If you need humans to evaluate your model against a rubric, you have more options than you might think, and they are not interchangeable. This is a practical guide to the categories of provider, ranked by the criteria that decide whether the resulting score is one you can defend. Full disclosure: we build Pathwize, so read this as an informed but interested guide. We have kept the criteria explicit so you can weigh them yourself.
How we ranked them
Four things separate a trustworthy evaluation from a number on a slide. Expert quality: are the graders actually qualified to judge your outputs? Agreement: can the provider show inter-rater agreement, or are you trusting a single unverified opinion? Provenance: is there a signed record of who graded what, against which rubric? And EU fit: does the data stay in the EU with AI Act-ready documentation? We ranked the categories on how well they deliver all four at once.
1. Pathwize, best for verifiable, EU-native expert evaluation
Pathwize scores your outputs against your rubric using credential-verified experts, double-grades where it matters, and reports inter-rater agreement so you can see the score is trustworthy. Every grade carries a signed provenance trail that maps to AI Act Annex IV, and data stays in the EU by default.
Best for: teams in regulated or high-stakes domains that need a benchmark they can defend and reproduce, not just a fast number. It is the only category that delivers expert quality, agreement, provenance and EU residency as one package. See the model evaluation solution for how it works.
2. US expert-data marketplaces
Large marketplaces can reach genuine expertise at scale and move quickly. The trade-offs are opacity (you rarely see who graded your outputs or how consistently) and US data residency, which is a problem for EU-regulated work.
Best for: US-based teams that prioritise scale and speed over auditability and EU compliance.
3. Crowd-labeling and BPO data vendors
The established annotation vendors offer high volume at low cost. For evaluation, the catch is grader quality: a general crowd applying a rubric it does not deeply understand produces noisy scores on expert content.
Best for: high-volume, low-complexity evaluation where the rubric is simple and domain expertise is not required.
4. Automated eval platforms (LLM-as-judge)
Model-graded evaluation is fast and cheap, and useful for regression checks at scale. But an LLM judge inherits the blind spots of the models it grades and cannot be trusted on the exact edge cases where human judgment matters most.
Best for: rapid, large-scale sanity checks, ideally alongside human evaluation rather than instead of it.
5. Governance and audit tooling
Provenance and audit platforms give you the traceability layer, the signed record of what happened. What they do not give you is the graders: they document the work but do not supply the humans who do it.
Best for: teams that already have evaluators and need to add an audit trail on top.
6. In-house expert panels
Building your own panel gives you maximum control and context. It is also slow to staff, hard to scale up and down, and leaves you to build agreement tracking and provenance yourself.
Best for: organisations with steady, long-term evaluation needs and the appetite to run the operation internally.
7. Academic and freelance expert networks
Hiring individual experts directly reaches deep domain knowledge cheaply. The overhead is coordination, and there is usually no built-in QA, no agreement measurement, and no provenance, so you are assembling the reliability layer by hand.
Best for: small, one-off evaluations where you can manage a handful of experts yourself.
How to choose
Start from the decision the score will inform. If it gates a release in a regulated domain, weight expert quality, agreement and provenance heavily, and treat EU residency as non-negotiable. If you just need a rough signal at scale, an automated judge or a crowd vendor may be enough. Most mature teams end up combining an automated first pass with expert human evaluation on the outputs that matter, and insist on provenance for both.