Guide · RLHF providers

Best RLHF Providers (2026)

Reinforcement learning from human feedback is where the annotation market's money went: preference judgments, expert demonstrations and evaluations are the highest-value human data in AI. These are the providers that sell it, compared on the record.

RLHF specialist

Surge AI

Preference data, SFT demonstrations, human evaluation and safety data — the whole catalog is post-training.

Full post-training program

Scale AI

RLHF alongside generation, red-teaming and evaluation under one enterprise contract.

Expert preference panels

Mercor

When preference judgments require doctors, lawyers and engineers rather than raters.

RLHF providers compared
Vendor	Model	Founded	Pricing	Best for
Surge AI	Managed service	2020	Not published	Labs that rank data quality above all else for RLHF and reasoning data, and prefer a founder-controlled vendor with no outside investors.
Scale AI	Managed service	2016	Not published	Frontier labs, large enterprises and government programs that need annotation and evaluation run as a managed, high-volume program.
Mercor	Expert network	2023	Not published	AI labs that need credentialed expert judgment — clinical, legal, financial, engineering — at serious scale and speed.
Snorkel AI	Data lab	2019	Not published	Enterprises and labs that want benchmarks, evaluations and expert datasets built with academic-grade rigor, delivered as a service.
Micro1	Expert network	2022	Not published	Companies that want vetted expert capacity without running their own recruiting funnel, and experts who want clearly posted pay ranges before they apply.

Staffing expert RLHF work

The binding constraint on modern RLHF is expert supply: preference data for a medical model needs clinicians. Expert networks solve the staffing problem directly — Micro1, for example, certifies experts through an AI-led interview and matches them to projects, with per-role pay ranges published on its job board (roughly $22–$70/hour for generalist roles, $200+/hour for deep specialists, per third-party reviews of its listings). Companies can register to post projects: register your company with Micro1 →

Disclosure: this is a referral link. If your company registers with Micro1 through it, we may earn a referral commission at no cost to you. It does not change where Micro1 ranks — see our methodology.

What separates good RLHF supply

Rubric quality: preference data is only as good as the grading criteria; ask to see the rubric before the price.
Rater calibration: measured agreement between raters, and a process for the disagreements — disagreement is signal, not noise, in expert domains.
Demonstration provenance: who wrote the ideal answers, and what qualifies them. See how the data labs package this.

Frequently asked questions

What is the best RLHF provider?

Surge AI is the specialist pick — RLHF is its core business, and Reuters reported it past $1 billion in 2024 revenue on that quality-first model. Scale AI is the pick when RLHF sits inside a full post-training program. Mercor and Micro1 supply the expert humans preference data increasingly requires. Snorkel AI is the pick for evaluation design around the RLHF loop.

What do RLHF providers actually sell?

Preference data (humans ranking model outputs), supervised fine-tuning demonstrations (experts showing the ideal answer), human evaluation (grading outputs against rubrics), safety and red-teaming data, and increasingly RL environments — sandboxed tasks a model can practice against. The mix shifts every model generation toward harder, more expert work.

How much does RLHF data cost?

Providers do not publish RLHF pricing. The inputs are public: generalist preference labeling follows annotator rates ($8/hour floors on transparent platforms up to $60+/hour US), while expert preference work follows expert rates — $85–$200/hour typical for credentialed specialists, per CleverX’s published guidance. RLHF budgets are set by how expert the judgments must be.

Sources

One primary source per vendor shown; full lists on each profile.

Facts on this page were drawn from the public sources above, checked November 2026. If you spot an error, tell us at the corrections address in the footer — we fix sourced errors and note the change.

Last updated: November 8, 2026