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Case Study
Platforms for AI Training Datasets: Where to Find the Right Data
October 1, 2026
TL;DR: Platforms for AI training datasets fall into two main categories. Frontier-grade expert data providers such as Athyna Intelligence, Scale AI, Surge AI, and Mercor provide expert-generated data, RLHF, evaluations, and reasoning tasks for teams training and evaluating advanced AI models. Dataset marketplaces such as Kaggle and Hugging Face provide existing datasets for broader machine learning use cases. The right category depends on whether you need ready-made data or experts who can produce and evaluate data for a specific task.
Searching for platforms for AI training datasets can surface very different types of providers in the same list. Some offer existing datasets that teams can download or license for machine learning projects. Others recruit and vet domain experts to create, label, or evaluate data for frontier AI models.
The right category depends on what you’re building. A team training a spam classifier may only need an existing labeled dataset, while a lab evaluating an LLM on coding, scientific reasoning, or professional tasks may need qualified experts who can produce or assess data against a specific task specification. Understanding that distinction is the first step in deciding where to find AI training data.
Platforms for AI training datasets generally fall into two categories: frontier-grade expert data providers and dataset marketplaces.
Expert providers recruit and vet domain specialists to create or evaluate data for specific models and tasks, including reinforcement learning from human feedback (RLHF), model evaluations, reasoning tasks, and red-teaming.
An AI training data marketplace lets teams discover, download, or license existing datasets for broader machine learning use cases such as computer vision, natural language processing (NLP), speech, and tabular data.
Which category fits depends on the model and its data needs. A spam classifier may only need an existing labeled dataset, while a frontier model handling coding, scientific reasoning, or professional tasks may require experts to create tasks and evaluate outputs against domain-specific criteria.
Expert data platforms for AI labs include Athyna Intelligence, Scale AI, Surge AI, Mercor, Turing, Handshake AI, Invisible, Labelbox, Toloka, SuperAnnotate, and Encord. These human data providers for AI training support workflows for creating, labeling, or evaluating data, including reinforcement learning from human feedback (RLHF), model evaluation, reasoning tasks, red-teaming, and domain-specific training.
Athyna Intelligence works with vetted PhDs and domain experts across LATAM to create and evaluate data for frontier AI labs. Its offering includes Athyna Intelligence Datasets alongside expert-led AI training and evaluation workflows.
Scale AI provides data infrastructure and human data services for training, post-training, and evaluating generative AI models through its Data Engine.
Surge AI provides human-generated data for training and post-training large language models, with a focus on collecting human feedback and evaluations around specific model behaviors and tasks.
Mercor connects AI labs with professionals in fields such as software engineering, medicine, law, finance, and consulting to create training data and evaluate models on specialized tasks.
Turing provides domain experts for AI training and evaluation across software development, STEM, healthcare, finance, and other specialized fields.
Handshake AI connects AI companies with students, graduates, researchers, and professionals for project-based AI training and evaluation work through its contributor network.
Invisible combines AI data infrastructure with human expertise to support data preparation, training, evaluation, and other operational workflows around AI systems.
Labelbox provides data infrastructure for creating, managing, and evaluating training data across generative AI and multimodal workflows. Its Alignerr network adds subject-matter experts for specialized AI training projects.
Toloka provides human-generated and human-evaluated data for LLM and multimodal model development, covering both large-scale data work and tasks requiring specialized knowledge.
SuperAnnotate combines data annotation and curation infrastructure with managed data services for teams training and evaluating AI models.
Encord provides data management, curation, annotation, and evaluation infrastructure for computer vision and multimodal AI, including projects involving complex visual and sensor data.
Athyna Intelligence Datasets are off-the-shelf and custom AI training datasets for frontier AI labs. The datasets are authored and reviewed by vetted PhDs and domain experts across LATAM, with contributors matched to tasks in their field of expertise.
Teams can access Athyna Intelligence Datasets in three ways:
The current dataset library covers Terminal-Bench, GDPval, and STEM reasoning.
Terminal-Bench includes coding tasks built as reproducible, Docker-pinned environments and run through the Harbor harness. GDPval includes professional tasks and deliverables authored to the GDPval specification and graded against expert-quality output. STEM reasoning includes original mathematics, physics, chemistry, biology, and other STEM tasks traced to peer-reviewed sources with DOIs.
For AI labs that need data beyond the existing library, Athyna Intelligence can scope a custom dataset around the project’s task specification, with PhDs and domain experts authoring and reviewing tasks in their respective fields.
AI training data marketplaces include Kaggle, Hugging Face, AWS Data Exchange, Defined.ai, Shaip, Opendatabay, and Datarade. These platforms let teams discover, download, or license existing datasets across machine learning use cases such as natural language processing, computer vision, speech, tabular data, and industry-specific applications.
Kaggle hosts community-published datasets alongside machine learning competitions, notebooks, and models. Its dataset catalog covers areas such as computer vision, NLP, classification, tabular data, and other common ML tasks, with many datasets available for free.
Hugging Face Datasets provides a large catalog of datasets for machine learning and AI development. Teams can find data across text, image, audio, video, tabular, geospatial, time-series, and other formats, including many open datasets that can be loaded through the Hugging Face ecosystem.
AWS Data Exchange is a marketplace for discovering and licensing third-party data through AWS. Its catalog includes free and paid datasets across industries such as financial services, healthcare, retail, media, and telecommunications, with data that can be integrated into AWS-based analytics and machine learning workflows.
Defined.ai operates an AI data marketplace with ready-to-use datasets across speech, text, image, video, and multimodal formats. It also provides data collection and annotation services for teams that need data beyond its existing catalog.
Shaip offers licensed datasets across areas including speech, computer vision, healthcare, and physical AI. Alongside its data catalog, the company provides custom data collection, annotation, and RLHF services, giving it offerings that extend beyond off-the-shelf datasets.
Opendatabay is a marketplace for licensed datasets across text, image, audio, video, code, tabular, time-series, human feedback, synthetic data, and agentic AI data. Teams can browse existing data products from multiple providers based on the format and use case they need.
Datarade is a broader B2B data marketplace that aggregates datasets from third-party providers across hundreds of categories. Its catalog includes AI and machine learning training data alongside industry, location, commerce, financial, and other types of commercial data.
Choosing an AI training data platform depends on the model, its data needs, and the expertise required. Frontier AI tasks often require expert data providers, while standard ML projects can often use existing datasets from a marketplace.
Teams that need to build the human side of the training pipeline can also consider how specialists are sourced and evaluated for ongoing model training and evaluation work. Athyna’s guide to hiring the right AI model training specialist explains what to look for when selecting specialists for AI training and evaluation work.
Athyna's content specialists covering global hiring and AI training trends for growing teams.
