Mercor pricing

About

Mercor is an AI talent and data marketplace that connects vetted human experts — doctors, lawyers, engineers, financial analysts, consultants — to AI training, evaluation, and agent-building projects run by frontier AI labs and enterprises. The thesis is that frontier models no longer improve on public data alone; they improve when domain experts apply professional judgment to how models reason and respond. Mercor packages that expertise as placeable, scoped contract work and, increasingly, as enterprise data partnerships and managed agent deployments.

The company operates three connected businesses: a talent marketplace (Experts) that finds, vets, and places professionals into AI projects “in days,” with 4M+ experts in its network; an enterprise data-partnership business (Data) that pays companies to anonymize and license their operational workflow data to AI labs; and an enterprise agents practice (Enterprise) that runs agent diagnostics, deployment, benchmarking, and data-monetization engagements, promising production agents in 4–6 weeks.

Per its own Enterprise page, Mercor reports a $10B valuation, a $1B+ revenue run rate, 100k+ contractors hired, and 400+ full-time employees, positioning it as one of the fastest-growing AI-data infrastructure companies. It is headquartered in San Francisco and remains privately held.

Pricing summary : marketplace take-rate, with only talent pay disclosed

Mercor runs a sales-gated marketplace in which the only publicly visible prices are talent-side pay rates — never the buyer-side price. The relevant dimensions:

1. Talent pay (visible, contractor-side): Mercor’s Experts page advertises an average of $141/hr, with individual roles posted between roughly $60/hr and $250/hr depending on domain and seniority (e.g. Internal Medicine $130–$180/hr, Biology PhD $80–$150/hr, MBB/Big-5 consultants $100/hr, engineering talent-network roles $70–$250/hr). Experts are paid weekly. This is what Mercor pays contractors — it is not what buyers pay Mercor.
2. Buyer price (undisclosed, sales-only): What AI labs and enterprises pay Mercor — the marketplace take-rate / margin on placed expert hours, plus engagement fees for agent diagnostics, deployment, and benchmarking — is never published. Every buyer surface terminates in a “Talk to the team” contact form. Third-party indicative only: analyst firm Sacra (via secondary coverage at eesel) pegs the employer-paid recruiting fee near ~30% of candidate pay and describes hourly work as cost-plus — useful as a rough anchor, but not a Mercor-confirmed rate.
3. Data-partnership compensation (contributor-side): On the Data page, Mercor pays enterprises to license anonymized operational data, scaling compensation with data volume and the number of tools connected (34+ integrations), with “no fees or costs” to the contributor and payment within 2–4 weeks of a signed agreement.

What makes this different: Mercor inverts the usual pricing-page convention — it loudly publishes what it pays out (hourly expert wages, data-partner payouts) while keeping what it charges entirely behind sales, so the headline dollar figures on the site are payouts, not the product price. This is a classic usage-based pricing marketplace where the metered unit (expert-hours / scoped projects) is real but the buyer rate is opaque, and the whole motion is sales-led.

Pricing by product

Important: Every dollar figure below is a talent-side pay rate or a data-contributor payout — money Mercor pays out. It is not the buyer-side price. What AI labs and enterprises pay Mercor (the marketplace take-rate / engagement fee) is undisclosed and sales-quoted on every surface.

Buyer-side engagements (price undisclosed)

Third-party indicative only (not Mercor-confirmed): Sacra’s analyst note, via eesel’s pricing teardown, estimates the employer recruiting fee near ~30% of candidate pay and characterizes hourly expert engagements as cost-plus. Treat these as rough anchors — Mercor publishes no rate card, so the realized margin per engagement is not verifiable from any first-party source.

Talent pay rates (contractor-side, public — NOT the buyer price)

Data-partnership compensation (contributor-side, paid TO the enterprise)

Sales motions across products: there is no PLG / self-serve tier anywhere — talent placement, enterprise agents, and data partnerships are all sales-led and engagement-scoped, with buyer pricing custom-quoted via “Talk to the team” forms.

Hidden costs : the take-rate is the hidden cost

There is no public Mercor bill to itemize, so a synthetic cost table would be fabrication. The honest framing is the opposite of most pages in this corpus: the hidden cost is the undisclosed take-rate itself. The only dollar figures Mercor publishes are payouts — what it pays experts ($60–$250/hr, ~$141/hr average) and data partners (custom). What a buyer pays for those same hours is never shown, so buyers cannot reconcile their invoice against the talent-side rate they can see on the public marketplace.

What is known, and where the cost hides:

• The markup is invisible by design. A buyer hiring an expert posted at $141/hr does not know whether Mercor’s fee is 10%, 30%, or 50% on top. Third-party analysts (Sacra via eesel) estimate a ~30% recruiting fee and cost-plus hourly billing, but Mercor confirms nothing — so the effective blended rate a lab pays is unverifiable.
• Engagement scope is the real meter. Enterprise services (Agent Diagnostics, Deployment, Benchmarking, Data Monetization) are quoted per engagement, not per seat or per token, so two buyers with similar headline needs can pay very different totals depending on how the SOW is scoped.
• Data-partnership “free” has a catch worth naming. Mercor tells data contributors there are “no fees or costs,” and that is true for the contributor — but it is silent on what the AI-lab buyer pays for that same anonymized dataset, which is the margin that funds the contributor payout.
• Procurement risk is a cost too. Because there is no rate card, every deal is a custom negotiation; without a reference price or a clear value metric, buyers carry the full information asymmetry, which the sales-led motion is built to exploit.

Illustrative buyer cost (third-party indicative only — NOT a Mercor quote)

There is no Mercor rate card, so the table below is not a real bill. It simply applies the only two numbers available — the public talent rate and a third-party take-rate estimate — to show the shape of what a buyer might pay for one expert, full-time-equivalent, for a month. Every figure except the talent rate is an outside estimate, not a Mercor-confirmed price.

The lesson is not the number — it is that a buyer cannot derive this from any Mercor surface. The $141/hr is real and public; the ~30% markup is an outside estimate; the total is therefore a guess, which is exactly the procurement problem Mercor’s opacity creates.

Want to model your own scenario? Use the Mercor pricing calculator to sketch buyer cost across expert rate and an assumed take-rate — bearing in mind that, unlike most pages in this corpus, every output is an indicative estimate built on third-party assumptions, not a Mercor quote. To compare opaque, sales-quoted usage-based pricing motions against transparent ones, browse the pricing blueprint corpus.

Pricing evolution : from talent marketplace to enterprise data + agents

Mercor’s “pricing” evolution is really a business-model evolution: the buyer-side price has been sales-gated from day one, so what changes over time is the shape of what’s being sold (AI-interview recruiting → human-data/RLHF labeling → enterprise agents + data partnerships) and the talent-side payouts that are publicly advertised. The cadence below tracks model and payout shifts, not list-price changes — because there is no public list price.

Cadence

Tracked range: 2025-02–2026-06. Wayback archives begin 2026-03 for the Enterprise/Experts surfaces; pre-2026 model history is reconstructed from press. No public buyer price has ever been listed, so the “price changes” column tracks advertised talent payouts, not buyer list prices.

Notable changes

• 2025-02-20 — Series A: $100M at $2B valuation; AI-interview recruiting marketplace, ~$85/hr avg pay, ~30k contractors (TechCrunch).
• 2025-10-27 — Series C: $350M at $10B valuation, Felicis-led with Benchmark, General Catalyst, Robinhood Ventures (TechCrunch).
• 2026-03 — Enterprise positioned as an agent-building software platform (Wayback 2026-03 Enterprise snapshot); average advertised pay ~$99/hr.
• 2026-03/04 — LiteLLM supply-chain breach exposed Slack, ticketing, and contractor-interaction data; Meta paused work (TechCrunch, Wired).
• 2026-05 — Standalone Data-partnership business surfaces (34+ OAuth integrations, SOC 2 Type II, 2–4 week payouts); average advertised pay rises to $105/hr (Wayback 2026-05).
• 2026-06 — Enterprise restructured into four named, sales-quoted services (Agent Diagnostics, Deployment, Benchmarking, Data Monetization); average advertised pay reaches $141/hr, 258.1K roles created, $3M+ daily payouts (Wayback 2026-06 + live capture).

The LiteLLM breach in detail

In late March 2026, Mercor was caught in a supply-chain attack on the open-source LiteLLM project: a hacking group (TeamPCP) inserted malicious code that was removed within hours, after which the extortion group Lapsus$ claimed it had targeted Mercor’s systems. The exposed material reportedly included Slack data, ticketing information, and videos of AI-system-to-contractor conversations; Mercor declined to confirm whether customer or contractor data was exfiltrated, characterizing itself as “one of thousands of companies” affected. Wired reported that Meta paused work with Mercor in response, and the incident drew a 600-point, 226-comment Hacker News thread (2026-04-27) — a notable trust event for a marketplace whose entire buyer relationship is opaque and sales-gated. For a price-secret vendor, a breach that touches contractor data is precisely the kind of event that erodes the asymmetric trust the model depends on.

What’s unique : publishing payouts while hiding the buyer price

1. Payouts are public; the price is secret. Almost every company in the pricing blueprint publishes what it charges and hides its costs. Mercor inverts this: it loudly advertises what it pays out — ~$141/hr average, $60–$250/hr by role, $3M+ in daily payouts — while keeping the buyer-side take-rate entirely behind a contact form. The headline dollar figures on the site are wages, not the product price, which is a deliberate recruiting-funnel design that also conveniently obscures Mercor’s margin.

2. The “AI interview” is the vetting mechanism and the moat. Mercor screens experts through an adaptive AI interview that “evaluates expertise at scale,” asking field-specific questions to assess depth. This lets a tiny team vet a 4M+ expert network without human recruiters — it is the operational lever behind the marketplace’s speed (“first offer instantly,” placement “in days”) and a genuine differentiator versus traditional staffing firms or labeling vendors like Scale AI / Surge.

3. A marketplace take-rate dressed as enterprise infrastructure. Underneath the agent-deployment and data-partnership packaging, the core economic engine is a classic two-sided marketplace take-rate: Mercor pays the supply side (experts, data contributors) and charges the demand side (AI labs, enterprises) a margin it never discloses. The enterprise-services and data-monetization SKUs are higher-margin wrappers on the same human-expertise supply.

4. It pays the supply side on both sides of its data business. In the data-partnership business, Mercor pays enterprises to license their anonymized operational data, then sells access to AI labs — so it is buying from one set of customers and selling to another, monetizing the spread. The contributor sees “no fees,” which is true; what they never see is the price the lab pays for their data.

Strengths & weaknesses

Billing UX : contact forms, weekly payouts, and OAuth-scoped data

• “Talk to the team” / “Talk to the Team” contact form (Experts/Partner) — the universal buyer entry point: First name, Last name, Business email, Company, Job title, “What can we help with?” dropdown, plus a free-text field. There is no checkout or rate card.
• “Get in touch” / “Let’s chat” / “Let’s talk” CTAs (Enterprise) — every enterprise service (Agent Diagnostics, Deployment, Benchmarking, Data Monetization) routes to scheduling time with sales rather than a price.
• Weekly contractor payouts — Mercor advertises “Daily Payouts $3M+” in aggregate and tells experts they “get paid weekly for your expertise”; payment terms are “defined upfront” per project.
• Data-partner OAuth onboarding — contributors “authenticate via OAuth” to grant read-only access across 34+ integrations, then receive a single background extraction; payout is wired within 2–4 weeks of delivery.
• Talent-network “Apply once” model — experts apply once to a Talent Network listing (e.g. Physician $110–$250/hr) and receive future offers that match their qualifications, with referral links and referral bonuses surfaced on each job page.

Strategic wins : why the sales-gated marketplace motion worked

1. Advertise payouts, not prices — turn the recruiting funnel into the marketing engine

By making the talent-side pay the loudest number on the site, Mercor solves its hardest problem (sourcing scarce domain experts) while keeping its margin invisible. The public $141/hr average and $3M+ daily payouts are simultaneously a recruiting pitch and a credibility signal to buyers (“look how serious our supply is”). It is a textbook example of choosing which side of a two-sided market to make transparent — and the value-metric problem resolves in Mercor’s favor because the metric it shows (wages) is not the metric it bills on.

2. Use sales-gating to capture maximum margin in a nascent, undifferentiated market

With no public rate card, every engagement is bespoke and the buyer carries the information asymmetry — the defining feature of a sales-led pricing motion. In a market where comparable RLHF/data vendors (Scale AI, Surge) are also opaque, transparency would only arm procurement; opacity lets Mercor price each frontier lab to its willingness to pay. This is the right call while the category is young and reference prices don’t exist.

3. Build adjacent, higher-margin SKUs on the same human-expertise supply

Mercor turned one supply base (vetted experts) into three demand-side products: talent placement, enterprise agents, and data partnerships. Each new SKU monetizes the same network at a higher margin without re-acquiring supply — a capital-efficient expansion that helped drive the move from a $2B to a $10B valuation in eight months. It mirrors how successful usage-based pricing businesses layer outcome-based wrappers on a metered base.

4. Pay the supply side to unlock a brand-new data asset

The data-partnership model pays enterprises for something they previously gave away or sat on — their operational workflow data — and resells it to labs. By absorbing all extraction/anonymization cost and charging the contributor nothing, Mercor removes the friction that kills most data-licensing deals, then monetizes the spread on the buyer side. It is a clean example of aligning the meter to value created rather than to cost incurred.

Areas to improve : buyer-side price opacity and trust gaps

1. Publish at least a pricing framework, even without a rate card

Total opacity maximizes margin but also maximizes buyer friction and suspicion — the recurring “the final price tag for the client remains a mystery” critique in third-party teardowns. Mercor could publish a structure (e.g. “cost-plus on placed hours; engagement-scoped for enterprise services”) without revealing the actual percentage, the way many enterprise vendors disclose their pricing model while keeping the number gated. That alone would reduce procurement anxiety without surrendering negotiating leverage.

2. Close the trust gap the LiteLLM breach opened

A breach that touched contractor data — and made a major lab pause work — is existential for a marketplace built on opaque, asymmetric trust. Mercor’s terse, non-committal incident response (declining to confirm what was exfiltrated) is the opposite of what rebuilds confidence. Publishing a concrete post-incident security posture (scope of access, third-party audit results, contractor-data handling) would directly address the data-governance concerns that high-stakes AI-lab customers now weigh.

3. Stabilize talent payouts to avoid signaling supply scarcity

Advertised average pay jumped from $99/hr to $141/hr in two months — great for recruiting, but to a sophisticated buyer it signals supply scarcity and rising input costs that will eventually pass through to engagement prices. Mercor should pair the rising-payout story with a supply-depth narrative (network size, fill rates) so the number reads as quality, not as cost pressure. Otherwise buyers will (rightly) assume their bespoke quotes are climbing in lockstep.

4. Give the data-partnership buyer side the transparency the contributor side already has

The data business is transparent to contributors (“no fees, paid in 2–4 weeks”) but a black box to the AI-lab buyer. As enterprise data-licensing matures, labs will demand provenance, volume, and pricing clarity. Offering a buyer-facing pricing schema for data — even a tiered, volume-based one — would de-risk the largest deals and differentiate Mercor from purely opaque competitors.

Key takeaways

1. Decide which side of a two-sided market to make transparent. Mercor makes payouts public and prices private because supply (experts) is its scarce resource and demand (labs) is its margin source. The transparency you choose should solve your hardest acquisition problem, not default to “show the buyer everything.”
2. Opacity is a viable pricing strategy in a young category — temporarily. When no reference prices exist and competitors are equally opaque, a sales-gated, no-rate-card motion lets you price each buyer to willingness-to-pay. But it is a maturity-bound advantage: as the category standardizes, the same opacity becomes a procurement liability.
3. A visible payout is not a price — don’t let buyers conflate them. Mercor’s $141/hr is a wage, not a charge, yet the site lets the two blur in the buyer’s mind. Any marketplace that publishes one side’s economics must be deliberate about whether that number anchors the other side’s expectations.
4. Margin expansion can come from re-packaging the same supply. Mercor monetized one expert network as placement, agents, and data — each a higher-margin wrapper. The growth lever was new demand-side SKUs, not new supply, which is far more capital-efficient.
5. Price secrecy raises the stakes of every trust event. When buyers already accept an opaque, asymmetric relationship, anything that breaks trust (a breach, a payment dispute) is disproportionately damaging because the relationship has no transparency cushion to fall back on. Mercor’s breach response shows the cost of under-investing here.

UBP implications

1. The metered unit and the disclosed number can be deliberately different. Mercor meters buyers on expert-hours / scoped engagements but discloses only the payout side. For UBP strategists, this separates “the value metric you bill on” from “the number you publish” — a powerful but trust-sensitive lever that only works while buyers tolerate opacity.
2. Take-rate models resist clean usage-based transparency, and that’s a choice. A marketplace could publish its take-rate as a clear percentage meter; Mercor’s refusal to do so shows that opacity is often a pricing decision, not a technical limitation. The UBP lesson is that “we can’t show a rate” usually means “we won’t” — and buyers should price that risk in.
3. Outcome- and engagement-scoped wrappers can sit on a metered base. Mercor’s enterprise services are engagement-priced on top of an hourly expert-hours base, echoing the broader shift toward outcome-based pricing. Usage-based foundations make it easy to layer higher-margin, value-priced SKUs without rebuilding the meter.

Sources

• Mercor Experts marketplace (talent pay rates) (accessed 2026-06-08)
• Mercor Data partnerships (accessed 2026-06-08)
• Mercor Enterprise (accessed 2026-06-08)
• Mercor Partner / contact (accessed 2026-06-08)
• Mercor blog — Series C announcement (accessed 2026-06-08)

Comparative context across the corpus is available in the pricing blueprint. Funding, take-rate, and breach details cited inline above come from third-party press and analyst coverage (TechCrunch, Wired, Sacra/eesel, Hacker News) recorded in this page’s signal_sources, not from Mercor’s own surfaces.

Bottom line

Mercor is the corpus’s clearest example of a payout-public, price-gated marketplace: it advertises what it pays experts (~$141/hr average, $60–$250/hr by role) and data partners, while keeping the buyer-side take-rate — the actual product price — entirely behind a “Talk to the team” form. Third parties peg the recruiting fee near 30% and hourly work as cost-plus, but Mercor confirms nothing, so the margin that funds a $10B valuation and a $1B+ run rate stays invisible by design. That opacity is a defensible strategy in a young, undifferentiated category — until a trust event like the March 2026 LiteLLM breach reminds everyone how little transparency the relationship actually rests on.

Want to compare Mercor against other sales-led, usage-based marketplace pricing models? Browse the pricing blueprint.

Pricing timeline : Major events on a vertical axis

Each milestone below corresponds to a public pricing change, product launch, or material adjustment. Major events use a filled marker; minor adjustments use a faded one.

Enterprise reorganized into 4 named services; average pay $141/hr

Jun 2026

The Enterprise page was restructured into four named, sales-quoted services (Agent Diagnostics, Deployment, Benchmarking, Data Monetization) and 'Data Partnerships' + 'Enterprise AI' joined the footer nav. The Experts marketplace advertised an average of $141/hr (roles $60–$250/hr), 258.1K roles created, and $3M+ daily payouts — while every buyer surface still routes to a 'Talk to the team' form with no rate card.

Data-partnership business surfaces; average pay $105/hr

May 2026

The standalone Data page (pay-enterprises-to-license-anonymized-operational-data) is archived from May 2026, with 34+ OAuth integrations, SOC 2 Type II, and 2–4 week payouts. Average advertised pay rose to $105/hr; roles created 189.6K (Wayback 2026-05).

LiteLLM supply-chain breach; average pay $99/hr

Apr 2026

A supply-chain compromise of the open-source LiteLLM project (claimed by Lapsus$/TeamPCP) exposed Slack, ticketing, and contractor-interaction data; Wired reported Meta paused work with Mercor. The live Experts page advertised an average pay of $99/hr, 177.5K roles created, and $2M+ daily payouts (Wayback 2026-04; TechCrunch/Wired, 2026-03/04).

Enterprise agents launched as a 'software platform' (WEEK 1–4 build)

Mar 2026

By March 2026 the Enterprise page framed Mercor as an agent-building software platform — a WEEK 1–4 'organizational context graph → agent spec → deploy → iterate' flow, plus an early ACE/APEX benchmark — alongside the $10B / $1B+ run-rate / 100k+ contractor / 400+ employee scale stats. All buyer pricing still sales-gated.

Series C — $350M at $10B valuation (5× in 8 months)

Oct 2025

Felicis-led $350M Series C (with Benchmark, General Catalyst, Robinhood Ventures) quintuples the valuation from $2B to $10B in eight months; Mercor tells investors it is on pace to $500M ARR. Buyer-side pricing remains undisclosed throughout (TechCrunch/CNBC, 2025-10-27).

Series A — $100M at $2B valuation

Feb 2025

Mercor (founded 2023) raises $100M at a $2B valuation as an AI-interview recruiting marketplace placing domain experts onto AI-lab training projects; founders Foody, Hiremath, and Midha are 21. Press reports an average contractor pay of ~$85/hr and ~30k contractors at this stage (TechCrunch, 2025-02-20).