Best Scale AI alternatives for expert data in 2026
A practical look at the leading alternatives to Scale AI for sourcing expert human data to train and evaluate AI models, agents, and benchmarks.
If you are looking for alternatives to Scale AI for expert human data in 2026, the names that come up most often are Mercor, Surge AI, Appen, Toloka, Turing, Handshake AI, Invisible Technologies, Labelbox, and CleverX. Each one supplies people, not just tooling, to train and evaluate AI models and agents. The right choice depends on whether you need volume labeling, broad crowds, or verified practising experts for high-judgement work.
A note on fairness and accuracy: This comparison is based on publicly available information and secondary research. Vendors update their pricing, features, and positioning over time, so please confirm the current details with each provider directly before you decide. We aim to be objective and fair. Every company covered here is a strong option in the space, and the best choice depends on your specific needs.
Why teams look beyond Scale AI
Scale AI helped define the modern data pipeline for AI. It built the Data Engine, the Outlier contributor platform, and the SEAL evaluation work that many labs leaned on. In 2025 and 2026 the market shifted. Meta took a large non-voting stake in Scale, founder Alexandr Wang moved to Meta, and several large customers reportedly reduced their reliance on the company, according to reporting from TechCrunch and others. None of that makes Scale a poor choice. It does mean buyers now compare a wider field.
The deeper reason is that AI training needs have changed. Early work was dominated by labeling at volume. The frontier has moved toward harder tasks: grading reasoning, writing pass or fail rubrics, building benchmarks from real professional work, and capturing ideal worked answers. That work rewards judgement over throughput, and judgement comes from people who actually do the job. So the question is less “who labels fastest” and more “whose experts can be trusted to score a model the way a senior professional would.”
How to choose an expert-data provider
A few criteria separate these vendors in practice:
- Expert depth versus crowd scale. Some platforms run large general crowds. Others curate smaller pools of specialists. For reasoning-heavy evaluation in regulated fields, depth usually beats raw headcount.
- Verification rigor. How does the vendor prove a person is who they claim and holds the credentials they claim? Identity checks, licence confirmation, work-history matching, and recorded interviews all matter.
- Domain coverage. Finance, healthcare, law, engineering, insurance, and operations each need different experts. Confirm the vendor can staff your exact domain.
- Work types. Evaluations, rubrics, benchmarks, demonstrations, preference ranking, and RL environments are distinct deliverables. Match the vendor to the output you need.
- Quality controls. Ask about inter-rater reliability, gold sets, reviewer reasoning, and audit trails.
- Commercial fit. Pricing shape, managed versus self-serve, and whether the vendor is tied to a specific lab.
For a deeper buyer framework, see how AI labs buy expert data and our roundup of the best expert data providers for AI labs.
The 10 best Scale AI alternatives for expert data
1. Scale AI (the incumbent)
What it is: The provider most teams compare against. Scale AI offers end-to-end data infrastructure that pairs human labeling and evaluation with tooling and APIs, through its Data Engine and the Outlier contributor platform. Its SEAL and Scale Labs research arm focus on evaluation and alignment.
Best for: Large programs that want integrated infrastructure, broad data pipelines, and a single vendor across modalities.
Strengths: Deep tooling, wide modality coverage, and a long track record with frontier labs.
Limits: After the Meta stake, some buyers worry about neutrality and want a provider not tied to one lab. Public detail on current evaluation pricing is thin.
Pricing shape: Custom and quote-based. Confirm current terms directly.
2. Mercor
What it is: Mercor is an expert marketplace for AI labs that connects domain experts to post-training work, including evaluation, rubric development, demonstrations, and RL environments. It has expanded fast and added enterprise evaluation products.
Best for: Labs that want a large, flexible pool of vetted experts for ongoing post-training programs.
Strengths: Scale, breadth of expert supply, and momentum with frontier customers.
Limits: Reported expert counts vary widely across sources, and the company disclosed a security incident in 2026. Verify current controls.
Pricing shape: Custom, with reported hourly expert rates that vary by domain. Confirm directly.
3. Surge AI
What it is: Surge AI is a premium RLHF and data platform that matches AI developers with a curated network of expert annotators it calls Surgers. Its work includes preference ranking, reasoning evaluation, and RL environments, supported by a managed platform and a Python SDK.
Best for: Reasoning evaluation, preference data, and RLHF at high quality.
Strengths: Strong reputation for annotator quality and a serious tooling layer. Widely cited as a leader in the space.
Limits: A contractor-classification class action has been reported as pending. Pricing is not public.
Pricing shape: Custom and quote-based.
4. Appen
What it is: Appen is a long-established data provider with a global crowd and the AI Data Platform. It covers text, image, audio, video, and 3D annotation, plus LLM fine-tuning support and some vetted subject-matter expert work.
Best for: Broad, multi-modal labeling at scale and multilingual data collection.
Strengths: Large global reach, mature processes, and wide modality coverage.
Limits: Historically crowd-first, so confirm the depth of specialist expertise for high-judgement tasks. Some analysts note competitive pressure. See our Appen alternatives guide for context.
Pricing shape: Custom and quote-based.
5. Toloka
What it is: Toloka provides expert-labeled data for RLHF, preference ranking, instruction tuning, and reward-model training, with multi-turn dialogue and domain-specific tasks. It launched a self-service platform in 2026.
Best for: General preference data at scale and multilingual coverage, plus synthetic-data verification.
Strengths: Broad domain and language claims, flexible task types, and a self-serve option for smaller teams.
Limits: Expert-coverage figures differ across Toloka’s own pages, so confirm what applies to your domain.
Pricing shape: Self-serve and custom tiers. Confirm current pricing on the vendor site.
6. Turing
What it is: Turing generates training data, evaluations, and RL environments for frontier labs, and runs agentic solutions for enterprises. Its Turing Frontier platform gives labs access to vetted experts across engineering, science, and enterprise domains, with expert-designed prompts and evaluation frameworks.
Best for: Agentic data, coding and STEM evaluation, and off-the-shelf data packs.
Strengths: Strong engineering and agentic focus, plus packaged datasets for reward modeling and post-training.
Limits: Much of the public detail is self-reported, so request references for your use case.
Pricing shape: Custom and quote-based.
7. Handshake AI
What it is: Handshake AI is the data arm of the Handshake career platform. It turns a network of students, PhDs, and domain experts into a source of training, evaluation, and RLHF data, and builds benchmarks and evaluation frameworks for frontier models and agents.
Best for: Academic and early-career expert supply across math, physics, and computer science, plus benchmark design.
Strengths: Deep sourcing reach through university networks and a clear evaluation focus.
Limits: Much of the expert pool skews toward academic and early-career talent, which may differ from senior practitioners. Revenue figures are unverified.
Pricing shape: Custom and quote-based.
8. Invisible Technologies
What it is: Invisible Technologies runs a Data Lab that sells RL environments, custom evaluations, supervised fine-tuning, and RLHF data, alongside a broader enterprise AI operations business.
Best for: Managed RLHF and fine-tuning programs, and teams that want operations support bundled in.
Strengths: Established RLHF delivery and a flexible managed model.
Limits: Some analysts describe a strategic shift toward enterprise operations, so confirm current frontier-lab capacity.
Pricing shape: Custom and quote-based.
9. Labelbox
What it is: Labelbox pairs its labeling platform with the Alignerr network of domain experts who evaluate, train, and improve models. Alignerr Connect is a marketplace for hiring vetted experts, and Labelbox Leaderboards use human raters for subjective generative AI evaluation.
Best for: Teams that want a labeling platform plus an expert network and managed evaluation in one place.
Strengths: Mature tooling, a large expert network, and a clear human-evaluation product.
Limits: Expert-pool size and earnings claims from third parties are hard to verify. Confirm which Alignerr experts fit your domain.
Pricing shape: Platform plus managed services, quote-based.
10. CleverX
What it is: CleverX positions itself as the expert intelligence layer for your AI. It supplies verified, practising industry experts, people who still do the job today, to train and evaluate AI models and agents. Its four offerings are Evaluations, where experts grade model or agent outputs against professional standards and record their reasoning; Rubrics, where experts define pass or fail criteria; Benchmarks, where experts build hard real-world tasks from their own work; and Demonstrations, where experts provide ideal worked answers and end-to-end agent workflows.
Best for: High-judgement evaluation, rubric design, benchmarks, and demonstrations in regulated and technical domains, where you need a senior practitioner’s standard rather than crowd volume.
Strengths: Six domains are available: finance and accounting, healthcare, law, engineering and manufacturing, insurance, and operations. Verification runs four checks per expert: a government-ID identity check, licence confirmation with the issuing body where relevant, a LinkedIn experience match, and a recorded interview in the expert’s field before you see their profile. It is a full service, so CleverX handles sourcing, validation, contracts, and pay. For a direct view, see CleverX vs Scale AI.
Limits: CleverX is focused on the expert-judgement layer, not high-volume labeling or broad data pipelines. Teams needing mass annotation will still pair it with a labeling platform.
Pricing shape: Custom and quote-based. The call to action on the page is “Book a call.”
Comparison table
| Provider | Primary strength | Best for | Pricing shape |
|---|---|---|---|
| Scale AI | End-to-end data infrastructure | Integrated pipelines across modalities | Custom / quote |
| Mercor | Large expert marketplace | Ongoing post-training programs | Custom / hourly |
| Surge AI | Premium RLHF and reasoning evals | Preference and reasoning data | Custom / quote |
| Appen | Global crowd, multi-modal | Broad labeling at scale | Custom / quote |
| Toloka | Preference data and languages | RLHF and reward modeling | Self-serve + custom |
| Turing | Agentic and STEM data | Coding and agent evaluation | Custom / quote |
| Handshake AI | University expert sourcing | Benchmarks and academic experts | Custom / quote |
| Invisible | Managed RLHF and fine-tuning | Bundled operations support | Custom / quote |
| Labelbox | Platform plus expert network | Tooling plus managed evals | Platform + services |
| CleverX | Verified practising experts | High-judgement evals and rubrics | Custom / quote |
How to choose the right one
Match the provider to the deliverable. If you need volume labeling across many modalities, Scale AI, Appen, and Labelbox are built for that. If you need RLHF and preference data at quality, look at Surge AI, Toloka, Mercor, and Invisible. If you need agentic or STEM evaluation, Turing and Handshake AI are worth a look.
If your work hinges on whether a model meets a professional standard, in finance, healthcare, law, engineering, insurance, or operations, prioritise verification and practitioner depth. That is where CleverX is designed to fit: verified, practising experts for evaluations, rubrics, benchmarks, and demonstrations, with identity, licence, LinkedIn, and recorded-interview checks before you ever see a profile. Many teams run a hybrid model, using a labeling platform for scale and a verified-expert provider for the hard judgement calls.
Whatever you choose, align your decision with a quality framework. The NIST AI Risk Management Framework is a useful reference for governing data quality and evaluation in higher-stakes domains.
Frequently asked questions
What is the best Scale AI alternative for expert data in 2026? There is no single best option, because it depends on the work. Mercor, Surge AI, and Handshake AI are strong for large post-training and RLHF programs. Labelbox and Appen suit broad labeling and evaluation at scale. CleverX fits teams that need verified practising experts for high-judgement evaluations, rubrics, benchmarks, and demonstrations in regulated and technical domains.
Why do AI teams look for alternatives to Scale AI? Teams look for alternatives when they need a different mix of expert depth, domain coverage, verification rigor, speed, or commercial terms. Some want specialists who still practise their profession today. Others want a particular pricing shape or a vendor that is not tied to a specific lab. Scale AI remains a capable incumbent, so the right move is to match the provider to the task.
What is the difference between data labeling and expert data? Data labeling usually means annotating examples at scale, often through large crowds or managed workforces. Expert data means judgement from qualified professionals, such as a physician grading a clinical answer or an engineer scoring a design. Labeling builds volume. Expert data builds the hard evaluations, rubrics, and demonstrations that test whether a model meets a professional standard.
How do I verify that expert data really comes from experts? Ask each vendor how it confirms identity and credentials, and whether a human with real domain experience did the work. Strong signals include government-ID identity checks, licence confirmation with the issuing body, a verified work and LinkedIn history, and a recorded interview in the expert’s field. Request audit trails and reviewer reasoning so you can trace every judgement.
How much does expert data for AI cost? Most providers in this space price on a custom or quote basis, because cost depends on domain, difficulty, volume, and turnaround. Specialist medical, legal, or engineering work costs more than general labeling. Confirm the pricing shape, rate structure, and quality controls with each vendor directly, since figures change often and vary by project.
Can CleverX replace Scale AI entirely? Not for every job. Scale AI and large labeling platforms are built for high-volume annotation and broad data pipelines. CleverX is focused on verified practising experts for evaluations, rubrics, benchmarks, and demonstrations in domains like finance, healthcare, law, engineering, insurance, and operations. Many teams use a labeling platform for volume and CleverX for the high-judgement expert layer.