6 Best AI Data Providers of 2025

1. Oxylabs

Oxylabs is a leading web intelligence collection platform specializing in large-scale data extraction for AI applications. Trusted by over 4,000 partners worldwide, Oxylabs has evolved into a comprehensive AI data provider that powers machine learning (ML) models across various industries. The advanced web scraping infrastructure enables businesses to collect and process massive datasets essential for training sophisticated AI systems. 

With the freshly-released Video Data solutions, Oxylabs is also pioneering the collection of multimodal training data for next-generation AI models. While Video Data API allows access to enterprise-grade multimodal data at scale, Video Datasets provide creator-approved, meticulously curated datasets so the customers can skip scraping and start training right away.

Features:

• Web Scraper API with AI-powered solutions for block-free data extraction;

• AI Video Data solutions with scalable video data collection capabilities;

• AI Studio – a low‑code platform gives developers and builders instant access to fresh, structured web data – without the hassle of writing scripts or dealing with HTML; 

• OxyCopilot – AI-powered assistant for generating web scraping and parsing requests using natural language prompts;

• Real-time data extraction from such popular targets like Amazon and Google with automated retry mechanisms;

• Scalable data collection infrastructure supporting millions of requests.

Available data: E-commerce, SERP & LLM monitoring, multimodal data for AI, real-estate, companies, travel, entertainment.

Formats:

• JSON, CSV, XLSX;

• API integration available;

• Storage options: SFTP, AWS S3, Microsoft Azure.

Pros:

• Advanced scraping technology: AI-powered solutions that bypass anti-bot measures and deliver high success rates for data collection;

• Comprehensive data coverage: Access to high-quality data across e-commerce, real-estate, travel, or other for diverse AI training needs;

• Developer-friendly platform: Ready-to-use code samples, detailed tutorials and guides, and active community support.

Cons:

• Learning curve: Advanced features may require technical expertise to implement effectively;

• Premium pricing: May not be affordable for smaller businesses.

Pricing:

• Free trial including up to 2K results available;

• Web Scraper API pricing: starting $49/month with the Micro plan;

• Standard datasets: starting $5,000/month.  

G2 score: 4.5

2. Appen 

Appen stands as the global leader in AI training data, representing a pivotal force that has enabled businesses to deploy world-class AI applications for nearly three decades. Their AI training data services span the entire machine learning lifecycle, from initial data collection and data labeling to supervised fine-tuning, evaluation, and benchmarking. Appen's expertise is particularly renowned in natural language processing, where they've helped develop the most sophisticated language models through their multilingual high-fidelity datasets covering over 180 languages and dialects. 

One thing that might prevent some potential customers from selecting Appen is their 100% custom pricing model. While this approach ensures a personalized approach and high-quality product, it slows down the purchasing process.

Features:

• AI Data Platform (ADAP) for collaborative dataset enhancement;

• Custom data sourcing, collection and creation services;

• Data annotation, model evaluation, and prompt preference management;

• Domain-specific RLHF and multi-modal LLM support;

• Scale and flexibility, meeting the demands of the most ambitious AI projects.

Available data: All major data types, including image, video, text, audio and custom collection/labeling across NLP, CV, speech.

Formats:

• Custom formats based on project requirements;

• JSON, CSV, XML integration;

• API access available;

• Cloud storage integration.

Pros: 

• Professional global workforce: Access to over 1 million skilled contributors worldwide ensuring diverse, high-quality datasets;

• Proven track record: Nearly three decades of experience enabling industry leaders to build recognizable AI applications;

• Comprehensive quality control: Advanced data pipeline with built-in quality measures.

Cons:

• No upfront pricing: All pricing requires manual inquiry and human approval process, which slows down procurement;

• Complex onboarding: Getting started requires going through human process rather than self-service options.

Pricing: 

• Custom, contact required;

• No free trial mentioned.

G2 score: 4.1

3. Scale AI

Founded in 2016 and now valued at $29 billion, Scale AI specializes in providing the highest quality training datasets for the most demanding AI applications, particularly in autonomous vehicles, defense systems, and large language models (LLMs). Their Data Engine powers many of the world's most advanced LLMs and generative models through world-class reinforcement learning from human feedback (RLHF), synthetic data generation, and comprehensive model evaluation services. 

Scale AI's client roster includes Meta, Time, Pinterest, General Motors, and the U.S. Department of Defense, demonstrating their capability to deliver mission-critical AI training data at unprecedented scale and precision.

Features: 

• Generative AI Data Engine for advanced LLMs and generative models;

• Human-in-the-loop approach combining machine learning and human input;

• Data Engine for improving ML models with high quality, diverse datasets;

• Integration with leading AI models from Google, Meta, Cohere, and more;

• Scale Evaluation platform for testing LLMs against benchmarks.

Available data: Multimodal annotation (text, image, video, etc.), data for autonomous vehicles, government and defense-specific datasets.

Formats:

• JSON, CSV, XML;

• API integration;

• Cloud-native delivery;

• Custom formats for enterprise clients.

Pros: 

• Enterprise-grade infrastructure: Trusted by the top companies worldwide for mission-critical applications;

• Cutting-edge technology: Powers many of the most advanced LLMs and generative models in the world;

• Proven scale: Handles large-volume production projects with consistent quality and performance.

Cons:

• No upfront pricing: Public per‑unit pricing isn’t published, budgets still require discovery;

• Limited flexibility: Focused on several specific use cases, may not be suitable for various scenarios or small-scale projects.

Pricing:

• Custom, contact required;

• Pay as you go available;

• Data Annotation: First 1,000 labeling units at no cost;

• Data Management: First 10,000 images at no cost.

G2 score: 5 (though, only 1 review is listed).

4. Nexdata 

Nexdata has established itself as a premier AI training data specialist over more than a decade of dedicated service to the AI community. Founded in 2011, this globally renowned company has empowered over 10,000 organizations worldwide to enhance their AI models' performance through curated, high-quality datasets. 

Similar to Appen and Scale AI, Nexdata’s pricing is custom, meaning users should reach out to sales to get a quote for their specific use case or project. What’s great – their ISO9001-certified quality management system ensures consistent, reliable training data that meets the rigorous standards required for production AI systems.

Features:

• Extensive library of off-the-shelf datasets ready for immediate delivery;

• PB-level ethical datasets with end-to-end project lifecycle coverage;

• Scalable platform supporting labeling of 10,000 annotators simultaneously;

• ISO9001 quality management certification with multi-level quality inspections;

• Global resources covering hundreds of languages worldwide.

Available data: LLM & GenAI data, speech data, image/video data, curated data for NLP, speech recognition and computer vision, autonomous vehicle data, video captions, STEM content, user-generated dialogue, multimodal datasets combining text, audio, images, and video.

Formats:

• .bin, .json, .xml, .csv, .xls, .sql, .txt formats;

• Delivery via S3 Bucket, SFTP, Email, UI Export, REST API, SOAP API, Streaming API;

• Custom format support available.

Pros: 

• Massive ready-to-use library: PB-level datasets that can be delivered in seconds to quickly improve AI model accuracy;

• Proven track record: Over 13 years of experience empowering 10,000+ companies worldwide;

• Quality assurance: 97% accuracy guarantee with rigorous quality control processes.

Cons: 

• No upfront pricing: Potential customers need to contact sales before purchasing, which indicates that the entry cost is high and might not be suitable for smaller businesses;

• Limited public reviews: Insufficient customer reviews available for comprehensive evaluation.

Pricing:

• Free samples available for individual data requirements;

• Custom pricing based on project scope.

G2 score: No G2 rating listed.

5. Defined.ai 

Another popular AI data provider in 2025 is Defined.ai. The company puts a lot of emphasis on ethical data collection, diversity, and transparency in AI development. This commitment extends beyond data collection to include comprehensive bias detection, consent management, and fair compensation practices for data contributors. Defined.ai's expertise spans multiple AI domains including natural language processing, computer vision, speech recognition, and conversational AI, with particular strength in multilingual datasets and cross-cultural AI applications.

Features:

• World's largest AI marketplace for ethically collected datasets;

• Neevo platform providing unique crowd-sourced data collection;

• Accelerat.ai conversational AI solution backed by the Portuguese Recovery and Resilience Plan;

• Comprehensive data sourcing, preparation, and real-world model evaluation capabilities.

Available data: Diverse datasets in speech, natural language processing, computer vision, multilingual data, healthcare, finance, and telecommunications focused datasets.

Formats:

• JSON, CSV, XML;

• Audio, text, and video annotations;

• API integration available;

• Custom format support.

Pros:

• Ethical data standards: All data collected and managed with highest ethical standards, ensuring responsible AI development;

• Global recognition: Recognized by World Economic Forum, United Nations, and Forbes for AI innovation. 

Cons: 

• No upfront pricing: Like competitors, offers no upfront pricing requiring manual inquiry which slows procurement process;

• Complex feature set: Advanced features are slightly complex, requiring a learning curve for new users.

Pricing: 

• Custom pricing;

• No free trial mentioned.

G2 score:4.5

6. Twine AI

Twine AI has emerged as a premier global provider of custom AI training data, leveraging a network of over 500,000 expert freelancers and consultants in 190+ countries to deliver highly specialized datasets for ML applications. Their approach to AI training data encompasses everything from speech recognition and computer vision datasets to complex behavioral analysis and emotion recognition data.

Their emphasis on ethical data collection, including comprehensive consent processes and bias minimization strategies, positions them as a responsible choice for world-renowned organizations, such as Wix, Amazon, Synthesia, and others.

Features:

• Data collection and RLHF techniques to tailor AI models to specific needs;

• Custom data collection for audio, image, and video with dedicated project management;

• Perfectly synchronized multimodal datasets where different data types are captured simultaneously;

• Monthly subscription model with flexibility to cancel or pause.

Available data: Multimodal datasets combining text, audio, images, and video, speech recognition data, custom datasets for specialized AI applications.

Formats:

• JSON, CSV, XML;

• Audio formats (WAV, MP3, etc.);

• Video formats (MP4, AVI, etc.);

• Custom metadata provision based on specifications.

Pros:

• Diverse global network: Over 500,000+ global experts providing unique data assets while minimizing model bias;

• Feedback loop: Feedback meetings with a dedicated Project Manager to ensure improved workflow;

• Flexible payment model: Monthly subscription allowing cancellation or pause at any point with multiple payment options.

Cons: 

• No upfront pricing: Offers no upfront pricing requiring manual inquiry to receive a quote;

• Newer market presence: Less established compared to some enterprise-focused competitors.

Pricing: 

• Custom pricing;

• No free trial mentioned;

• Monthly subscription with a possibility to cancel anytime.

G2 score: No G2 rating listed.

Key considerations when choosing a provider

Choosing the right AI data provider requires careful attention to every detail – from available data formats and delivery options to pricing and user ratings on various platforms. Different providers excel in different areas, so matching their strengths and advanced features to your specific project needs is essential. Whether you prioritize speed, accuracy, ethical sourcing, or 24/7 assistance, weighing each factor will ensure you select the best fit for your AI initiative.

What is training data and who provides it?

AI training data is the type of data used to teach AI models how to perform certain tasks, like recognizing patterns, generating text, or making decisions. This data can include anything from text, images, videos, audio, transcripts, and it must be labeled or organized in a way that the AI can learn from. The quality, quantity, and diversity of training data directly impact the model’s performance and accuracy.

There are a few ways how AI training data is acquired. Businesses can source data from specialized data providers that offer ready-to-use, high-quality training datasets tailored for specific AI applications. Others choose to collect their own data with the help of web scraping, which allows them to gather large volumes of publicly available information from websites for AI training purposes. The choice of data source depends on the use case, desired data quality, legal requirements, and ethical considerations.

Summary