What are the best LLM evaluation companies in 2026?

Deepen AI Services runs dedicated, trained raters who score your outputs against your rubric every release and check your LLM judge. Scale AI runs evaluations and leaderboards for model developers, Surge AI, Mercor and Labelbox bring expert networks, and Shaip, Sama, iMerit, Appen and Toloka offer evaluation alongside their data work.

What is human evaluation of LLMs?

People score a model's outputs against a rubric, such as accuracy, helpfulness, safety and tone, and give a reason for each score. It catches what automated metrics miss, and it's the reference an LLM judge is checked against.

Do I still need human evaluation if I use an LLM judge?

Yes, to know how far to trust the judge. Human scores on the same items show where the judge agrees and where it goes wrong, and catch new kinds of failure the judge wasn't built for.

How much does LLM evaluation cost?

Most evaluation companies quote each project, because the price depends on the expertise and turnaround. Deepen AI Services quotes the rate in your plan before you pay, and Prolific charges participant pay plus a platform fee, usually 42.8% for companies.

What are the best LLM evaluation tools?

Braintrust, LangSmith and the open-source DeepEval framework are examples of tools that run automated checks and LLM judges. They work alongside human raters, who supply the scores those checks are calibrated against.

LLM and AI evaluation companies in 2026

Facts checked on each company's own website on 24 October 2026.

LLM evaluation companies supply the people who judge what automated metrics can't: whether an answer is correct, helpful, safe and on-brand. Deepen AI Services runs a dedicated, trained, in-house rater team that scores your outputs against your rubric with a reason for every score, keeps the same people on every release, and checks where your LLM judge goes wrong. Scale AI runs evaluations, red teaming and leaderboards for model developers. Surge AI, Mercor and Labelbox bring expert networks, and Shaip, Sama, iMerit, Appen and Toloka offer model evaluation alongside their data work. Prolific provides screened participants.

Human evaluation or an evaluation tool?

Evaluation tools run automated checks, LLM judges and dashboards on every build. Human evaluation companies supply the judgment those checks are calibrated against. Most teams need both: people to score a fixed test set, check the judge and catch new failure modes, and a tool to run the checks at scale.

Braintrust: an observability and evaluation platform for AI agents, where outputs can be scored by LLMs, code or humans, with annotation views.
LangSmith: LangChain's platform for evaluating and monitoring agents, with LLM-as-judge and code evals and feedback scores in dashboards.
DeepEval: an open-source framework for evaluating LLMs, maintained by Confident AI, whose platform aligns automated metrics with human judgment.

Deepen AI's raters work inside your own tools and score the same items as your judge, so you know how far to trust it.

The short answer: the right company for the work

The work	Companies	What each offers
Scoring outputs against your rubric, every release	Deepen AI Services, Scale AI, Shaip, Sama	Deepen AI Services keeps the same trained raters and a fixed test set, so releases compare. Scale AI, Shaip and Sama run model evaluation programs.
Checking your LLM judge	Deepen AI Services	Raters score the same items as your judge, and the report shows where the two agree and where the judge goes wrong.
Expert and frontier-model evaluations	Deepen AI Services, Scale AI, Surge AI, Mercor, Labelbox	Deepen AI Services quotes specialist raters in your plan. Scale AI evaluates for model developers, and Surge AI, Mercor and Labelbox run expert networks.
Red teaming before release	Deepen AI Services, Scale AI, iMerit, Toloka	Deepen AI Services' trained testers log every attempt that worked, with its severity. Scale AI, iMerit and Toloka also offer red teaming.
Benchmarks and leaderboards	Deepen AI Services, Scale AI, Toloka	Deepen AI Services builds custom test sets from your model's real failures. Scale AI and Toloka publish public leaderboards.
Ratings from trained raters or from the public	Deepen AI Services, Prolific	Deepen AI Services provides trained raters who learn your rubric. Prolific provides screened participants, self-serve.

The companies compared

Company	Evaluation work	How you start	Pricing
Deepen AI Services	Rubric scoring with a reason for each score, judge checks, red teaming and custom benchmarks, by the same raters every release	A plan by email in 2 working days, then a two-week pilot	Quoted in your plan before you pay
Scale AI	Scale Evaluation, expert raters, red teaming and SEAL leaderboards	Book a demo	No prices on its site
Surge AI	Frontier data, post-training runs and an expert workforce	Contact its team	No prices on its site
Mercor	Human evaluation by experts, and benchmarks	Book a demo or contact sales	No client prices on its site
Labelbox	Evaluations and RL environments, with the Alignerr expert network	Talk to its team	No prices on its homepage
Shaip	Expert-led model evaluation and AI data	Talk to a specialist	No prices on its homepage
Sama	Model evaluation alongside annotation	Talk to an expert	Discussed in a first call
iMerit	Evaluation and red teaming with domain experts	Talk to an expert or book a demo	No prices on its site
Appen	Model evaluation and AI training data	Talk to an expert	No prices on its site
Toloka	AI evaluation, red-teaming and an Arena leaderboard	Talk to an expert	No prices on its site
Prolific	AI evaluation by screened participants	Self-serve	Participant pay plus 42.8% (33.3% for academics)

The companies in detail

1. Deepen AI Services: trained raters on your rubric, every release

Deepen AI Services gives you a dedicated team of trained, in-house raters. Each one learns your rubric and scores a set of known answers before rating live outputs, and every output comes back with a score on each rubric line and the reason behind it. When the rubric doesn't cover a case, the team lead sends it to you, and your answer is added to the rubric.

Best for: Release-by-release evaluation of chat, search answers, summaries, extraction and generated copy, plus specialist domains quoted in your plan.
Judge checks: Raters score the same items as your LLM judge, and the report shows where the two agree and where the judge goes wrong.
Consistency: Known items stay mixed into the work, agreement is in your weekly report, and a fixed test set and rubric let every new version be scored the same way.
Also: AI red teaming before release, and custom benchmarks built from your model's real failures, yours to keep.
Pricing: Quoted in your plan, because the rate depends on the expertise and turnaround the work needs. You pay for the hours raters work, with a team lead, QA checks and a weekly report included.
How you start: Send your rubric and 20 to 30 outputs you've already scored. You get a plan and a price by email within 2 working days, with no sales call, then a two-week paid pilot on your real outputs.
Security: Your outputs stay under NDA, inside your tools, with the access you set. SOC 2 Type II, ISO 27001, ISO 9001 and TISAX.

See LLM evaluation and AI evaluation.

2. Scale AI: evaluation for model developers

Scale Evaluation is a platform for analyzing model performance and safety across domains, capabilities and model versions, backed by expert human raters. Scale cites thousands of trained red teamers, offers proprietary and targeted evaluation sets, and publishes expert-evaluated leaderboards from its SEAL lab. You start by booking a demo.

3. Surge AI: data for frontier models

Surge AI focuses on frontier data: off-the-shelf datasets, post-training runs, RL environments and an expert workforce, and it publishes its own benchmarks. Its site shows no prices, and you start through its contact page.

4. Mercor: expert human evaluation

Mercor offers human evaluation by experts, such as physicians, lawyers and engineers, and builds benchmarks on real professional work. It publishes what it pays experts by role, averaging $100 an hour, but not what clients pay.

5. Labelbox: evaluations and expert raters

Labelbox builds RL environments and evaluations for post-training, and runs Alignerr, a network it describes as 2.6 million skill-tested experts who write rubrics, preferences and assessments. Its homepage shows no prices.

6. Shaip: expert-led model evaluation

Shaip offers expert-led model evaluation alongside AI data collection and annotation for enterprise AI teams. You start by talking to a specialist, and its homepage shows no prices.

7. Sama, iMerit and Appen: evaluation alongside annotation

Sama lists model evaluation among its annotation and validation services. iMerit, now part of EXL, offers evaluation and red teaming with domain experts from its Scholars program. Appen supplies model evaluation with AI training data across more than 500 locales. None publishes prices.

8. Toloka: AI evaluation and an Arena leaderboard

Toloka lists AI evaluation, red-teaming and an Arena leaderboard among its data services for AI agents and LLMs. Its site points businesses to a conversation with its experts and shows no prices.

9. Prolific: screened participants for AI evaluation

Prolific gives AI teams self-serve access to more than 300,000 active participants. You pay the participants plus a platform fee, usually 42.8% for companies, and design the evaluation and review the answers yourself.

How to choose an LLM evaluation company

Can the raters judge your outputs? General chat needs careful, trained raters. Legal, medical or code outputs need people who know the field.
How are raters calibrated? Ask how they learn your rubric, how agreement is checked, and how you'll see it.
Will releases compare? The same raters, a fixed test set and a fixed rubric make version-to-version results meaningful.
Can they check your LLM judge? Human scores on the same items show where an automated judge can be trusted.
Do you get a reason for every score? Reasons turn scores into fixes, and make the results auditable.
Are unreleased outputs safe? Look for an NDA, work inside your tools and audited controls such as SOC 2 and ISO 27001.

What to send for a pilot

Your rubric, or examples of good and bad answers
20 to 30 outputs you've already scored
Where the outputs live: your evaluation tool or a shared sheet
How many outputs you need scored each round, and by when
The model versions you want to compare

For running the review itself, see the playbook for human review of LLM outputs.

The best LLM evaluation companies in 2026