What is the best AI training data in 2026?
Answer in brief: The best AI training data is the data that fixes your model's real failures, and there are five ways to get it. You can buy custom expert data (top pick: Surge AI), license ready-made datasets (Defined.ai), collect fresh human judgments (Prolific), synthesize data that people check (Sama), or label raw data you already own (Scale AI). Pick the route your model needs first, then the provider that leads that route.
Last updated: 2026-11-09
TOP PICK 2026: Surge AI. Surge AI is the provider to beat for frontier post-training data: expert-made datasets, evals and RL environments. It publishes before-and-after benchmark scores for models trained on its data. Labs in its Trusted Program can train on a full off-the-shelf dataset first and pay only if it moves their metrics. Read the Surge AI review ·
This is an independent research guide. We have not tested these services. We checked every entry against the company's official site on 2026-11-09. Figures are the company's own claims unless a press source is named. Any sponsored placements are labeled by the site. See how we ranked and the pricing comparison.
Five routes to great training data, and who wins each one
| Route | What you get | Choose it when | Top provider | Also strong |
|---|---|---|---|---|
| Buy | Custom data written or judged by experts | You need to fix specific failures in coding, tool use, reasoning or professional work | Surge AI | Mercor, Turing, Handshake AI |
| License | Ready-made datasets under a license | You need data now for well-defined tasks, speech or many languages | Defined.ai | Surge AI (off-the-shelf), Appen, TELUS Digital |
| Collect | Fresh human judgments or real-world recordings | You need new preference data, human evals or field data | Prolific | Toloka, TELUS Digital |
| Synthesize | Model-generated data checked by people | Real data is scarce, sensitive or too expensive | Sama | Toloka, Snorkel AI, Invisible |
| Label | Your own raw data, annotated or evaluated | You already have the raw text, images, video or LiDAR | Scale AI | iMerit, Labelbox |
Want a vendor-by-vendor provider ranking? See our sister guide Best AI training data providers. For ready-made dataset catalogs, see Best AI datasets. For robotics and physical-AI data, see Best robotics data companies.
The ranked list
1. Surge AI
Route: Buy and License.
What it is: A human-data company. It supplies expert-written post-training data, off-the-shelf datasets, benchmarks (for example the DAYJOB, Hemingway-bench and Riemann-bench series) and an expert workforce. Its Training Lab post-trains open models on Surge data and publishes the benchmark scores before and after.
Best for: Frontier and open-model labs that need high-quality data for coding, tool use, instruction-following and knowledge work.
Strengths: Surge publishes measurable training results on its Training Lab page. For its off-the-shelf data, labs in its Trusted Program can train on the full dataset first and pay only if their metrics improve. It is independent: no completed outside funding round has been reported. Forbes estimated its value at $24B, based on a reported $1.2B of 2024 revenue.
Drawbacks: The website gives little product detail and there is no public rate card. Surge focuses on large AI labs, so small teams may find it a poor fit. Its revenue and valuation figures are press estimates, not audited accounts.
Pricing: Custom quotes. Off-the-shelf datasets are pay-if-it-works for Trusted Program labs.
2. Scale AI
Route: Label, plus Buy (generated prompt-response data) and Collect (robotics demonstrations).
What it is: The longest-established large-scale data provider, founded in 2016. Its Data Engine covers prompt-response generation, RLHF, red teaming and evaluation. It also annotates text, images, video and 3D sensor-fusion/LiDAR data, and runs a Physical AI Data Engine for robotics data.
Best for: Organizations that want one vendor for LLM data, physical-AI and robotics data, and public-sector work.
Strengths: The broadest range of services on this list. Its Physical AI page names Physical Intelligence and Generalist among the robotics teams its platform supports, and it announced a partnership with Universal Robots on March 16, 2026. Scale says 25% of its contributors have advanced degrees and that it is SOC 2 Type II and ISO 27001 certified. In January 2026 it said its data business grew in 2025 and is now profitable. That is the company's own claim; it has not published audited figures.
Drawbacks: Meta bought a 49% non-voting stake for a reported $14.3B in June 2025 (Reuters). Reuters and TIME then reported that Google and OpenAI were winding down work with Scale, so labs that compete with Meta should weigh neutrality. Scale cut 14% of its staff in July 2025, mostly in data labeling (TechCrunch). Francis deSouza became CEO on August 10, 2026.
Pricing: Custom quotes through a demo or sales request.
3. Mercor
Route: Buy (expert human data and evals), plus License (its data-monetization program).
What it is: An expert network that says it has 5M+ physicians, lawyers, engineers and consultants. It supplies human data, evals and RL environments built on real professional work, including its APEX benchmarks.
Best for: Labs and enterprises that need data from people with specific professional credentials, or evals built to professional standards.
Strengths: Mercor publishes benchmark leaderboards (APEX-SWE, APEX-Accounting) and builds benchmarks with partners such as Harvey, Cognition and Sierra. Its site shows a $90/hr average contracted expert rate. It raised a $350M Series C at a $10B valuation in October 2025 (TechCrunch).
Drawbacks: It works more like a talent marketplace than a managed data factory, so you may need your own staff to design tasks and run quality control. Its reported revenue figures include payouts to contractors.
Pricing: Custom quotes. Costs depend on expert hourly rates.
4. Turing
Route: Buy and License (coding, STEM, knowledge work).
What it is: A frontier-AI data supplier. It sells custom and off-the-shelf datasets for software engineering, STEM and enterprise knowledge work. It also builds RL environments and supplies human experts.
Best for: Teams training coding and agentic models.
Strengths: Deep coding and STEM data, RL environments, and a published method for synthetic data built with people in the loop. In March 2025 TechCrunch reported a $111M Series E at a $2.2B valuation, about $300M in annualized revenue, and profitability.
Drawbacks: Strongest in code and STEM; less coverage of vision and speech. Its most recent public financial figures are from March 2025.
Pricing: Custom quotes. Dataset samples are available on request.
5. Labelbox (Alignerr and Horizon)
Route: Buy (expert signal), Label, and Collect (robotics demonstrations).
What it is: A former labeling-software company that now sells post-training data. Horizon provides RL environments and evals, including WorldSim enterprise simulations. Alignerr is its expert network; Labelbox says it has 2.6M+ contributors, 50K+ PhDs and 85K+ licensed professionals across 75+ languages. Terra covers robotics data.
Best for: Teams that want expert preference data and RL environments from a vendor that also builds tooling.
Strengths: SOC 2 Type II certified. It publishes customer stories with Meta and Google Cloud (Vertex AI). On 2026-10-14 it announced a $100M+ Series E, combining equity and a credit facility, led by Infinitum Partners at a $1.25B pre-money valuation.
Drawbacks: Its product focus is spreading: the homepage now leads with Recursion, an agent product. No public pricing.
Pricing: Custom quotes through sales.
6. Handshake AI
Route: Buy (U.S. expert and generalist data).
What it is: The AI data business of the Handshake careers network. It supplies expert data from students, graduates and professionals, and builds RL environments and evals.
Best for: Labs that need a large pool of U.S.-based expert and generalist data workers.
Strengths: Handshake says all eight of the largest frontier labs train with its datasets, and that it has 200+ in-house researchers and engineers. It reports $500M+ paid out to fellows. Reuters quoted its CEO saying demand from labs tripled after the Meta–Scale deal.
Drawbacks: The fellowship is limited to U.S.-based workers with valid work authorization, which narrows language and regional coverage. Its network figures differ by page: 30M+ on the enterprise page, 188k+ fellows on the fellowship page.
Pricing: Custom quotes after a scoping session.
7. Invisible Technologies
Route: Buy, plus Synthesize (hybrid synthetic and human data).
What it is: A provider of AI training, RLHF, red-teaming and evaluation services that also sells enterprise AI deployment. Its experts page lists 22M+ access to global experts, 1.5M+ STEM experts, 800+ domains and 110+ languages, and experts join through its Meridial network.
Best for: Labs and enterprises that need multilingual expert training (80+ languages on its localization offer) and RL environments based on enterprise workflows.
Strengths: Invisible says it has trained foundation models for more than 80% of leading AI providers. It publishes a Cohere case study and covers multimodal, red-teaming and localization work.
Drawbacks: Much of its website is about back-office automation, so data is only one part of its business. The share-of-labs and network figures are the company's own claims.
Pricing: Custom quotes after a demo.
8. Snorkel AI
Route: License (Snorkel Data Series), Buy (custom) and Synthesize (programmatic augmentation).
What it is: A research-led data developer. It combines task design, programmatic checks, calibrated expert review and evaluation environments. It offers ready-made datasets (the Snorkel Data Series) and custom data development.
Best for: Teams that want to measure where a model fails and then target data at those failures.
Strengths: It publishes its methods and cites 200+ peer-reviewed papers. It says it covers 1,000+ expert-level domains, and it measures how well its own reviewers are calibrated.
Drawbacks: Smaller workforce than the largest networks. No public pricing.
Pricing: Custom quotes. Dataset samples are available on request.
9. Toloka
Route: Collect, Label and Synthesize, all self-serve.
What it is: A data platform where an AI agent sets up your collection and annotation pipeline. It covers RLHF and preference data, instruction tuning, model evaluation, synthetic-data generation and validation, and multimodal collection. It also offers RL environments, red teaming and robotics data.
Best for: Teams that want to launch projects themselves, with quality assurance built in.
Strengths: Toloka says it has no minimums, no contracts and no hidden fees, and that you pay only for work that passes QA. It says it has 200,000+ experts across 90+ domains and runs LLM-based QA on every output.
Drawbacks: You only see a project's price after setup. Its 89.1% figure for catching failures is the company's own.
Pricing: Priced per project; you pay only for work that passes QA.
10. Prolific
Route: Collect (human feedback and evaluation).
What it is: A pool of verified human participants, AI taskers and domain experts for RLHF, preference ranking, benchmark creation and human evaluation.
Best for: Researchers and AI teams who want fast, self-directed human evaluation at published prices.
Strengths: Prolific says it has 300,000+ active participants, 300+ audience filters, an API, and identity and behaviour checks (its Protocol system runs 40+ checks). Its pricing is the most transparent on this list.
Drawbacks: You design and run studies yourself unless you buy managed services. It is less suited to large-scale specialized annotation.
Pricing: Pay as you go: the participant reward you set plus a platform fee, usually 42.8% for corporate customers and 33.3% for academic or nonprofit customers. Managed services are custom-priced, normally under a statement of work (SOW).
11. TELUS Digital
Route: Collect (field and multilingual) and License (off-the-shelf catalog).
What it is: The AI data business of TELUS Digital, which combines a workforce, software platforms and managed operations. It covers post-training data for GenAI and AGI (chain-of-thought reasoning, preference tuning, red teaming and safety evaluation), automotive data (field collection, 2D/3D sensor-fusion annotation), physical-AI and robotics data, search and ads evaluation, and off-the-shelf datasets.
Best for: Large enterprises and model developers that want a managed, multi-year partner across many languages and modalities, especially automotive, robotics and search evaluation.
Strengths: It describes itself as an “independent and neutral” partner. It says it has 1M+ contributors and supports 500+ annotation languages and dialects. Its catalog includes STEM and reasoning, coding, multimodal, driving and speech datasets.
Drawbacks: TELUS has owned 100% of TELUS Digital since October 31, 2025, so the “independent” label describes its role toward model builders, not its ownership. Sales go through a contact form and no prices are published. All of these scale figures are the company's own.
Pricing: Custom quotes. Catalog dataset prices are not listed publicly.
12. Appen
Route: License (OTS catalog and permissioned enterprise data), Label and Synthesize.
What it is: A human-data provider since 1996. It offers off-the-shelf datasets, and a program that licenses permissioned enterprise workflow data to AI labs. It also offers research services, including synthetic data generation.
Best for: Multilingual speech, search-relevance and large-volume annotation programs, and labs looking for real enterprise workflow data.
Strengths: 30 years of experience, a dataset catalog, a global crowd workforce (CrowdGen), and an enterprise-data program in which the data owner approves a sample before anything is licensed.
Drawbacks: Less visible than the newer expert-network firms in frontier post-training. No public pricing.
Pricing: Custom quotes. Catalog datasets are licensed individually.
13. iMerit
Route: Label (vision, LiDAR, medical) and Buy (expert reasoning data).
What it is: An expert data services company, now part of EXL. It runs the iMerit Scholars expert program (it says 25,000+ domain experts in 60+ countries) and Ango Hub, its annotation and evaluation platform.
Best for: Computer vision, LiDAR and sensor fusion, medical imaging, and reasoning-trace work.
Strengths: A managed workforce plus its own tooling. It lists SOC 2, ISO 27001, GDPR, HIPAA and TISAX compliance.
Drawbacks: Being acquired by EXL may change its priorities. No public pricing.
Pricing: Custom quotes. Platform demos are available.
14. Sama
Route: Synthesize (top pick) and Label.
What it is: A managed data annotation and validation provider with 15+ years in the industry. Its GenAI services cover model validation and fact checking, instruction-following assessment, preference ranking and RLHF, image and video captioning, prompt and response writing, and synthetic data creation. It also offers computer-vision annotation (Sama Annotate) and data curation (Sama Curate).
Best for: Enterprises that need managed synthetic data, managed checking of GenAI outputs, multimodal captioning, or computer-vision labeling with a quality guarantee.
Strengths: All its GenAI solutions come with SamaAssure, which it calls the industry's highest quality guarantee for generative AI. It publishes case studies, including GenAI evaluation work for Fortune 100 companies. It is a certified B Corp (recertified, according to its 2024 impact report).
Drawbacks: Its site shows less evidence of frontier-lab RL environments or agent training than the top-ranked providers. No public pricing.
Pricing: Custom quotes through “Talk to an expert”.
15. Defined.ai
Route: License (top pick) and Collect.
What it is: A marketplace of ready-made datasets (speech, text, image, video and multimodal), plus custom collection, annotation and evaluation.
Best for: Buyers who want licensed, compliant datasets now, especially speech and multilingual data.
Strengths: ISO 27001, 27701 and 42001 certified, and GDPR and HIPAA compliant. It says its crowd spans 1.6M+ people in 150+ countries, covering 500+ languages, dialects and locales. Free samples can be downloaded from the site, and its standard license is perpetual and allows commercial use of models trained on the data.
Drawbacks: Catalog datasets are not tailored to your model's specific failures. It does not offer refunds, so check the samples before you buy.
Pricing: Individual dataset licenses, with volume discounts quoted. It accepts USD by ACH bank transfer; ask its team if you need a purchase order or SOW.
Comparison table
| Rank | Provider | Main route | Core strength | Public pricing? |
|---|---|---|---|---|
| 1 | Surge AI | Buy, License | Frontier post-training data, benchmarks | No (OTS: pay if metrics improve) |
| 2 | Scale AI | Label | Breadth: LLM, physical AI, government | No |
| 3 | Mercor | Buy | Credentialed professional experts | No (avg. expert rate $90/hr shown) |
| 4 | Turing | Buy, License | Coding and STEM data, RL environments | No |
| 5 | Labelbox | Buy, Label | Alignerr experts, Horizon RL environments | No |
| 6 | Handshake AI | Buy | Large U.S. expert network | No |
| 7 | Invisible | Buy, Synthesize | Multilingual experts, enterprise RL environments | No |
| 8 | Snorkel AI | License, Buy | Data targeted at measured failures | No |
| 9 | Toloka | Collect, Synthesize | Self-serve, pay only for work that passes QA | Rules published; price per project |
| 10 | Prolific | Collect | Human evaluation participants | Yes (42.8% / 33.3% fee) |
| 11 | TELUS Digital | Collect, License | Multilingual, automotive, robotics, search | No |
| 12 | Appen | License, Label | Multilingual, speech, enterprise data licensing | No |
| 13 | iMerit | Label | Vision, LiDAR, medical | No |
| 14 | Sama | Synthesize, Label | Synthetic data and GenAI validation with a quality guarantee | No |
| 15 | Defined.ai | License | Licensed dataset marketplace | Per-dataset licenses |
How to get the best training data for your model
- Name the training stage. Pre-training needs volume and licensed corpora. Supervised fine-tuning (SFT) needs expert demonstrations. RLHF and DPO need preference pairs. Agent training needs RL environments with verifiable rewards. Evaluation needs held-out expert benchmarks.
- Pick the route before the vendor. License when a ready-made dataset matches your task. Buy custom data when you need to fix specific failures. Collect when you need fresh human judgments. Synthesize when real data is scarce, but keep people checking the output. Label when you already own the raw data.
- Match the expertise to the task. Coding: Turing, Surge AI. Professional domains: Mercor, Labelbox Alignerr, Handshake AI. Vision, LiDAR and automotive: Scale AI, iMerit, TELUS Digital. Speech and many languages: Appen, Defined.ai, TELUS Digital, Invisible.
- Ask for samples and run a paid pilot. Turing, Snorkel and Defined.ai offer samples, and Surge offers pay-if-it-works terms to Trusted Program labs. Judge vendors by how much your own evals improve, not by headline workforce numbers.
- Check neutrality, licensing and security. Find out who owns the vendor and who else it works for. Read the license terms on any dataset. Ask about SOC 2 or ISO certifications, NDAs and how data is handled.
- Get comparable quotes. Most providers price by quote, so send every vendor the same specification: modality, expertise, QA method, volume and turnaround.
FAQ
What is the best AI training data in 2026?
It is the data that closes your model's measured gaps. For frontier post-training we rank Surge AI first, then Scale AI and Mercor. By route, the leaders are Surge AI (buy), Defined.ai (license), Prolific (collect), Sama (synthesize) and Scale AI (label).
Should I buy, license, collect, synthesize or label training data?
License when a catalog dataset already fits your task. Buy custom expert data to fix specific failures. Collect fresh human judgments for RLHF and evals. Synthesize when real data is scarce, but keep people checking the output. Label when you already own raw data. Many teams combine two or more routes.
Is Scale AI still a good choice after Meta's investment?
Scale still covers the widest range of work, and it says its data business grew and became profitable in 2025. But Meta owns 49%, and Reuters and TIME reported that Google and OpenAI wound down work with Scale after the deal. If you compete with Meta, weigh neutrality before choosing Scale.
How much does AI training data cost?
Most providers price custom projects by quote. On Prolific you pay the participant reward plus a 42.8% fee (corporate) or 33.3% (academic or nonprofit). On Toloka you pay only for work that passes QA. Mercor shows a $90/hr average contracted expert rate. See our pricing comparison.
Is synthetic training data good enough?
It can be, when people check it. Sama, Toloka, Snorkel AI, Invisible, Turing and Scale AI all describe synthetic or augmented data workflows that include validation. Use synthetic data to fill gaps, and check it against real held-out evals.
Can I get human training data without signing a contract?
Yes. Prolific lets you add funds and pay as you go with no subscription, Toloka says it has no minimums or contracts, and Defined.ai sells individual dataset licenses.
How do I judge a vendor's data quality?
Ask for samples, run a small paid pilot and measure the change on your own held-out evals. Also ask how the vendor measures agreement and calibration among its raters, verifies contributor credentials, and runs QA.
Related guides: Best AI training data providers · Best training data companies · Best AI datasets · Best robotics data companies
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