AI Data / Synthetic Data
Tool | Category | Segment | Platform / Tool | Plan / License | Monthly Price USD | Pricing Model | Free Tier / OSS | Included Usage / Limits | Data Type / Modality | Generation / Labeling Workflow | Quality / Safety Controls | Integrations / Frameworks | Deployment / Hosting | Security / Privacy | Team / Governance | Best Fit | Main Limits / Caveats |
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No tagline | AI Data / Synthetic Data | Dataset collaboration app | Kiln | Open source app/library | $0 software; provider/model costs separate | Free app and OSS library for AI product datasets, evals and optimization | ✓ | Repository describes a free app and open-source library including synthetic data generation, dataset management, evals, RAG, agents and fine-tuning | LLM datasets, eval cases, RAG/agent examples and fine-tuning data | Human-in-the-loop dataset management, synthetic data generation and evaluation workflows | Built-in evals, dataset collaboration, auto-optimization and fine-tuning workflow checks | Desktop/app workflow, Python libraries, LLM providers, RAG/agent/fine-tuning workflows and MCP | Local app/self-hosted style workflow with provider connections | Local project storage possible; prompts and generations go to selected model providers | Collaboration depends project/app setup; no broad SaaS plan captured | Product teams iterating on datasets, evals and fine-tuning prep in one local-first tool | Not a raw distributed data factory; model/provider costs and data governance remain external |
No tagline | AI Data / Synthetic Data | Agentic synthetic data | DeepFabric | Apache-2.0 / open source | $0 software; model/API/compute costs separate | Open-source synthetic training data generator | ✓ | Repository says DeepFabric generates high-quality synthetics, trains, measures and evaluates in a single pipeline | Agent traces, tool-calling data, structured outputs and domain-specific LLM training datasets | Topic graph generation, reasoning traces, tool execution samples and schema-constrained examples | Constrained decoding, response validation, diversity/topic coverage and isolated WebAssembly tool execution | Python, LLM providers, structured schema tools and model training/evaluation pipelines | Local/self-hosted Python workflows or cloud jobs | Data stays in chosen runtime except calls to configured LLM providers | No SaaS governance by default | Agent/tool-calling dataset generation where schema correctness matters | Newer project; generated data still needs downstream eval against real tasks |
No tagline | AI Data / Synthetic Data | Synthetic data framework | Distilabel | Apache-2.0 / open source | $0 software; LLM/API/compute costs separate | Python framework for synthetic data and AI feedback pipelines | ✓ | Repository describes Distilabel as a framework for synthetic data and AI feedback; package installs via pip and supports provider extras | Text, instruction data, preferences, dialogue, classification/extraction and generative LLM datasets | Programmatic pipelines using LLM providers, local models and tasks for generation/judging | Research-paper based recipes, AI feedback, duplicate detection extras, clustering and pipeline fault tolerance | OpenAI, Anthropic, Cohere, Groq, Mistral, Vertex AI, Hugging Face, Ollama, vLLM, LiteLLM, Argilla and Ray | Local Python, notebooks, batch jobs or self-hosted data pipelines | Self-hosted pipeline control; data sent only to configured model providers and storage targets | No SaaS team layer in OSS; governance depends repo, provider keys and data store | Engineers building auditable LLM synthetic data and feedback pipelines | Maintainers note original authors moved on and community maintainers are carrying releases; production teams should pin versions |
No tagline | AI Data / Synthetic Data | Synthetic data and training workflow | DataDreamer | MIT / open source | $0 software; model/API/compute costs separate | Open-source Python library for prompting, synthetic data generation and training workflows | ✓ | Repository describes DataDreamer as a research-grade Python library for prompting, synthetic data generation and training workflows | LLM synthetic datasets, task augmentation data, alignment data and instruction-tuning examples | Multi-step prompting workflows, generation, model training/alignment and caching/resumability | Reproducibility focus, caching, resumability and support for quantization and PEFT-oriented training | Open-source and API LLMs, Hugging Face datasets/models, training stacks and Python workflows | Local Python, notebooks, research pipelines or cloud jobs | Data privacy depends on selected LLM providers and storage targets | No SaaS governance; experiment governance is local/repo-based | Researchers building reproducible synthetic-data-to-training workflows | Research-grade flexibility means more engineering work than no-code tools |
No tagline | AI Data / Synthetic Data | Data quality and feedback UI | Argilla | Open source tool | $0 software; hosting/model costs separate | Open-source collaboration tool for AI engineers and domain experts | ✓ | Official site describes Argilla as a collaboration tool for AI engineers and domain experts to build high-quality datasets | NLP training datasets, preference data, RLHF/eval feedback and annotation records | Human-in-the-loop review, active learning and dataset feedback workflows | Expert review, active learning, transparency, annotation UI and dataset iteration | Distilabel, Hugging Face, Python NLP stack and Argilla docs/tools | Self-hosted/open-source deployment or community demo; Argilla has joined Hugging Face | Self-hosting can keep datasets internal; cloud/demo use follows hosted service terms | Workspace/user governance depends deployment and Hugging Face/Argilla setup | Teams adding expert review and feedback loops to generated LLM datasets | Not primarily a synthetic generator; best paired with Distilabel or labeling pipelines |
No tagline | AI Data / Synthetic Data | Synthetic data platform | YData Fabric | Community SaaS/platform plus paid self-host | $0 Community; pay-as-you-go and Enterprise separate | Community free, pay-as-you-go marketplace, Enterprise custom | Yes, Community starts free | Pricing page lists Community with 20+ connectors, catalog, profiling, labs, synthetic data generation and Fabric SDK; pay-as-you-go adds database synthesis and self-hosted AWS/Azure | Tabular, time series, multi-table/database and text synthetic data per docs | UI and SDK workflows for data profiling, synthetic generator creation and synthetic database generation | Profiling, data catalog, type validation, synthetic quality evaluation and pipeline workflows | YData Fabric, Fabric SDK, AWS/Azure marketplaces, connectors and Kubernetes-native deployment | Community hosted; pay-as-you-go self-hosted on AWS/Azure; Enterprise private cloud/on-prem | Synthetic data for privacy/compliance; deployment options include private cloud/on-prem for Enterprise | Community for individuals; pay-as-you-go allows unlimited concurrent users; Enterprise adds control/support | Teams needing synthetic tabular/database data with UI and self-host options | Exact pay-as-you-go unit pricing is marketplace-specific; Community limits are not fully quantified on page |
No tagline | AI Data / Synthetic Data | Privacy-preserving test data | Tonic Structural | Commercial custom pricing | Custom pricing | Annual/custom plan for structured data de-identification and synthesis | No public free tier captured for Structural | Pricing page lists Professional custom pricing with up to 10TB source data, up to 10 users and 2 data source types; Enterprise adds unlimited scope | Structured/semi-structured databases and production-like test data | De-identify, subset and synthesize structured/semi-structured data while preserving relationships | Privacy scan, privacy reports, generator presets, cross-table consistency, audit trail, sensitivity rules and encryption support | PostgreSQL, MySQL, MariaDB, SQL Server, MongoDB, Snowflake, BigQuery, Redshift, Databricks, Salesforce, Spark SDK and flat files | Tonic Cloud for Professional; Cloud or self-hosted for Enterprise | Designed for de-identification/compliance; enterprise deployment and encryption options available | Professional includes Tonic Auth/Google SSO; Enterprise includes RBAC and SSO/SAML | Engineering and QA teams needing safe production-like databases | Custom pricing and source/data-source limits on Professional; Oracle/Db2 listed as Enterprise-only |
No tagline | AI Data / Synthetic Data | Enterprise test data automation | Synthesized | Commercial / contact sales | Custom pricing | Enterprise platform for test data generation, masking and subsetting | No public free tier captured | Official site describes production-like test data with generation, masking and subsetting for development, QA and agentic AI workflows | Databases, production-like test data, masked subsets and generated data for CI/CD | Create transformation jobs, classify data, mask/subset and generate representative datasets on demand | Codified compliance rules, access-right checks, masking policies and CI/CD job automation | Databases, CI/CD pipelines, data pipelines and enterprise test automation workflows | Enterprise deployment model; request demo/get started path | Designed to reduce regulatory risk with masking policies and compliance rules | Access rights, enterprise controls and demo/sales-led governance | Regulated engineering teams needing production-like test data without exposing sensitive data | Pricing and deployment details require sales discussion |
No tagline | AI Data / Synthetic Data | Computer vision data curation | Voxel51 FiftyOne | Commercial SaaS/enterprise plus OSS ecosystem | Contact sales | Team/Growth/Custom plans; open-source FiftyOne ecosystem exists separately | OSS ecosystem available; commercial plans are contact sales | Pricing page lists Team with 8 user seats, 16 guest seats, 4 VPUs, 2,800 compute hours/month, unlimited data and model inference | Images, video, 3D, DICOM/NIfTI, geospatial, audio and multimodal grouped datasets | Dataset exploration, slicing, natural-language search, similarity search, annotation workflows and model evaluation | Outlier/anomaly detection, data issue detection, automated quality scoring and model comparison analytics | Annotation tools, cloud storage, SDK/notebooks, vector search databases, models, experiment tracking and datasets | Cloud/public/private/hybrid; on-prem or air-gapped available on higher tiers/add-ons | Pricing page lists SSO, RBAC, ISO 27001, audit logs and encryption; PII/PHI support add-ons | Team/Growth/Custom seat and deployment governance | Computer vision teams curating and evaluating large multimodal datasets | Pricing is contact-sales; synthetic generation is not the core function |
No tagline | AI Data / Synthetic Data | AI data development platform | Snorkel Flow | Commercial / talk to expert | Custom pricing | Enterprise AI data development platform | No public free tier captured | Official page says Snorkel Flow curates training data, evaluates models, optimizes RAG pipelines and fine-tunes LLMs | Enterprise text/documents, labels, LLM eval data, RAG metadata and specialized model data | SME knowledge capture, programmatic labeling, model-guided error analysis and synthetic data generation | Guided error analysis, diversity/coverage monitoring, SME feedback and model/eval loops | Snowflake, Parquet, S3, BigQuery, SQL, Databricks, MLflow, SageMaker, Vertex AI, Azure ML and Spark | Snorkel-hosted, AWS, Azure, GCP or Kubernetes/on-prem style infrastructure | Enterprise-grade security and governance; host within infrastructure of choice per official page | Collaborative SME workflows and enterprise controls; pricing is sales-led | Enterprises replacing manual labeling with programmatic data development | Heavyweight enterprise platform; not a self-serve OSS synthetic data generator |
No tagline | AI Data / Synthetic Data | Synthetic data platform | MOSTLY AI | SaaS plus OSS SDK | $0 Free; Marketplace $3,000/month | Free SaaS credit plan, open-source SDK and paid AWS Marketplace deployment | Yes, Free forever with credits; OSS SDK under Apache-2.0 | Pricing page says Free forever with 2 credits per day and max 25 credits/month; Marketplace is $3,000/month | Structured/tabular synthetic data, data sharing and AI/ML development datasets | Train generators and generate synthetic data via platform or Python SDK | Synthetic Data SDK, credits, platform controls and marketplace/private deployment options | SaaS, AWS Marketplace, API, Python SDK and custom deployment paths | SaaS for Free; AWS Marketplace or custom deployment for higher tiers | Open-source SDK can run in user environment; platform privacy depends SaaS/marketplace deployment | Free plan has limited active chat; Marketplace adds team deployment and SSO-style controls | Teams evaluating synthetic tabular data with a no-cost hosted entry point | Free credits are small; serious team usage moves to marketplace/custom deployment |
No tagline | AI Data / Synthetic Data | Agentic synthetic data SaaS | Tonic Fabricate | Hosted SaaS | $0/month Free; Plus $29/month | Monthly plan includes credits; additional usage is metered | Yes, Free tier includes $10 monthly credits | Pricing page lists Free at $0/month with $10 monthly credits; Plus at $29/month with $25 credits and pay-as-you-go; Enterprise custom | Relational databases, free-text, PDFs, DOCX, mock APIs and unstructured datasets | Chat with Data Agent to generate and refine synthetic datasets from prompts | Training opt-out, export controls and usage tracking; enterprise expands export and workspace/RBAC options | Tonic Cloud, LLM APIs, relational data exports and enterprise self-hosting for higher tiers | Tonic Cloud for Free/Plus; Enterprise can use Tonic Cloud or self-hosted | Generated data ownership retained by user per pricing FAQ; prompts/feedback may be used to improve Data Agent | Free/Plus individual workflow; Enterprise adds multiple workspaces, RBAC, SSO and centralized billing | Developers generating realistic synthetic datasets from scratch for prototypes and tests | Credit-based usage can run out quickly on complex multi-turn generation; prompt/feedback use needs review |
No tagline | AI Data / Synthetic Data | Tabular synthetic data library | Synthetic Data Vault (SDV) | Business Source License / community edition | $0 for community software; enterprise licensing separate | Local Python library plus licensed enterprise offering | Yes, community edition | Docs say SDV Community is publicly available and designed for tabular synthetic data; Enterprise adds scalable and advanced features | Single-table, multi-table relational and sequential/tabular data | Train synthesizers on real data, sample synthetic rows and evaluate synthetic quality | Quality reports, visual comparison, constraints, anonymization options and metadata-driven modeling | Python, pandas, DataCebo ecosystem, notebooks and enterprise connectors in licensed edition | Local/on-prem Python for community; enterprise deployment for licensed users | Community runs locally/on-prem with standard CPUs; privacy depends input data handling and license terms | No SaaS team layer in community; enterprise adds organizational capabilities | Privacy-preserving tabular synthetic data POCs and local experimentation | BSL is not a fully permissive OSS license; enterprise features require license |
No tagline | AI Data / Synthetic Data | LLM dataset builder | Easy Dataset | AGPL-3.0 / open source | $0 software; model/API costs separate | Open-source app for creating datasets for LLM fine-tuning, RAG and eval | ✓ | Repository describes Easy Dataset as a powerful tool for creating fine-tuning datasets for large language models | LLM fine-tuning datasets, RAG datasets and evaluation datasets | Document/project-driven dataset creation, local app workflow and model-assisted generation | Manual review workflow and project-level dataset management; quality depends configured prompts/models | Desktop/web app stack, local database, LLM providers and dataset export workflows | Local/self-hosted app or Docker deployment | Local deployment can keep source files in operator environment; provider calls may send text externally | No SaaS governance in OSS; AGPL obligations apply for networked modifications | Individuals and small teams creating LLM training/eval datasets from existing documents | AGPL license may affect commercial redistribution; project is not an enterprise governance platform |
No tagline | AI Data / Synthetic Data | Tabular synthetic data library | Synthcity | Apache-2.0 / open source | $0 software; compute costs separate | Python library for generating and evaluating synthetic tabular data | ✓ | Repository describes Synthcity as a library for generating and evaluating synthetic tabular data for privacy, fairness and data augmentation | Tabular, time-series/survival and healthcare-style structured data depending plugin | Plugin-based generators such as GAN/privacy/fairness methods and benchmark workflows | Benchmarking, privacy/fairness/data augmentation focus and generator comparison | Python, scikit-learn style workflows, PyPI package and research notebooks | Local Python or research/ML pipelines | Can run locally; users must impute missing data first per docs and manage sensitive inputs | No SaaS governance; research code governance is local | Research teams comparing synthetic tabular generation methods | More research-oriented than enterprise UI; missing-data handling is outside the library |
No tagline | AI Data / Synthetic Data | Data curation framework | Bespoke Curator | Apache-2.0 / open source | $0 software; model/API/compute costs separate | Python library for bulk inference and scalable data curation | ✓ | Repository describes bulk inference and scalable data curation for post-training and structured data extraction | LLM post-training data, reasoning datasets, structured extraction corpora and model-generated examples | Batch inference, dataset curation, model response generation and downstream fine-tuning integrations | Dataset generation examples, provider batch support, structured extraction workflows and reproducible Python pipelines | OpenAI-compatible/provider APIs, Gemini Batch, Claude, Hugging Face datasets and Tinker fine-tuning integration | Local Python, notebooks, cloud batch workers or self-hosted pipelines | Data path depends on configured model providers; local orchestration can keep intermediate data private | No SaaS governance by default; pipeline governance is operator-managed | Teams producing large post-training datasets from LLM generations and filters | Quality depends on prompts, judge models and source data; not a managed labeling workforce |
No tagline | AI Data / Synthetic Data | LLM auto-labeling | Autolabel | MIT / open source | $0 software; LLM/API costs separate | Python library for labeling, cleaning and enriching text datasets with LLMs | ✓ | Repository says Autolabel labels, cleans and enriches text datasets with any LLM of your choice | Text classification, extraction, enrichment and benchmarkable labeling tasks | Config-driven LLM labeling pipelines with prompt templates and benchmark scripts | Model benchmarking, validation examples, benchmark reports and cost/time comparison against manual labeling | OpenAI, Anthropic, Gemini, vLLM/local models and other LLM providers through configuration | Local Python jobs, notebooks or self-hosted labeling pipelines | Data sent to configured LLM provider unless local models are used | No SaaS governance by default; key/data handling is operator-managed | Replacing manual text labeling with LLM-assisted weak supervision for early datasets | Accuracy depends strongly on task, labels and model choice; human review remains important |
No tagline | AI Data / Synthetic Data | Rule-based fake data | Faker | MIT / open source | $0 software | Python package for fake data generation | ✓ | Repository describes Faker as a Python package that generates fake data for bootstrapping databases, XML, stress tests or anonymized production-service data | Names, addresses, identifiers, localized fake values, fixtures and test records | Rule/provider-based fake record generation rather than learned statistical synthesis | Deterministic seeds, locale-specific providers and no real-data memorization by design | Python tests, fixtures, factories, ORMs and ETL scripts | Local Python package in app/test pipelines | No real data needed for basic fake data; safe for local development fixtures | No SaaS governance; local dependency management only | Simple fake test records and anonymized-looking fixtures | Does not learn correlations from real datasets; not a high-fidelity synthetic data model |
No tagline | AI Data / Synthetic Data | Synthetic data library | YData Synthetic | Open source | $0 software; compute costs separate | Open-source synthetic data generators for tabular and time-series data | ✓ | Repository describes synthetic data generators for tabular and time-series data | Tabular and time-series data | Train generator models to create synthetic structured/time-series records | Model-based synthesis; quality checks must be handled by user or YData Fabric stack | Python, notebooks and YData/Fabric ecosystem | Local Python/research pipelines | Local execution possible; privacy depends source data handling | No SaaS governance by default | Developers experimenting with open-source tabular/time-series synthetic data | Community repository is less turnkey than YData Fabric platform |
No tagline | AI Data / Synthetic Data | Annotation and data engine | Encord | Hosted platform tiers | Starter price not displayed; Team/Enterprise contact-style flow | Tiered platform plans with add-ons | Self-serve Starter entry exists; exact price not displayed | Pricing page lists Starter, Team and Enterprise; Team adds data agents, analytics, model evaluation and onboarding; Enterprise adds SSO, SLA, VPC/on-prem | Images, videos, audio, documents, DICOM/NIfTI, geospatial, ECG, 3D/LiDAR and custom data add-ons | Annotation, model prediction import, AI-assisted labeling and data agents | Performance analytics, model evaluation, Segment Anything Model 2, multi-workspace and add-on QA/eval workflows | API/SDK, cloud/VPC/on-prem deployments, model prediction import and multimodal annotation workflows | Cloud by default; VPC and on-prem are add-ons or Enterprise | Enterprise adds SSO, SLA/support and private deployment options | Starter/Team/Enterprise governance with multiple workspaces on Enterprise | Teams managing multimodal training data and model-assisted labeling | Exact self-serve pricing not visible; many modalities/features are add-ons |
No tagline | AI Data / Synthetic Data | Computer vision curation and labeling | Lightly | Commercial plus open-source tools | Contact sales | Enterprise suite and open-source tools; no public unit pricing captured | Some OSS tools available; SaaS/pricing not public | Official site lists automated data curation, model pretraining/fine-tuning, dataset management and smart data collection | Images/video/visual data, LLM/CV training data services and edge collection data | Find and label valuable samples, curate/label/manage datasets and collect high-value edge data | Data curation, QA, self-supervised pretraining and edge data selection; ISO 27001/GDPR posture listed | LightlyStudio, LightlyTrain, LightlyOne/Edge, open-source LightlySSL, ML pipelines and documentation | Hosted/platform plus open-source/local tooling and enterprise services | Site lists ISO 27001 and GDPR compliance | Enterprise onboarding with dedicated Slack/email support for services; public team tiers not captured | Vision teams reducing labeling volume by selecting valuable samples | Pricing is sales-led; not focused on tabular or LLM text synthetic data |
No tagline | AI Data / Synthetic Data | Training data service | Scale Data Engine | Commercial service / demo | Custom pricing | Managed data engine and expert data services | No public free tier captured | Official page describes Scale Generative AI Data Engine for RLHF, data generation, model evaluation, safety and alignment | Text, image, video, LiDAR/3D, RLHF, evaluation, red teaming and prompt-response data | Expert data generation, RLHF, red teaming, evaluation and annotation workflows | Expert review, safety/alignment workflows, complex prompt-response generation and model weak-point evaluation | Scale platform/services, frontier AI lab workflows and multimodal annotation pipelines | Managed enterprise service/platform | Enterprise data handling depends contract; page positions it for frontier AI and world-class data | Sales-led enterprise engagement and managed workforce/governance | Frontier labs and large enterprises needing managed high-quality training/eval data | No public unit pricing; less suitable for self-serve developers |
No tagline | AI Data / Synthetic Data | Expert AI training data service | Toloka | Commercial service | Custom pricing | Managed expert training/evaluation data service | No public free tier captured | Official site lists expert data for agents and models, including AI agent training, creative AI, advanced LLM/VLM datasets and programming data | Agent trajectories, preference data, reasoning chains, code data, creative multimodal data and safety red-team data | Expert-captured workflows, domain demonstrations, RL-style tasks and professional annotation/filtering | Built-in verification, safety red-teaming, expert feedback and quality filtering | LLM/VLM/agent training workflows, coding copilot datasets and RL environments/MCP replicas | Managed Toloka service and expert workforce | Security and privacy depend enterprise engagement; page emphasizes expert workflows | Managed expert team/service governance; pricing not public | AI labs needing expert demonstrations, agent trajectories and preference data | Sales-led service, not a downloadable synthetic data framework |
No tagline | AI Data / Synthetic Data | Enterprise synthetic data | Gretel | Commercial / NVIDIA-owned | Contact sales | Enterprise synthetic data platform; current pricing page redirects to contact | No public free tier captured on current contact page | Current site says Gretel is now part of NVIDIA and links to Data Designer and Safe Synthetics docs/contact | Synthetic data for generative AI, safe data, tabular/text use cases and ML customization workflows | Data Designer and Safe Synthetics workflows for generating safe synthetic data | Privacy-preserving synthetic data positioning, security/compliance links and NVIDIA/Gretel docs | Gretel SDK, Python client, REST API, Hugging Face examples, blueprints and NVIDIA ecosystem | Gretel/NVIDIA hosted or enterprise deployment paths per sales/docs | Security/compliance resources are linked; exact controls require current NVIDIA/Gretel engagement | Enterprise/sales-led governance | Enterprises evaluating NVIDIA-backed synthetic data tooling | Pricing and packaging changed after NVIDIA acquisition; public details are sparse |
No tagline | AI Data / Synthetic Data | AI data platform | Labelbox | Hosted platform tiers | $0 Free Tier; subscription/add-ons priced by estimate/contact | Free platform tier plus subscription tier and labeling-service add-ons | ✓ | Pricing page says free access to Labelbox Platform; Free Tier includes up to 30 users, 50 projects, 25 ontologies and 1 workspace | Multimodal datasets, annotations, model-assisted labels, eval chat data and curation metadata | Catalog, annotate, model features, natural-language search, model-assisted labeling and auto-labeling in subscription tier | Data curation with natural-language search, model-assisted labeling, AI critic and labeling quality guarantee in paid/service tiers | Labelbox platform, Alignerr services, custom embeddings, frontier/custom models and API workflows | Hosted Labelbox platform | Security and HIPAA add-ons available for subscription tier; SSO in subscription tier | Free up to 30 users; subscription unlocks unlimited users/projects/ontologies and SSO | Small teams starting annotation/data curation with a free hosted tier | Auto-labeling, Foundry models, AI critic and custom models require subscription; LBU/add-on pricing is not public |