RLHF Data Services Market Size, Share, Growth and Forecast, 2025-2035

Expert human data has become a core input for AI labs

The global RLHF data services market was valued at USD 4.07 billion in 2025 and is projected to increase from USD 4.80 billion in 2026 to USD 21.29 billion by 2035, registering a CAGR of 18.00% during 2026–2035. Spending comes from model developers that buy ranked answers, expert-written solutions, graded test sets and recorded agent tasks to train and check large language models before release. North America held the largest regional share, 45.0% in 2025, because the United States drew $109.08 billion of private AI investment in 2024, according to Stanford HAI. Asia Pacific is the fastest-growing region at 19.16% a year as China, India and Malaysia fund national model and talent programs. A major commercial change came in June 2025, when Meta took a 49% stake in Scale AI and several labs moved work to other suppliers. Buyers are also shifting budgets toward credentialed experts in coding, STEM, medicine and law rather than generalist crowds.

What does the RLHF data services market include?

The RLHF data services market covers paid work that supplies human judgment for training and testing AI models after pretraining. This includes pairwise preference rankings, expert-written answers and corrections, rubric-graded evaluation sets and recorded agent trajectories in browsers, code editors and business software. Buyers contract per task, per expert hour or through managed programs with quality guarantees and confidentiality terms.

The scope excludes general image and text labeling for classic machine learning, annotation software sold as a stand-alone license and model training compute. Value is counted at the vendor level, so payments that vendors pass to contract experts are included in the service price paid by the lab or enterprise.

Preference data leads while agent trajectories grow fastest

Why do preference data services hold the largest share?

Based on service, the market is categorized into preference data, expert annotation, evaluations and agent trajectories. Preference Data led with 36.0% of 2025 value because ranking and rewriting model answers remains the core step of post-training at frontier labs. Reuters reported that Surge AI, a preference data specialist founded in 2020, counts Google, OpenAI and Anthropic among its clients. Agent Trajectories grow fastest at 19.96% a year as labs train models to act in software. Turing listed agentic behavior among the focus areas of its AGI Advancement unit when it raised $111 million in a Series E round in March 2025.

Why does coding lead and medicine grow fastest?

Based on domain, the market is categorized into coding, STEM, medicine and law. Coding led with 41.0% of 2025 value because software tasks can be checked against tests, which makes expert-written fixes easy to grade and reuse. OpenAI built SWE-bench Verified in August 2024 by having 93 professional Python developers screen 1,699 samples down to 500. Medicine grows fastest at 19.25% a year as health buyers want clinician-graded evidence before deployment. HealthBench, released in May 2025, relies on rubrics written by 262 physicians across 5,000 conversations, according to its technical paper.

Why do frontier labs dominate buying today?

Based on buyer, the market is categorized into frontier labs and enterprises. Frontier Labs led with 72.0% of 2025 value because a handful of model developers fund most large preference and evaluation programs. Meta took a 49% stake in Scale AI for a reported $14.3 billion in June 2025, according to RTÉ, which shows how far labs will go to secure supply. Enterprises grow fastest at 19.41% a year as banks, law firms and hospitals test models on their own work. Mercor's APEX index, published Oct. 1, 2025, used about 100 experts to write 200 cases in banking, law, consulting and medicine.

Lab post-training budgets and expert demand drive growth

Frontier labs need large volumes of human preference data

Frontier model developers depend on human preference data because ranking, rewriting and scoring answers is still the main way to shape model behavior after pretraining. Stanford HAI's 2025 AI Index counted 40 notable models from U.S. institutions in 2024, against 15 from China and three from Europe, and each release needs fresh rounds of post-training on large language models. Global private investment in generative AI reached $33.9 billion in 2024, up 18.7% from 2023, according to the same index. Reuters reported that Surge AI sought up to $1 billion at a valuation above $15 billion in its first outside capital raise. In the Credence Research model this driver adds about 7.2 percentage points to annual growth to 2035.

Enterprise domain work lifts expert annotation spending

Enterprises are paying for expert annotation because general models still miss the standards of regulated professions. OpenAI's GDPval evaluation, released Sept. 25, 2025, covered 1,320 tasks across 44 occupations in nine sectors, which shows how buyers now test models on real professional output. Stanford HAI reported that 78% of organizations used AI in 2024, up from 55% a year earlier, which widens the pool of buyers that need domain checks. Handshake reported more than 500,000 PhD holders in its network when it launched Handshake AI in June 2025, as The Letter Two reported. In the Credence Research model this driver adds about 5.0 percentage points to annual growth to 2035.

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Continuous alignment and multilingual evaluation create projects

Alignment is no longer a one-time step, because every model update and every new language market needs fresh evaluation. Obligations for general-purpose AI providers entered into application on Aug. 2, 2025, according to European Commission guidance for model providers, which pushes providers toward documented testing. Cohere for AI drew about 3,000 researchers in 119 countries to annotate roughly 204,000 prompt and completion pairs for its Aya project, according to Axios in February 2024. Invisible Technologies said in September 2025 that it had trained models for more than 80% of leading AI model providers, including Cohere, Microsoft and AWS. In the Credence Research model this driver adds about 3.2 percentage points to annual growth to 2035.

Government and defense programs widen the buyer base

Public agencies are becoming buyers of model evaluation and tuning work as they move AI into planning and operations. The Defense Innovation Unit awarded Scale AI the Thunderforge prototype contract on March 5, 2025, with Anduril and Microsoft as partners and first use at Indo-Pacific Command and European Command. India's Cabinet approved the IndiaAI Mission with an outlay of Rs 10,371.92 crore in March 2024, as India TV reported, which funds national datasets and compute. Saudi Arabia launched HUMAIN under the Public Investment Fund on May 12, 2025, with Arabic large language models as a stated aim, according to Arab News. These programs need experts who can grade outputs in local languages and secure settings.

Credentialed experts and verifiable rewards reshape work

Shift from generalist crowds to credentialed experts

Buyers are shifting budgets from large generalist crowds to smaller pools of credentialed specialists with verified skills. xAI laid off about 500 members of its roughly 1,500-person annotation team on Sept. 12, 2025 and said it would expand its specialist AI tutor team tenfold, according to TechCrunch. Scale AI cut about 200 full-time staff and 500 contractors in July 2025, saying it had ramped up generative AI capacity too quickly, as TechRepublic reported. Mercor then raised $350 million at a $10 billion valuation on Oct. 27, 2025 with a network of more than 30,000 experts, per TechCrunch coverage. As a result, Preference Data still holds 36.0%, but expert work takes a larger slice of each contract.

Rise of verifiable rewards and graded task environments

A rising share of post-training now uses verifiable rewards, which changes what labs buy from human data vendors each year. DeepSeek's R1 paper, published in Nature on Sept. 17, 2025, described reasoning training that gave a reward of one for a correct math or code answer and zero otherwise, according to Science News. As a result, vendors now sell test suites, graded tasks and simulated work environments rather than only pairwise rankings, alongside synthetic training corpora. Turing said in March 2025 that its network spans more than four million software engineers, data scientists and STEM experts. This shift favors vendors that can write, verify and maintain large libraries of graded tasks for each new model release.

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Computer-use agents open a large trajectory data opening

Computer-use agents give vendors a large opening, because labs need recorded human demonstrations of multi-step tasks. OpenAI's Operator, launched Jan. 23, 2025, scored 38.1% on OSWorld and 58.1% on WebArena, according to The Register, which leaves wide room for better training data. xAI said in September 2025 that it was hiring specialist tutors across STEM, finance, medicine and safety, which signals demand for skilled task recorders. Agent Trajectories grow at 19.96% a year in the Credence Research model, the fastest rate among services. Vendors that can supply secure desktops, logged tool calls and expert review stand to win the largest agent programs.

Sovereign and multilingual model programs open new demand

Sovereign and multilingual model programs open demand outside the United States for native-speaker raters and evaluators. Latam-GPT gathered more than eight terabytes of data with nearly 200 specialists across eight countries, coordinated by Chile's CENIA, according to bne IntelliNews. Stanford HAI data show 2024 private AI investment of $9.29 billion in China, $4.52 billion in the United Kingdom and $1.16 billion in India, which funds local model teams. Asia Pacific is the fastest-growing region in the Credence Research model. Vendors with raters in Arabic, Hindi, Spanish and Portuguese can win evaluation work that U.S.-centric crowds cannot staff.

Labor rules, client concentration and expert scarcity weigh

Contractor classification lawsuits raise labor costs

Worker classification is a cost risk for vendors that rely on large contractor pools. Scale AI faced a wage suit filed Dec. 10, 2024 and a second suit on Jan. 3, 2025, in which an Outlier worker said her pay of about $15 an hour fell below California's $16.00 minimum wage, according to TechCrunch. As a result, vendors face higher compliance, payroll and legal costs in key U.S. states. Many vendors now move toward vetted experts on clearer contracts.

Client concentration and neutrality concerns

Revenue concentration among a few frontier labs exposes vendors to sudden loss of work. After Meta took its stake in Scale AI in June 2025, OpenAI and Google were reported to be ending their work with the vendor, according to TechRepublic in July 2025. This shows that a single ownership change can move large programs within weeks. Vendors now stress neutrality, separate data rooms and multi-client governance to keep lab trust.

Scarce senior experts limit project scale

Senior professionals are scarce, which caps how fast vendors can staff high-value projects in medicine, law and finance. OpenAI's GDPval tasks were written by professionals with about 14 years of experience on average, a profile that few contract networks can supply at volume. This raises hourly rates and lengthens project start times for buyers. Vendors respond with referral programs, university partnerships and tiered review that saves senior time for final checks.

EU enforcement deadlines add documentation costs

EU rules add documentation work that buyers expect vendors to support with audit-ready records. The European Commission's enforcement powers, including fines, apply from Aug. 2, 2026, and models placed on the market before Aug. 2, 2025 must comply by Aug. 2, 2027. As a result, providers need traceable records of how human feedback was gathered and checked. Vendors face added costs for consent records, rater metadata and data provenance logs.

North America leads as Asia Pacific grows fastest

North America leads on frontier lab spending

North America held 45.0% of 2025 value and grows at 17.46% a year to 2035. The region leads because U.S. institutions produced 40 notable models in 2024, according to Stanford HAI, and most frontier post-training budgets sit with these developers. Defense demand adds a second buyer group after the Thunderforge award in March 2025. As a result, the region remains the main market for preference data, coding evaluations and agent trajectory work.

Europe grows on compliance testing and model builders

Europe held 25.0% of 2025 value and grows at 17.56% a year to 2035. Private AI investment reached $4.52 billion in the United Kingdom, $2.62 billion in France and $1.97 billion in Germany in 2024, according to Stanford HAI. In turn, EU duties for general-purpose AI providers that took effect in August 2025 raise demand for documented evaluation and red-team testing. European buyers also need raters in many official languages, which favors multilingual vendors.

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Asia Pacific grows fastest on national model programs

Asia Pacific held 24.0% of 2025 value and is the fastest-growing region at 19.16% a year to 2035. China drew $9.29 billion and India $1.16 billion of private AI investment in 2024, according to Stanford HAI. India's IndiaAI Mission, approved in March 2024 with Rs 10,371.92 crore, funds datasets and compute for local models. Malaysia's Khazanah Nasional led Turing's Series E in March 2025, which links regional capital to the expert supply chain.

Latin America builds a regional model base

Latin America held 3.0% of 2025 value and grows at 18.76% a year to 2035. Chile's CENIA presented Latam-GPT, a 70 billion parameter model, on Feb. 10, 2026, with more than 60 institutions taking part, according to bne IntelliNews. This creates demand for Spanish and Portuguese evaluators who understand regional usage and law. The region also supplies remote expert talent to U.S. vendors because time zones match North American working hours.

Middle East & Africa gains from Arabic model programs

Middle East & Africa held 3.0% of 2025 value and grows at 18.96% a year to 2035. HUMAIN, chaired by Saudi Arabia's Crown Prince, plans AI adoption in energy, healthcare, manufacturing and financial services, according to Arab News in May 2025. These sectors need Arabic-speaking experts who can grade model answers in regulated settings. Africa adds a smaller but growing share through remote contributor pools that serve global vendors.

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Funding rounds and lab benchmarks marked 2025 activity

Rubrics, reward checks and agent recordings define methods

• Pairwise preference ranking asks raters to compare two or more model answers and pick the better one, which feeds reward models used in RLHF.
• Rubric-based grading gives experts a written checklist for each prompt, as in HealthBench, so scores stay consistent across many reviewers.
• Verifiable reward environments pair tasks with automatic checks such as unit tests or exact answers, with experts writing and auditing the tasks.
• Computer-use trajectories record each click, keystroke and tool call that a skilled human makes while finishing a task in real software.

Expert networks compete on depth, speed and neutrality

Competition in RLHF data services centers on the depth of expert networks, speed of delivery and trust with frontier labs. Scale AI combines large managed programs with public sector work such as the Thunderforge contract, while Surge AI focuses on high-quality preference data for a few large labs. Mercor and Handshake AI recruit credentialed professionals and graduate researchers, and Turing draws on its software engineering network for coding and reasoning tasks. Invisible Technologies pairs expert work with an enterprise software platform. Neutrality has become a selling point since the Meta investment in Scale AI, and labs now split work across several vendors. Vendors also differ in tooling, from in-house rating interfaces to general annotation tools licensed from software providers. Price competition is weaker than in classic labeling because expert quality decides model results.

Companies profiled and their roles in this market

Expert quality will decide vendor share to 2035

Credence Research's view is that expert quality, not crowd size, will decide vendor share through 2035. Labs are moving work away from generalist pools, as the cuts at xAI and Scale AI in 2025 showed, and toward smaller teams of credentialed specialists. Classic image and text labeling remains a separate, more price-driven field than RLHF data services. Vendors that can prove rater skill, keep client data separate and build graded task environments for agents are best placed to grow faster than the market. Enterprise buyers will add a second demand base as they test models on their own workflows.

Model-based sizing checked against public vendor activity

The report uses 2025 as the base year and forecasts values from 2026 to 2035 in U.S. dollars. Market values come from the Credence Research model, and the regional, service, domain and buyer splits are Credence Research estimates. Inputs include public company announcements, funding releases, government and regulator publications, published benchmark papers and the Stanford HAI AI Index. The 2025 value was checked against public vendor activity and lab spending signals. No interviews or surveys were conducted for this edition of the report.

Agent and enterprise work extend demand through 2035

The RLHF data services market is expected to grow from USD 4.80 billion in 2026 to USD 21.29 billion by 2035 as human judgment stays central to model training and testing. Agent trajectory work should gain share as computer-use products mature and labs need longer, logged demonstrations. Enterprises will buy more domain evaluation as they move models into banking, law and health care. Verifiable rewards will reduce some routine ranking work, but they will raise demand for experts who write and audit graded tasks. Regional programs in Asia Pacific, Latin America and the Middle East will add multilingual demand.

Note: The depth of individual sections depends on data availability for this market. Volume, capacity, trade and other unit-based metrics are included where applicable.