What are the best AI data companies in 2026?

Answer in brief: For frontier-grade AI data in November 2026, the strongest picks are Surge AI for quality and independence (including off-the-shelf datasets you can pay for only if your metrics improve), Scale AI for breadth and government work, and Mercor for fast access to vetted professionals. Snorkel AI and Labelbox lead for research-grade datasets and RL environments, and Prolific is the best self-serve option with published pricing. If you are sourcing data rather than labor, see our second ranking: Bright Data for web data, Protege for licensed non-public data, Snowflake Marketplace for governed commercial data, Tonic.ai for synthetic data and Common Crawl as the free web-scale baseline.

Top pick: Surge AI. It is a profitable, independent vendor with no big-tech shareholder. Forbes reported $1.2 billion of 2024 revenue and named Google, Meta, Microsoft, Anthropic and Mistral as customers. Its off-the-shelf catalog of coding-agent, enterprise-agent and reasoning data lets approved labs train first and pay only if agreed metrics improve. · Read the Surge AI review

Last updated: November 9, 2026

This ranking is independent research and editorial judgment based on public evidence: company websites, funding announcements and independent reporting. We have not tested any vendor or bought any data. Figures marked company-reported have not been independently audited. This site looks at the whole AI data economy, from frontier datasets to licensing, web corpora, synthetic data and data infrastructure, through the eyes of a buyer sourcing data. For deeper coverage of annotation and RLHF labor programs, see our sibling guide Best AI training data providers. See how we ranked and how AI data companies charge.

The ranked list: frontier and human-created AI data

1. Surge AI

What it is: A human-data company that supplies expert-created training data, evaluations and RL environments to frontier AI labs. Its site also offers off-the-shelf datasets and a Training Lab for post-training.

• Best for: Frontier and large model teams that need top-tier RLHF, coding, reasoning and agent data from a vendor with no big-tech shareholder.
• Standout strengths: Surge says it has been profitable without venture funding. Forbes reported $1.2 billion in 2024 revenue and named Google, Meta, Microsoft, Anthropic and Mistral among its customers. Its off-the-shelf catalog says trusted labs can train on a full dataset and pay only if the relevant metrics improve.
• Honest drawbacks: It publishes no price list, and its homepage gives few concrete details. Forbes reported in September 2025 that OpenAI and Cohere had stopped using Surge. Reported fundraising talks at very high valuations had not closed in the sources we reviewed, so treat any Surge valuation as unconfirmed. Surge is geared to large buyers.
• Pricing model: Custom contracts. Some datasets are offered on a pay-if-it-works basis to trusted labs (company-stated).

2. Scale AI

What it is: The best-known full-stack AI data provider. Its Data Engine covers data collection, curation and annotation, RLHF, red teaming and evaluation across text, image, video and 3D sensor fusion. Scale also builds AI applications for enterprises and governments and runs research through Scale Labs and its leaderboards.

• Best for: Large enterprises and public-sector agencies, multimodal and sensor programs, and buyers who want data and deployment from one vendor.
• Standout strengths: It has the broadest modality coverage of any company here. Scale says 25% of its contributors hold advanced degrees. In May 2026 the Pentagon's CDAO raised the ceiling of Scale's production agreement from $100 million to $500 million; that is a ceiling, not money already spent. Scale launched Scale Labs on March 9, 2026.
• Honest drawbacks: Meta bought a 49% stake for $14.3 billion in June 2025. TIME and TechCrunch reported that OpenAI and Google wound down work with Scale afterwards, so neutrality is a real procurement question (Scale says its customer-data protections did not change). Francis deSouza became CEO on August 10, 2026, a hire Axios read as a stronger push into enterprise AI deployment. Scale cut about 200 staff, largely in data labeling, in July 2025.
• Pricing model: Custom enterprise and government contracts, sold through a demo.

3. Mercor

What it is: An expert network and human-data company. It recruits physicians, lawyers, engineers, consultants and other professionals to build training data, evaluations and RL environments for AI labs and enterprises. It also publishes the APEX benchmarks.

• Best for: Teams that need credentialed experts in law, finance, medicine, engineering or software, quickly, for rubrics, evaluations and post-training data.
• Standout strengths: Mercor's site cites 5M+ experts, a $90/hour average contracted rate and $4M+ in daily payouts. In October 2025 it raised a $350 million Series C led by Felicis Ventures at a $10 billion valuation (CNBC, TechCrunch). Mercor builds public benchmarks with partners including Harvey, Cognition and Sierra.
• Honest drawbacks: Buyer pricing is not published. Expert-level rates make high-volume, simple labeling expensive. Its scope now spans recruiting, human data, data monetization and enterprise AI deployment.
• Pricing model: Custom. Expert pay rates are visible on its public role listings.

4. Snorkel AI

What it is: A self-described frontier AI data lab spun out of the Stanford AI Lab. It builds expert datasets, benchmarks, evaluations and agentic environments. Its expert Data-as-a-Service offering launched in September 2025.

• Best for: Labs and enterprises that need hard, research-grade data where correctness is difficult to define, including specialized agents for finance, legal, insurance and software engineering.
• Standout strengths: Snorkel announced a $350 million Series E at a $3.5 billion valuation on September 22, 2026, and says it crossed $375 million in annualized revenue run rate. It cites 250+ peer-reviewed papers, runs public leaderboards such as Terminal-Bench and SnorkelFinance, and funds Open Benchmarks Grants.
• Honest drawbacks: The site now leads with data services, so buyers who want only labeling software should confirm what tooling is still offered. Pricing is not public, and the revenue figures are company-reported.
• Pricing model: Custom engagements. You can request dataset samples.

5. Labelbox

What it is: Started as annotation software and now sells several lines: Horizon (RL environments and evaluations for reasoning, tool use and computer use), Alignerr (expert network), Terra (robotics data) and Recursion (managed agents for enterprises).

• Best for: Teams that want a platform plus managed expert data, RL environments for agents, or robotics demonstration data.
• Standout strengths: On October 14, 2026 Labelbox announced more than $100 million in Series E financing (equity plus a credit facility) at a $1.25 billion pre-money valuation, led by Infinitum Partners. It reports 400+ customers, work with 90%+ of leading US AI labs, and profitability in 2026 with more than 5x year-over-year growth (all company-reported).
• Honest drawbacks: Its pages give different network sizes (2.6M+ skill-tested experts for Alignerr; more than 4 million people overall). The new enterprise agents product widens its focus. The Series E includes debt, with no equity/credit split disclosed, and the pricing URL we tried returned a 404 on November 9, 2026.
• Pricing model: Custom. We found no public pricing page.

6. Turing

What it is: A company that supplies proprietary human data for SFT, RLHF and DPO, evaluation data, RL environments and off-the-shelf datasets, backed by a large network of engineers and domain experts. It also offers engineering services and runs a Frontier Research Lab.

• Best for: Coding, agentic-coding and STEM post-training data.
• Standout strengths: Turing raised a $111 million Series E at a $2.2 billion valuation, bringing total funding to $225 million. It says it has served 1,000+ clients. In July 2026 its research lab released 300 verifier-graded terminal tasks and 201 agentic STEM tasks.
• Honest drawbacks: Revenue figures are run rates or third-party estimates: TechCrunch reported about $300 million ARR in March 2025, and Sacra estimates $350 million annualized at the end of 2025. Turing's own pages do not name lab customers. It mixes talent services with data work. Its site blocked our automated reader, so these details come from secondary retrieval and dated reporting.
• Pricing model: Custom.

7. Handshake AI

What it is: The AI-data arm of the Handshake careers network. It pays students, graduates, PhDs and professionals through the Handshake AI Fellowship to build and grade training data, and sells enterprise AI services. It acquired the data-quality company Cleanlab in January 2026.

• Best for: Labs that need large US-based pools of graduate-level and early-career experts across many academic fields.
• Standout strengths: Handshake says the 8 largest frontier labs all train with its datasets. It cites a 30M+ expert network, 200+ in-house researchers and engineers, $500M+ in payouts and 188k+ fellows (company-reported).
• Honest drawbacks: Fellows must be based in the US with supported work authorization, which limits geographic and language coverage. Advertised roles range from up to $17/hour (generalist evaluation) to up to $150/hour (software engineering), so skill tiers vary. No reliable funding or valuation figure was public in our sources.
• Pricing model: Custom, starting with a scoping session.

8. Invisible Technologies

What it is: An AI training and enterprise AI platform. Its modules are Neuron (data platform), Atomic (process builder), Meridial (expert network for RLHF and validation), Synapse (evaluation) and Axon (agents).

• Best for: Organizations that want RLHF and evaluation data plus hands-on help putting AI into business operations.
• Standout strengths: Invisible says it has trained over 80% of the world's leading AI models and has 24,000+ vetted experts. It raised $100 million in September 2025 in a round led by Vanara Capital (total $144 million) and publishes a case study with Cohere.
• Honest drawbacks: Its stated expert network is far smaller than Mercor's or Handshake's. It is increasingly focused on enterprise software and consulting-style deployment. Its headline claims are self-reported.
• Pricing model: Custom, sold through a demo.

9. micro1

What it is: A data lab offering expert human data, Realm (RL environments), Cortex (contextual evaluation for agents in production) and real-world robotics data, plus benchmarks such as its Medical Index.

• Best for: Agent evaluation, RL environments and embodied or robotics data.
• Standout strengths: It is a fast-growing challenger. micro1 announced a $35 million Series A at a $500 million valuation in September 2025, led by 01 Advisors. Forbes reported in September 2026 that it raised more than $100 million at a $4 billion valuation.
• Honest drawbacks: The later round rests on Forbes's unnamed sources, and micro1 declined to comment, so treat it as unconfirmed. It is younger, with a shorter public track record, and its homepage gives few specifics.
• Pricing model: Custom.

10. Toloka

What it is: A training-data provider for AI agents and LLMs. It offers RL environments, specialized datasets, evaluation and red-teaming, the Toloka Arena leaderboard, off-the-shelf datasets, and physical-AI and egocentric video data. It also has an expert network (Mindrift) and a self-service platform.

• Best for: Agentic, coding and safety data, multilingual work, and teams that want both managed and self-serve options.
• Standout strengths: Broad catalog spanning agents, coding, safety and robotics. Toloka says it secured $72 million in 2025 in a round led by Bezos Expeditions. Nebius retains a majority economic stake but no longer has voting control.
• Honest drawbacks: Its pricing URL returned a 404 on November 9, 2026. Its legal pages include eligibility and geographic restrictions, so check availability for your region. Homepage testimonials are anonymous.
• Pricing model: A self-service platform (under a published agreement) plus custom enterprise projects. No public price list found.

11. Prolific

What it is: A platform of verified human participants for AI preference data, safety evaluations, benchmarks and academic research. Managed services are also available.

• Best for: Researchers and AI teams that need demographically representative or quota samples quickly, with transparent self-serve pricing.
• Standout strengths: It has the most transparent pricing in this list. Prolific says it has 300,000+ active participants in 40+ countries, 300+ audience filters, API and no-code integrations, and 40+ identity and behavior checks. No contract is needed to start. Its latest disclosed funding is a £25 million (about $32 million) Series A in July 2023, co-led by Partech and Oxford Science Enterprises.
• Honest drawbacks: The platform fee is high: usually 42.8% for corporate customers. Coverage is strongest in the US, UK and Europe. Self-serve means you design the study and the quality checks yourself, and large specialized programs move to custom-quoted managed services.
• Pricing model: The participant reward you set plus a platform fee (usually 42.8% corporate, 33.3% academic or non-profit). Prolific recommends paying at least $12/hour, with a minimum of $8/hour. Managed services are custom.

12. Appen

What it is: A veteran human-data company, founded in 1996. It offers frontier-model alignment data, an off-the-shelf dataset catalog and enterprise data services, through its CrowdGen workforce.

• Best for: Multilingual, speech and search-relevance data, and licensing ready-made datasets.
• Standout strengths: Three decades of work across speech, machine translation, search evaluation and RLHF, plus a browsable dataset catalog. As a listed company it publishes financial reports.
• Honest drawbacks: We found less evidence of 2025–2026 frontier-lab momentum than for the leaders. The homepage offers few current specifics.
• Pricing model: Custom projects and dataset licensing.

13. iMerit

What it is: An expert data and annotation company, now part of EXL. It offers generative-AI evaluation and RLHF through iMerit Scholars, its Ango Hub annotation and evaluation platform, and in-house teams for mobility, healthcare and robotics.

• Best for: Medical imaging (DICOM), LiDAR and sensor fusion for autonomous systems, and regulated enterprise data.
• Standout strengths: Ango Hub handles image, video, LiDAR, DICOM, text, PDF and audio. iMerit cites 25,000+ domain experts in 60+ countries, a dedicated in-house workforce, SOC 2, ISO, HIPAA and TISAX credentials, and EXL's backing.
• Honest drawbacks: Integration after the acquisition may change its roadmap. It has less visibility in frontier-LLM work than the top five.
• Pricing model: Custom.

14. Sama

What it is: A human-verified annotation, data-validation and model-evaluation company for computer vision, multimodal and sensor data, operating since 2008. It is a certified B Corp.

• Best for: Production computer vision in ADAS, retail, agriculture and robotics, and buyers who weigh impact-sourcing practices.
• Standout strengths: Sama says it has a 99% first-batch acceptance rate and that customers stay an average of 8 years. It publishes case studies such as Walmart search relevance and was recertified as a B Corp. It raised a $70 million Series B led by CDPQ in November 2021.
• Honest drawbacks: It is less oriented to expert reasoning data for frontier LLMs. Its latest disclosed financing dates from 2021, and the most recent impact report linked on its site covers 2024. Its quality figures are self-reported.
• Pricing model: Custom, quoted after a scoping call.

Comparison table

Rank	Company	Core strength	Best for	Public pricing?	Latest disclosed funding or ownership
1	Surge AI	Premium RLHF, evals, RL environments, off-the-shelf data	Frontier labs	No	Says no venture funding
2	Scale AI	Broadest data and deployment stack	Enterprise, government, multimodal	No	Meta 49% stake (2025)
3	Mercor	Professional expert network	Domain-expert evals and data	No	$350M Series C at $10B (Oct 2025)
4	Snorkel AI	Research-grade data and benchmarks	Hard, specialized domains	No	$350M at $3.5B (Sep 2026)
5	Labelbox	RL environments plus platform	Agents, robotics, platform buyers	No	$100M+ Series E incl. credit, $1.25B pre-money (Oct 2026)
6	Turing	Coding and STEM post-training	Coding agents	No	$111M at $2.2B; $225M total
7	Handshake AI	Graduate and expert network	US academic-domain experts	No	Not disclosed
8	Invisible Technologies	Data plus enterprise AI ops	RLHF plus deployment	No	$100M (Sep 2025); $144M total
9	micro1	RL environments and agent evals	Agent evaluation, robotics	No	$35M at $500M (Sep 2025); later round reported
10	Toloka	Agent, coding and safety data	Managed or self-serve projects	No public price list	$72M led by Bezos Expeditions (2025)
11	Prolific	Verified representative participants	Preference data, research	Yes	£25M (~$32M) Series A (Jul 2023)
12	Appen	Long-running multilingual crowd	Speech, language, OTS datasets	No	Publicly listed
13	iMerit	Medical and sensor annotation	Regulated and mobility AI	No	Acquired by EXL (2026)
14	Sama	Production computer vision	ADAS, retail, robotics	No	$70M Series B led by CDPQ (Nov 2021)

Beyond labeling: the best companies for sourcing AI data

Most AI data budgets no longer go only to labor. Teams also license data that cannot be scraped, buy ready-made web datasets, generate synthetic data and curate the corpus they already own. These are the strongest companies in that wider AI data economy.

Top pick for sourcing data: Bright Data. A 50PB+ web archive that it says grows by about 2.5PB a day, 190+ ready-made datasets in JSON, CSV or Parquet, real-time feeds for grounding agents, and pricing you can see before you talk to sales: datasets from $250 per 100,000 records. · Read the Bright Data review

1. Bright Data: web data for training, fine-tuning and agents. Datasets from $250 per 100K records; scraper APIs from $0.75 per 1K records.
2. Protege: licensed non-public data such as de-identified health records, video, audio and motion capture. $30M round led by a16z (January 2026), $65M total.
3. Snowflake Marketplace: governed commercial data, apps and agents delivered in your own account. Partners booked $100M+ in H1 2026 (company-reported).
4. Defined.ai: marketplace for consented audio, image, video and text datasets, ISO 42001-certified.
5. DatologyAI: curation of public, proprietary and licensed data into training-ready mixes, inside your VPC.
6. Tonic.ai: synthetic relational and document data, with a free tier and a $29/month plan.
7. Databricks Marketplace: open, cross-cloud sharing of datasets, models and notebooks.
8. Common Crawl: the free web-scale corpus; the October 2026 crawl holds 2.16 billion pages.
9. Diffbot: the web as a knowledge graph, with credit plans from free to $899/month.
10. MOSTLY AI: open-source (Apache 2.0) tabular synthetic data with differential privacy.
11. Hugging Face: the open dataset hub, with versioned repositories and dataset cards.
12. Zyte: extraction API for live web data, from $0.06 per 1,000 successful responses.
13. Apify: ready-made scrapers that AI agents can pay for per run through x402.
14. Oxylabs: managed custom datasets and, since October 2026, a unified Web API.

Rank	Company	Segment	Best for	Public pricing?
1	Bright Data	Web data	Datasets, archive, live feeds	Yes
2	Protege	Licensing	Non-public real-world data	No
3	Snowflake Marketplace	Marketplace	Governed enterprise data	Per listing
4	Defined.ai	Licensing	Speech and multimodal datasets	Per dataset
5	DatologyAI	Infrastructure	Curating your own corpus	No
6	Tonic.ai	Synthetic	Relational and document data	Yes (Fabricate)
7	Databricks Marketplace	Marketplace	Open cross-cloud sharing	Per listing
8	Common Crawl	Web corpus	Free raw web data	Free
9	Diffbot	Web data	Knowledge graph and extraction	Yes
10	MOSTLY AI	Synthetic	Tabular privacy-safe data	SDK free
11	Hugging Face	Infrastructure	Open dataset hub	Free tier
12	Zyte	Web data	Extraction API	Yes
13	Apify	Web data	Scrapers and agents	Yes
14	Oxylabs	Web data	Managed custom datasets	Datasets custom

How to choose an AI data company

1. Name the data type first. Preference and RLHF data, expert demonstrations, RL environments, evaluation sets, vision or sensor annotation, representative participant studies, licensed content, web corpora and synthetic data are different markets. Leaders differ across them.
2. Decide whether you need data or labor. Off-the-shelf and licensed datasets ship faster. Custom expert programs fit unique tasks. For labor-led programs, see our training-data providers guide.
3. Check neutrality and ownership. If you compete with a vendor's major shareholder or largest customer, ask how your data, prompts and model outputs are isolated.
4. Demand a paid pilot or samples with your own gold set. Compare vendors on the same tasks. Measure agreement with your expert answers, rework rates, turnaround and the effect on your own evaluations, not marketing claims.
5. Verify the experts. Ask how credentials are checked, how contributors are tested per domain, and whether you can set qualification bars.
6. Get the data rights in writing. Confirm who owns the outputs, whether the vendor can reuse them, permitted uses (training, fine-tuning, retrieval), exclusivity, and whether other labs can license the same off-the-shelf dataset.
7. Ask for provenance. Licensed, consented, scraped and synthetic data carry different risks. Ask where each record came from and how opt-outs are handled.
8. Review security. Ask about certifications, on-premises or private-cloud options, and contributor access controls if your data is sensitive.
9. Match the pricing model to the work. Self-serve platforms suit studies you design yourself. Managed services suit complex programs. A free corpus is only free until you count filtering, storage and egress. See pricing models.
10. Check geography and language. Some networks are US-only or strongest in English-speaking markets.
11. Ask about contributor pay and welfare. Fair pay and clear guidelines tend to go with more engaged contributors and more reliable data.

Frequently asked questions

What is an AI data company?

An AI data company supplies the data used to train, evaluate and ground AI models. That includes human-created data such as preference rankings, expert demonstrations, evaluation benchmarks and RL environments, plus licensed content, web-scale corpora, ready-made datasets, synthetic data and the infrastructure to source and curate it.

Which AI data company is the best overall?

There is no single winner for every use case. For frontier-model data in late 2026, Surge AI, Scale AI and Mercor are the strongest overall. Snorkel AI and Labelbox lead for research-grade datasets and RL environments, and Prolific is best for self-serve participant studies. For sourcing data rather than labor, Bright Data leads for web data and Protege for licensed non-public data.

Who are Scale AI's main competitors?

The main competitors are Surge AI, Mercor, Snorkel AI, Labelbox, Turing, Handshake AI, Invisible Technologies and micro1 for frontier human data. Appen, iMerit, Sama and Toloka compete for annotation and multilingual work.

Is Scale AI still neutral after Meta's investment?

Scale remains legally independent, but Meta owns 49% after its 2025 investment. TIME and TechCrunch reported that OpenAI and Google wound down work with Scale afterwards. Buyers who compete with Meta should weigh this and ask about data isolation.

Can I buy off-the-shelf AI training data instead of commissioning it?

Yes. Surge AI, Mercor, Turing, Appen and Toloka sell ready-made datasets, and Snorkel AI offers dataset samples. Defined.ai, Bright Data and the Snowflake and Databricks marketplaces sell licensed or web datasets. Ask whether the same data is licensed to other labs.

How much do AI data companies charge?

Most frontier data vendors charge custom prices per project, per task or per expert hour. Prolific is a public exception: you set participant pay and add a platform fee of usually 42.8% for corporate customers or 33.3% for academic and non-profit customers. Web data is usually priced per record, per request or per credit; Bright Data, for example, lists datasets from $250 per 100,000 records.

What is the difference between data labeling and human data for LLMs?

Data labeling usually means tagging images, text or sensor data with predefined labels. Human data for LLMs means experts writing, ranking and grading model responses, building rubrics, and creating tasks and environments that teach reasoning and agentic skills.

What are RL environments?

RL environments are simulated workspaces, such as codebases, browsers, spreadsheets or business apps, where a model attempts tasks and gets a graded reward. Labelbox, Snorkel AI, Surge AI, micro1, Toloka, Mercor and Invisible all offer them.

Is there a free web-scale training corpus?

Yes. Common Crawl publishes free crawls; the October 2026 crawl has 2.16 billion pages. You pay only your own processing costs.

Did you test these companies?

No. We did not run hands-on tests, commission paid projects or buy data. The ranking is independent research based on public evidence from company websites, announcements and independent reporting, as described on our methodology page.

Related guides: Best AI training data providers · Best AI datasets · Best robotics data companies · Best inference providers

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