus® empowers data exchange and overcomes limitations in data access, scarcity, and bias - at scale. With our synthetic data platform, you generate high-quality and compliant data samples tailored to your business needs and analytics goals. With synthetic data, you easily tap into a wide range of high-quality sources that are not always available in the real world. By accessing high-quality, consistent data, you conduct more reliable research, leading to better products, services, and business decisions. With fast, reliable data sources at your fingertips, you accelerate product development cycles and improve time-to-market. Synthetic data is designed to be private and secure by default, protecting sensitive data and maintaining compliance with privacy laws and regulations. -
19
Subsalt
Subsalt Inc.
Subsalt is the first platform built to enable the use of anonymous data at enterprise scale. Subsalt's Query Engine dynamically optimizes the tradeoffs between data privacy and fidelity to the source data. Queries return fully-synthetic data that preserves row-level granularity and data formats without disruptive data transformations. Subsalt provides compliance guarantees supported by third-party audits that satisfy HIPAA's Expert Determination standard. Subsalt supports multiple deployment models to meet the unique privacy and security requirements of each client. Subsalt is SOC2-Type 2 and HIPAA compliant. The system has been designed to minimize the risk of exposure or breach of real data. Existing data and ML tools integrate directly with Subsalt's Postgres-compatible SQL interface, making adoption a breeze. -
20
Amazon SageMaker Ground Truth
Amazon Web Services
Amazon SageMaker allows you to identify raw data such as images, text files, and videos; add informative labels and generate labeled synthetic data to create high-quality training data sets for your machine learning (ML) models. SageMaker offers two options, Amazon SageMaker Ground Truth Plus and Amazon SageMaker Ground Truth, which give you the flexibility to use an expert workforce to create and manage data labeling workflows on your behalf or manage your own data labeling workflows. data labeling. If you want the flexibility to create and manage your own personal and data labeling workflows, you can use SageMaker Ground Truth. SageMaker Ground Truth is a data labeling service that makes data labeling easy and gives you the option of using human annotators via Amazon Mechanical Turk, third-party providers, or your own private staff.Starting Price: $0.08 per month -
21
Synthesized
Synthesized
Power up your AI and data projects with the most valuable data At Synthesized, we unlock data's full potential by automating all stages of data provisioning and data preparation with a cutting-edge AI. We protect from privacy and compliance hurdles by virtue of the data being synthesized through the platform. Software for preparing and provisioning of accurate synthetic data to build better models at scale. Businesses solve the problem of data sharing with Synthesized. 40% of companies investing in AI cannot report business gains. Stay ahead of your competitors and help data scientists, product and marketing teams focus on uncovering critical insight with our simple-to-use platform for data preparation, sanitization and quality assessment. Testing data-driven applications is difficult without representative datasets and this leads to issues when services go live. -
22
Bifrost
Bifrost AI
Quickly and easily generate diverse and realistic synthetic data and high-fidelity 3D worlds to enhance model performance. Bifrost's platform is the fastest way to generate the high-quality synthetic images that you need to improve ML performance and overcome real-world data limitations. Prototype and test up to 30x faster by circumventing costly and time-consuming real-world data collection and annotation. Generate data to account for rare scenarios underrepresented in real data, resulting in more balanced datasets. Manual annotation and labeling is an error-prone, resource-intensive process. Easily and quickly generate data that is pre-labeled and pixel-perfect. Real-world data can inherit the biases of conditions under which the data was collected, and generate data to solve for these instances. -
23
Aindo
Aindo
Accelerate time-consuming data processing steps, including structuring, labeling, and preprocessing. Manage your data in one central, easy-to-integrate platform. Increase data accessibility rapidly through privacy-protecting synthetic data and user-friendly exchange platforms. The Aindo synthetic data platform allows you to securely exchange data across departments, with external service providers, partners, and the artificial intelligence community. Explore new synergies through synthetic data exchange and collaboration. Acquire missing data openly and securely. Provide comfort and trust to your clients and stakeholders. The Aindo synthetic data platform removes data inaccuracies and implicit bias for fair and complete insights. Augment information to make databases robust to special events. Balance datasets that misrepresent true populations for a fair and accurate overall depiction. Fill in data gaps in a sound and exact manner. -
24
Datanamic Data Generator
Datanamic
Datanamic Data Generator is a powerful data generator that allows developers to easily populate databases with thousands of rows of meaningful and syntactically correct test data for database testing purposes. An empty database is not useful for making sure your application will work as designed. You need test data. Writing your own test data generators or scripts is time consuming. Datanamic Data Generator will help you. The tool can be used by DBAs, developers, or testers, who need sample data to test a database-driven application. Datanamic Data Generator makes database test data generation easy and painless. It reads your database and displays tables and columns with their data generation settings. Only a few simple entries are necessary to generate comprehensive (realistic) test data. The tool can be used to generate test data from scratch or from existing data.Starting Price: €59 per month -
25
DataCebo Synthetic Data Vault (SDV)
DataCebo
The Synthetic Data Vault (SDV) is a Python library designed to be your one-stop shop for creating tabular synthetic data. The SDV uses a variety of machine learning algorithms to learn patterns from your real data and emulate them in synthetic data. The SDV offers multiple models, ranging from classical statistical methods (GaussianCopula) to deep learning methods (CTGAN). Generate data for single tables, multiple connected tables, or sequential tables. Compare the synthetic data to the real data against a variety of measures. Diagnose problems and generate a quality report to get more insights. Control data processing to improve the quality of synthetic data, choose from different types of anonymization, and define business rules in the form of logical constraints. Use synthetic data in place of real data for added protection, or use it in addition to your real data as an enhancement. The SDV is an overall ecosystem for synthetic data models, benchmarks, and metrics.Starting Price: Free -
26
DataGen
DataGen
DataGen is a leading AI platform specializing in synthetic data generation and custom generative AI models for machine learning projects. Their flagship product, SynthEngyne, supports multi-format data generation including text, images, tabular, and time-series data, ensuring privacy-compliant, high-quality training datasets. The platform offers scalable, real-time processing and advanced quality controls like deduplication to maintain dataset fidelity. DataGen also provides professional AI development services such as model deployment, fine-tuning, synthetic data consulting, and intelligent automation systems. With flexible pricing plans ranging from free tiers for individuals to custom enterprise solutions, DataGen caters to a wide range of users. Their solutions serve diverse industries including healthcare, finance, automotive, and retail. -
27
Private AI
Private AI
Safely share your production data with ML, data science, and analytics teams while safeguarding customer trust. Stop fiddling with regexes and open-source models. Private AI efficiently anonymizes 50+ entities of PII, PCI, and PHI across GDPR, CPRA, and HIPAA in 49 languages with unrivaled accuracy. Replace PII, PCI, and PHI in text with synthetic data to create model training datasets that look exactly like your production data without compromising customer privacy. Remove PII from 10+ file formats, such as PDF, DOCX, PNG, and audio to protect your customer data and comply with privacy regulations. Private AI uses the latest in transformer architectures to achieve remarkable accuracy out of the box, no third-party processing is required. Our technology has outperformed every other redaction service on the market. Feel free to ask us for a copy of our evaluation toolkit to test on your own data. -
28
YData
YData
Adopting data-centric AI has never been easier with automated data quality profiling and synthetic data generation. We help data scientists to unlock data's full potential. YData Fabric empowers users to easily understand and manage data assets, synthetic data for fast data access, and pipelines for iterative and scalable flows. Better data, and more reliable models delivered at scale. Automate data profiling for simple and fast exploratory data analysis. Upload and connect to your datasets through an easily configurable interface. Generate synthetic data that mimics the statistical properties and behavior of the real data. Protect your sensitive data, augment your datasets, and improve the efficiency of your models by replacing real data or enriching it with synthetic data. Refine and improve processes with pipelines, consume the data, clean it, transform your data, and work its quality to boost machine learning models' performance. -
29
Rockfish Data
Rockfish Data
Rockfish Data is the industry's first outcome-centric synthetic data generation platform, unlocking the true value of operational data. Rockfish helps enterprises take advantage of siloed data to train ML/AI workflows, produce compelling datasets for product demos, and more. The platform intelligently adapts to and optimizes diverse datasets, seamlessly adjusting to various data types, sources, and structures for maximum efficiency. It focuses on delivering specific, measurable results that drive tangible business value, with a purpose-built architecture emphasizing robust security measures to ensure data integrity and privacy. By operationalizing synthetic data, Rockfish enables organizations to overcome data silos, enhance machine learning and artificial intelligence workflows, and generate high-quality datasets for various applications. -
30
SKY ENGINE AI
SKY ENGINE AI
SKY ENGINE AI is a fully managed 3D Generative AI platform that transforms how enterprises build Vision AI by producing high-quality synthetic data at scale. It replaces difficult, expensive real-world data collection with physics-accurate simulation, multispectrum rendering, and automated ground-truth generation. The platform integrates a synthetic data engine, domain adaptation tools, sensor simulators, and deep learning pipelines into a single environment. Teams can test hypotheses, capture rare edge cases, and iterate datasets rapidly using advanced randomization, GAN post-processing, and 3D generative blueprints. With GPU-integrated development tools, distributed rendering, and full cloud resource management, SKY ENGINE AI eliminates workflow complexity and accelerates AI development. The result is faster model training, significantly lower costs, and highly reliable Vision AI across industries. -
31
Rendered.ai
Rendered.ai
Overcome challenges in acquiring data for machine learning and AI systems training. Rendered.ai is a PaaS designed for data scientists, engineers, and developers. Generate synthetic datasets for ML/AI training and validation. Experiment with sensor models, scene content, and post-processing effects. Characterize and catalog real and synthetic datasets. Download or move data to your own cloud repositories for processing and training. Power innovation and increase productivity with synthetic data as a capability. Build custom pipelines to model diverse sensors and computer vision inputs. Start quickly with free, customizable Python sample code to model SAR, RGB satellite imagery, and more sensor types. Experiment and iterate with flexible licensing that enables nearly unlimited content generation. Create labeled content rapidly in a hosted, high-performance computing environment. Enable collaboration between data scientists and data engineers with a no-code configuration experience. -
32
Supervisely
Supervisely
The leading platform for entire computer vision lifecycle. Iterate from image annotation to accurate neural networks 10x faster. With our best-in-class data labeling tools transform your images / videos / 3d point cloud into high-quality training data. Train your models, track experiments, visualize and continuously improve model predictions, build custom solution within the single environment. Our self-hosted solution guaranties data privacy, powerful customization capabilities, and easy integration into your technology stack. A turnkey solution for Computer Vision: multi-format data annotation & management, quality control at scale and neural networks training in end-to-end platform. Inspired by professional video editing software, created by data scientists for data scientists — the most powerful video labeling tool for machine learning and more. -
33
Anyverse
Anyverse
A flexible and accurate synthetic data generation platform. Craft the data you need for your perception system in minutes. Design scenarios for your use case with endless variations. Generate your datasets in the cloud. Anyverse offers a scalable synthetic data software platform to design, train, validate, or fine-tune your perception system. It provides unparalleled computing power in the cloud to generate all the data you need in a fraction of the time and cost compared with other real-world data workflows. Anyverse provides a modular platform that enables efficient scene definition and dataset production. Anyverse™ Studio is a standalone graphical interface application that manages all Anyverse functions, including scenario definition, variability settings, asset behaviors, dataset settings, and inspection. Data is stored in the cloud, and the Anyverse cloud engine is responsible for final scene generation, simulation, and rendering. -
34
Delphix
Perforce
Delphix is the industry leader in DataOps and provides an intelligent data platform that accelerates digital transformation for leading companies around the world. The Delphix DataOps Platform supports a broad spectrum of systems, from mainframes to Oracle databases, ERP applications, and Kubernetes containers. Delphix supports a comprehensive range of data operations to enable modern CI/CD workflows and automates data compliance for privacy regulations, including GDPR, CCPA, and the New York Privacy Act. In addition, Delphix helps companies sync data from private to public clouds, accelerating cloud migrations, customer experience transformation, and the adoption of disruptive AI technologies. Automate data for fast, quality software releases, cloud adoption, and legacy modernization. Source data from mainframe to cloud-native apps across SaaS, private, and public clouds. -
35
eperi Gateway
Eperi
You alone have full control over the encryption of your data – at any time and definitely without backdoors. All Systems – One Solution. Thanks to the patented template concept you can use the eperi® Gateway for all your (cloud-) applications and adapt it to your data protection requirements. Your employees can continue working as usual, while the eperi® Gateway encrypts data transparently and preserves the central functions of your applications (such as search and sort). Take advantage of cloud applications while staying compliant with the stringent regulations of the financial industry like privacy-preserving analytics. IoT brings forth smart machines and products that collect data independently and automatically. Thanks to encryption, you can ensure data privacy and comply with legislation. -
36
Shaip
Shaip
Shaip offers end-to-end generative AI services, specializing in high-quality data collection and annotation across multiple data types including text, audio, images, and video. The platform sources and curates diverse datasets from over 60 countries, supporting AI and machine learning projects globally. Shaip provides precise data labeling services with domain experts ensuring accuracy in tasks like image segmentation and object detection. It also focuses on healthcare data, delivering vast repositories of physician audio, electronic health records, and medical images for AI training. With multilingual audio datasets covering 60+ languages and dialects, Shaip enhances conversational AI development. The company ensures data privacy through de-identification services, protecting sensitive information while maintaining data utility. -
37
AutonomIQ
AutonomIQ
Our AI-driven, autonomous low-code automation platform is designed to help you achieve the highest quality outcome in the shortest amount of time possible. Generate automation scripts automatically in plain English with our Natural Language Processing (NLP) powered solution, and allow your coders to focus on innovation. Maintain quality throughout your application lifecycle with our autonomous discovery and up-to-date tracking of changes. Reduce risk in your dynamic development environment with our autonomous healing capability and deliver flawless updates by keeping automation current. Ensure compliance with all regulatory requirements and eliminate security risk using AI-generated synthetic data for all your automation needs. Run multiple tests in parallel, determine test frequency, keep pace with browser updates and executions across operating systems and platforms. -
38
OneView
OneView
Working exclusively with real data creates significant challenges for machine learning model training. Synthetic data enables limitless machine learning model training, addressing the drawbacks and challenges of real data. Boost the performance of your geospatial analytics by creating the imagery you need. Customizable satellite, drone, and aerial imagery. Create scenarios, change object ratios, and adjust imaging parameters quickly and iteratively. Any rare objects or occurrences can be created. The resulting datasets are fully-annotated, error-free, and ready for training. The OneView simulation engine creates 3D worlds as the base for synthetic satellite and aerial images, layered with multiple randomization factors, filters, and variation parameters. The synthetic images replace real data for remote sensing systems in machine learning model training. They achieve superior interpretation results, especially in cases with limited coverage or poor-quality data. -
39
Benerator
Benerator
Describe your data model on an abstract level in XML. Involve your business people as no developer skills are necessary. Use a wide range of function libraries to fake realistic data. Write your own extensions in Javascript or Java. Integrate your data processes into Gitlab CI or Jenkins. Generate, anonymize, and migrate with Benerator’s model-driven data toolkit. Define processes to anonymize or pseudonymize data in plain XML on an abstract level without the need for developer skills. Stay GDPR compliant with your data and protect the privacy of your customers. Mask and obfuscate sensitive data for BI, test, development, or training purposes. Combine data from various sources (subsetting) and keep the data integrity. Migrate and transform your data in multisystem landscapes. Reuse your testing data models to migrate production environments. Keep your data consistent and reliable in a microsystem architecture. -
40
TELUS Digital Ground Truth Studio
TELUS Digital
TELUS Digital is the customer experience transformation partner to the world’s most admired brands. Our diverse team weaves data, technology and human ingenuity to deliver differentiated customer journeys, drive operational effectiveness and scale AI solutions with meaningful value and positive impact. We craft real-world solutions in the moments that matter, from customer acquisition to lifelong loyalty. Enabled by our global reach of over 83,000 experts in more than 35 countries and deep industry expertise, we help over 600 organizations make the customer experience feel effortless. At the core of our innovation is Fuel iX™, an enterprise-grade generative AI platform that helps clients safely access and optimize leading LLMs to scale their own AI from pilot to production. -
41
Azure Machine Learning
Microsoft
Accelerate the end-to-end machine learning lifecycle with Azure Machine Learning Studio. Empower developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible ML. Productivity for all skill levels, with code-first and drag-and-drop designer, and automated machine learning. Robust MLOps capabilities that integrate with existing DevOps processes and help manage the complete ML lifecycle. Responsible ML capabilities – understand models with interpretability and fairness, protect data with differential privacy and confidential computing, and control the ML lifecycle with audit trials and datasheets. Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R. -
42
Prodigy
Explosion
Radically efficient machine teaching. An annotation tool powered by active learning. Prodigy is a scriptable annotation tool so efficient that data scientists can do the annotation themselves, enabling a new level of rapid iteration. Today’s transfer learning technologies mean you can train production-quality models with very few examples. With Prodigy you can take full advantage of modern machine learning by adopting a more agile approach to data collection. You'll move faster, be more independent and ship far more successful projects. Prodigy brings together state-of-the-art insights from machine learning and user experience. With its continuous active learning system, you're only asked to annotate examples the model does not already know the answer to. The web application is powerful, extensible and follows modern UX principles. The secret is very simple: it's designed to help you focus on one decision at a time and keep you clicking – like Tinder for data.Starting Price: $490 one-time fee -
43
Symage
Symage
Symage is a synthetic data platform that generates custom, photorealistic image datasets with automated pixel-perfect labeling to support training and improving AI and computer vision models; using physics-based rendering and simulation rather than generative AI, it produces high-fidelity synthetic images that mirror real-world conditions and handle diverse scenarios, lighting, camera angles, object motion, and edge cases with controlled precision, which helps eliminate data bias, reduce manual labeling, and dramatically cut data preparation time by up to 90%. Designed to give teams the right data for model training rather than relying on limited real datasets, Symage lets users tailor environments and variables to match specific use cases, ensuring datasets are balanced, scalable, and accurately labeled at every pixel. It is built on decades of expertise in robotics, AI, machine learning, and simulation, offering a way to overcome data scarcity and boost model accuracy. -
44
Mistral Forge
Mistral AI
Mistral AI’s Forge platform enables enterprises to build customized AI models tailored to their internal data, workflows, and domain expertise. It provides end-to-end model development capabilities, covering everything from pre-training and synthetic data generation to reinforcement learning and evaluation. Organizations can integrate proprietary datasets and decision frameworks to create models that align closely with their business needs. Forge supports flexible deployment options, allowing companies to run models on-premises, in private cloud environments, or through Mistral infrastructure. The platform emphasizes security and governance, ensuring strict data isolation and compliance with enterprise policies. It also includes advanced evaluation tools that measure performance based on business-specific KPIs rather than generic benchmarks. By managing the full AI lifecycle in one system, Forge helps companies transform institutional knowledge into high-performing AI. -
45
CloudFactory
CloudFactory
Human-powered Data Processing for AI and Automation. Our managed teams have served hundreds of clients across use cases that range from simple to complex. Our proven processes deliver quality data quickly and are designed to scale and change along with your needs. Our flexible platform integrates with any commercial or proprietary tool set so you can use the right tool for the job. Flexible contract terms and pricing help you to get started quickly and to scale up and down as needed with no lock-in. For nearly a decade, clients have trusted our secure IT-Infrastructure and workforce vetting processes to deliver quality work remotely. We maintained operations during COVID-19 lockdowns, keeping our clients up-and-running and adding geographic and vendor diversity to their workforces. -
46
Snorkel AI
Snorkel AI
AI today is blocked by lack of labeled data, not models. Unblock AI with the first data-centric AI development platform powered by a programmatic approach. Snorkel AI is leading the shift from model-centric to data-centric AI development with its unique programmatic approach. Save time and costs by replacing manual labeling with rapid, programmatic labeling. Adapt to changing data or business goals by quickly changing code, not manually re-labeling entire datasets. Develop and deploy high-quality AI models via rapid, guided iteration on the part that matters–the training data. Version and audit data like code, leading to more responsive and ethical deployments. Incorporate subject matter experts' knowledge by collaborating around a common interface, the data needed to train models. Reduce risk and meet compliance by labeling programmatically and keeping data in-house, not shipping to external annotators. -
47
Data is an invaluable business asset. With the right AI model, it’s possible to use data to build and understand customer profiles, look for trends, and identify new business opportunities. But it requires huge volumes of data to develop accurate and robust AI models, and that’s a challenge, from both a data quality and quantity perspective. In addition, stringent regulations, most notably GDPR, restrict the use of certain sensitive data, like customer data. It’s time for a new approach. Especially in a software testing environment where good quality testing data is hard to access. We typically see actual customer data being used, which risks GDPR non-compliance and ensuing heavy financial fines. Artificial Intelligence (AI) is expected to increase business productivity by at least 40% but businesses struggle to deploy or fully unlock AI solutions due to data-related challenges. ADA generates synthetic data using advanced deep learning.
-
48
Informatica Test Data Management
Informatica
We help you discover, create, and subset test data; visualize test data coverage; and protect data so you can focus on development. Automate provisioning of masked, subsetted, and synthetically generated data to meet development and testing needs. Identify sensitive data locations quickly with consistent masking in and across databases. Store, augment, share, and reuse test datasets to improve testers’ efficiency. Provision smaller data sets to minimize infrastructure requirements and speed performance. Use our comprehensive set of masking techniques to consistently protect data across applications. Support packaged applications to ensure solution integrity and speed deployments. Engage risk, compliance, and audit teams to align with data governance initiatives. Improve test efficiency with reliable, trusted production data sets; reduce server and storage footprints with data set sizes targeted for each team. -
49
MakerSuite
Google
MakerSuite is a tool that simplifies this workflow. With MakerSuite, you’ll be able to iterate on prompts, augment your dataset with synthetic data, and easily tune custom models. When you’re ready to move to code, MakerSuite will let you export your prompt as code in your favorite languages and frameworks, like Python and Node.js. -
50
Scale Data Engine
Scale AI
Scale Data Engine helps ML teams build better datasets. Bring together your data, ground truth, and model predictions to effortlessly fix model failures and data quality issues. Optimize your labeling spend by identifying class imbalance, errors, and edge cases in your data with Scale Data Engine. Significantly improve model performance by uncovering and fixing model failures. Find and label high-value data by curating unlabeled data with active learning and edge case mining. Curate the best datasets by collaborating with ML engineers, labelers, and data ops on the same platform. Easily visualize and explore your data to quickly find edge cases that need labeling. Check how well your models are performing and always ship the best one. Easily view your data, metadata, and aggregate statistics with rich overlays, using our powerful UI. Scale Data Engine supports visualization of images, videos, and lidar scenes, overlaid with all associated labels, predictions, and metadata.
