The Data Index
The global AI training dataset market hit $3.59 billion in 2025and is racing toward $16 billion by 2033. Every company generates data that AI companies will pay for — most just don't know it yet. This guide breaks down what's valuable, who's buying, and what your data is worth.
Buy and sell financial data — transaction logs, POS records, insurance claims, lending data, trading patterns, and payment flows. Fintech AI and fraud detection models need real transaction data at scale.
Buy and sell education data — student performance, LMS interactions, tutoring transcripts, enrollment patterns, and campus operations. EdTech AI needs real learning outcome data to personalize education.
Buy and sell travel data — flight schedules, booking patterns, traveler behavior, airport operations, cargo manifests, aviation safety, and aircraft telemetry. Real-world data for travel optimization AI.
Buy and sell synthetic data — AI-generated images, text, audio, video, and tabular data, plus augmented real-world datasets. The meta-market for AI training data made by AI.
Buy and sell document data — legal filings, contracts, patents, medical charts, inspection reports, and corporate filings. NLP companies need millions of real documents to train extraction, classification, and summarization models.
Buy and sell location data — GPS fleet tracking, foot traffic patterns, delivery routes, indoor positioning, maritime AIS, and satellite coordinates. Mobility AI and logistics companies need real-world spatial data.
Buy and sell image data — satellite imagery, medical scans, product photography, construction progress photos, thermal imaging, and microscopy. Computer vision AI needs labeled image datasets across every domain.
Buy and sell retail data — purchase histories, loyalty program records, cart abandonment patterns, pricing elasticity, and consumer demographics. Retail AI and recommendation engines need real shopping behavior data.
Buy and sell behavioral data — app usage patterns, clickstreams, search queries, content engagement, survey responses, and session replays. Product AI and personalization engines need real user behavior data.
Buy and sell logistics data — shipping manifests, warehouse inventory, customs declarations, cold chain logs, last-mile delivery attempts, and container tracking. Supply chain AI needs real operational data.
Buy and sell automotive data — OBD-II diagnostics, driving behavior, autonomous vehicle training data, repair records, EV charging patterns, and collision reports. Self-driving and fleet management AI need real vehicle data.
Buy and sell real estate data — property appraisals, MLS listings, rent rolls, building energy usage, smart building sensors, and construction bids. PropTech AI needs real property and transaction data.
Buy and sell agriculture data — crop yields, soil sensors, livestock tracking, food safety inspections, supply chain provenance, and irrigation records. AgTech AI needs real farm and food system data.
Buy and sell audio data — call center recordings, voicemail, podcast raw audio, courtroom proceedings, emergency dispatch, and environmental sound. Speech AI and voice recognition companies need diverse real-world audio datasets.
Buy and sell sensor and IoT data — industrial equipment telemetry, smart home devices, agricultural sensors, wearables, pipeline monitors, and weather stations. Predictive maintenance AI needs real sensor data with failure patterns.
Buy and sell code data — GitHub corpora, source code, code reviews, bug reports, API documentation, package registries, and developer telemetry. The training data behind Copilot, Codex, Cursor, and every code-generation AI.
Buy and sell crypto data — on-chain transactions, wallet activity, NFT sales, DeFi protocols, smart contract code, market pricing, and Web3 identity. The training data for crypto analytics and trading AI.
Buy and sell energy data — smart meter readings, solar panel output, grid load patterns, EV charging usage, wind turbine telemetry, and carbon emissions tracking. Energy AI needs real consumption and generation data.
Buy and sell government data — court records, transit ridership, 311 requests, building code violations, campaign finance, and environmental impact reports. GovTech AI needs structured public sector data.
Buy and sell communications data — email metadata, enterprise chat logs, helpdesk tickets, call detail records, chatbot conversations, and network traffic patterns. Enterprise AI needs real communication data for training.
Buy and sell manufacturing data — quality control images, defect logs, assembly line telemetry, CAD files, maintenance records, and CNC machine data. Industrial AI needs real production and quality data.
Buy and sell sports and entertainment data — player tracking, ticket sales, fan engagement, broadcast footage, esports replays, and betting line movements. Sports analytics AI needs real performance and engagement data.
Buy and sell AI training datasets — LLM corpora, RLHF data, benchmarks, synthetic data, and domain fine-tuning datasets.
Buy and sell legal data — case law, contracts, litigation outcomes, regulatory filings, and compliance datasets.
Buy and sell medical data — EHR records, diagnostic imaging, genomic sequences, lab results, clinical trials, and wearable health data. The healthcare AI training data market hit $1.47B in 2025. Hospitals and clinics are sitting on data worth millions.
Buy and sell cybersecurity data — threat intelligence, breach data, vulnerability databases, and security incident datasets.
Buy and sell workforce data — salary benchmarks, attrition data, skills gaps, DEI metrics, and labor market datasets.
Buy and sell hospitality data — hotel occupancy, restaurant POS, travel booking, airport traffic, and tourism datasets.
Buy and sell marketing data — ad performance, SEO rankings, attribution, influencer metrics, and campaign datasets.
Buy and sell video data — surveillance footage, dashcam recordings, drone video, surgical recordings, sports broadcasts, and more. The AI video analytics market is growing at 22% CAGR. Computer vision companies need millions of hours of real-world footage for training.
Buy and sell defense data — military maintenance, contract awards, satellite tracking, and aerospace datasets.
Buy and sell cannabis industry data — dispensary sales, lab testing, seed-to-sale tracking, and regulatory compliance datasets.
Buy and sell nonprofit data — donation patterns, grant awards, volunteer engagement, and social impact measurement datasets.
Buy and sell criminal justice data — sentencing, recidivism, bail, parole, and justice system outcome datasets.
Buy and sell scientific data — academic papers, citation networks, lab experiments, peer review, research grants, conference proceedings, patents, and replication datasets. The reasoning data AI labs scrape hard.
Buy and sell climate data — climate models, carbon emissions, air and water quality, biodiversity, deforestation, renewable energy, weather, and ocean monitoring. The data powering ESG and climate AI.
01 / The Market
In 2025, the global AI training dataset market crossed $3.59 billion. By 2033, it's projected to reach $16.3 billion— growing at 22.9% annually. The dataset licensing market alone is worth $4.8 billion and heading to $22.6 billion by 2034.
This isn't speculative. In 2025, $157 billionwas spent on 33+ acquisitions in AI data, cloud, and governance. Salesforce paid $8 billion for Informatica. IBM paid $11 billion for Confluent. Meta acquired a 49% stake in Scale AI — the company that labels and evaluates the data that trains the world's largest language models.
When Roche acquired Flatiron Health for $1.9 billion, it valued each oncology patient record at roughly $1,000 per record. IQVIA has access to over 600 million patient records globally. Truveta is building a 10-million-patient genomic database by buying data directly from hospitals.
The companies spending this money aren't startups burning venture capital. They're Google, Microsoft, Amazon, Meta, and the pharmaceutical giants. They need data to train AI models, and the data they need is sitting in filing cabinets, server rooms, and cloud databases at companies that have no idea what they're sitting on.
02 / Why Your Data Matters
AI models are only as good as the data they're trained on. And the biggest bottleneck in AI development today isn't compute, isn't algorithms — it's access to high-quality, real-world training data.
Synthetic data can help, but it can't replace the messy, complex, edge-case-rich data that comes from actual hospital admissions, actual insurance claims, actual security camera footage, actual manufacturing defect logs. AI companies have exhausted most publicly available datasets. What they need now is proprietary data from the companies that generate it every day.
That means your company. Whether you're a hospital, a trucking fleet, a dental practice, a restaurant chain, a law firm, or a solar farm — you're generating data that AI companies are actively searching for and willing to pay real money to access.
The data doesn't need to be clean. It doesn't need to be labeled. It doesn't even need to be organized. Raw data has value. Cleaned data has more. Annotated data by domain experts — a radiologist labeling tumors, a lawyer tagging contract clauses, a mechanic flagging defective parts — that's where the real premium is.
Enterprise text alone — CRM notes, support tickets, email threads, internal wikis — represents the largest and most valuable pool of training data for language models. Your company's institutional knowledge, captured in unstructured text, is training material for the next generation of AI assistants, coding tools, and decision engines.
03 / What Sells
Not all data is created equal. Pricing varies wildly — from fractions of a cent per record for commodity datasets to thousands of dollars per record for rare, annotated medical or genomic data. Here's what commands the highest prices:
EHR records, medical imaging (DICOM), clinical trial data, discharge summaries. Flatiron Health's oncology records sold at ~$1,000 each. The healthcare NLP market alone is $5.18B. HIPAA de-identification required but buyers handle BAAs.
Whole genome sequencing, exome data, RNA expression, epigenetic profiles. The genomics market hit $32.6B in 2025. Pharma companies pay premium for datasets linking genetic variants to drug responses. Rare disease data commands the highest prices.
Dashcam footage, LiDAR point clouds, fleet telematics, driving behavior data. Companies like Waymo, Tesla, and Cruise need millions of hours of real driving scenarios. Annotated edge cases (near-misses, weather, construction) are extremely valuable.
CT scans, MRI, X-rays, mammograms with radiologist annotations. The AI medical imaging market is growing at 33% CAGR to $37.5B by 2035. NHS launched a 700,000-mammogram AI trial in 2025. Pathology slides with expert labels sell for hundreds each.
POS transactions, insurance claims, lending data, credit bureau records. Volume is the play — millions of records at pennies each or thousands of rich longitudinal records at dollars each. Fraud detection AI and credit scoring models are the primary buyers.
Manufacturing telemetry, oil & gas pipeline sensors, fleet vehicle diagnostics, smart building HVAC. Predictive maintenance AI needs failure-case data most — normal operation is cheap, pre-failure patterns are gold.
04 / Who's Buying
The buyers aren't mysterious. They're the companies building the AI products you already use — and they're spending at a pace that makes the 2021 crypto boom look quaint.
The Big Labs:OpenAI, Anthropic, Google DeepMind, Meta AI, and xAI are in a constant arms race for training data. They've already consumed most of the public internet. What they need now is specialized, domain-specific data that doesn't exist on the web — clinical records, proprietary databases, sensor feeds, internal documents.
The Data Platforms:Scale AI (valued at $14B+), IQVIA ($45B market cap), Tempus AI ($6.1B IPO), Veracyte, Flatiron, Datavant — these companies aggregate, clean, label, and resell data to the AI labs. They're the middlemen, and they're always buying from the source.
The Industry Giants:Every Fortune 500 company is building internal AI. JPMorgan, UnitedHealth, Johnson & Johnson, Caterpillar, John Deere — they need training data specific to their domain. Often they'll buy from competitors, suppliers, or adjacent industries to get data they can't generate internally.
The Startups:Thousands of AI startups in healthcare, legal, finance, logistics, and manufacturing need training data to build their products. They can't afford to generate it themselves, so they buy it. A startup building radiology AI needs 50,000 annotated chest X-rays. A legal AI company needs millions of contracts. A fleet management startup needs years of telematics data.
05 / Pricing
Data pricing isn't standardized — it depends on rarity, quality, volume, exclusivity, and the buyer's use case. But there are clear tiers:
Tier 1: Raw Data ($0.01 — $10/record)
Unprocessed, as-is exports from your systems. CSV dumps, database extracts, log files. Minimal formatting or cleaning. Value is in volume — millions of records at low per-unit cost. Buyers do the cleaning and annotation themselves.
Tier 2: Cleaned & Structured ($1 — $100/record)
De-identified (if applicable), formatted to industry standards (FHIR, OMOP, XBRL), linked across tables, with consistent schema. This is where most data deals happen. Buyers pay for the work you've done to make the data usable.
Tier 3: Annotated & Enriched ($10 — $1,000+/record)
Expert-labeled by domain specialists. A radiologist marking tumors on CT scans. A lawyer tagging clause types in contracts. An engineer annotating failure modes in sensor data. This is the premium tier — it's expensive to produce and impossible to fake.
Deal structures vary: one-time purchases, recurring subscriptions, revenue shares, and licensing agreements are all common. Exclusive access commands 3-10x premium over non-exclusive. Data with longitudinal depth (same entities tracked over time) is worth more than point-in-time snapshots.
The data monetization market overall is worth $5.2 billion in 2025, projected to reach $48.5 billion by 2035. North America accounts for 68% of the market. If you're generating data in the US, you're in the most valuable market on earth for selling it.
06 / Getting Started
FileYield isn't just a marketplace where you list data and wait. We're a brokerage. That means we actively find buyers for your data through outreach, matching, and industry connections. Here's how it works:
1. Free Valuation.Tell us what data you have. Our AI-powered appraisal tool walks you through a simple questionnaire — industry, data types, volume, format. Within minutes, you'll see an estimated value range based on real market data and recent deals.
2. We Find Buyers.This is where we're different. We don't just post your listing and hope someone finds it. We identify companies that need your specific data type, reach out directly, and make introductions. Our research database covers thousands of AI companies and their data needs.
3. Secure Deal Room.Once a buyer is interested, negotiations happen in our platform. NDAs, sample data exchange, pricing discussions, and final agreements — all tracked and documented. Seller identity stays anonymous until you choose to reveal it.
4. Compliance Built In.For regulated data (healthcare, financial, education), we guide you through de-identification requirements, provide BAA templates, and connect you with compliance partners if needed. We don't touch your raw data — we connect you with buyers who handle it properly.
5. Fair Commission. 8% seller-side, 6% buyer-side. No upfront fees. No monthly subscription. You only pay when a deal closes.
Free valuation. No commitment. Takes 5 minutes.
Get Your Free Valuation07 / Compliance
Short answer: almost certainly yes, with the right precautions. Companies sell data every day — it's a multi-billion dollar industry. But the rules depend on what kind of data you have:
Healthcare (HIPAA):You can sell de-identified patient data without patient consent. HIPAA's Safe Harbor method requires removing 18 specific identifiers (names, dates, locations, etc.). Expert Determination is an alternative for datasets that need more specificity. A Business Associate Agreement (BAA) is required between you and the buyer. This is standard — every major health data transaction uses it.
Financial (GLBA/SOX): Aggregated, anonymized financial data is routinely sold. Individual account data requires consumer notice and opt-out. Transaction patterns, market data, and risk models are all fair game with proper anonymization.
Consumer (CCPA/GDPR): If your data includes personal information about California or EU residents, consumers have rights to know, delete, and opt out of the sale. But B2B data, operational data, IoT sensor data, and anonymized datasets are generally unaffected.
Proprietary Business Data:Your company's operational data — manufacturing logs, supply chain records, fleet telemetry, energy consumption — is yours to sell. No special regulations apply beyond standard contract law. This is the easiest category to monetize.
The key principle: if you generated the data in the course of business, you almost certainly own it. The exceptions are data with personal information (which needs anonymization) and data subject to contractual restrictions (check your vendor and customer agreements).
08 / The Future
AI M&A volume hit 262 deals in the first half of 2025— up 35% year over year. Google spent $32 billion on Wiz. Anthropic committed $50 billion to AI infrastructure. Amazon poured $50 billion into AI data centers. The money flowing into AI data acquisition is unprecedented.
But this window won't stay open forever. As AI models improve, they'll need less data — or they'll generate synthetic alternatives. The companies selling data today, while demand is at its peak and supply is still fragmented, are the ones capturing the most value.
Synthetic data adoption grew 36% in 2025. It's getting better. But for now, real-world data from real businesses is irreplaceable for training AI that works in the real world. The question isn't whether your data is valuable — it's whether you'll monetize it before the window closes.
Every day your data sits in a database doing nothing, it's depreciating. Clinical data from 2020 is less valuable than data from 2025. Sensor data loses relevance as equipment is upgraded. Transaction patterns shift with consumer behavior. The best time to sell was yesterday. The second best time is now.
Free valuation. No commitment. See what AI companies will pay for what you already have.