Alternatives to Scale: A buyer's guide to data annotation and evaluation solutions

The human data infrastructure powering AI is undergoing a shift. As models grow more capable, they become increasingly dependent on human input throughout the development cycle. Progress now hinges less on dataset size and more on the quality, relevance, and rigour of the feedback used to train, align, and evaluate.

The stakes couldn't be higher. On June 12 2025, Meta confirmed a $14 billion investment for a 49% non-voting stake in Scale AI and hired CEO Alexandr Wang to run its new "superintelligence" group. This news has already prompted customers like Google to pause work with the platform, driving many teams to explore alternatives that offer greater independence and control.

So, who are those alternatives?

We look at the full landscape of human data infrastructure, from platform-based solutions that give you direct access to participants, to fully managed services that handle everything for you. Whether you're just getting started or looking to diversify your approach, these are the Scale alternatives ready to shape the way AI is being built today. 

The evolution of human feedback for AI

The AI development playbook has changed. Instead of chasing ever-larger datasets, progress is more dependent on the quality and relevance of human feedback. That’s especially true for large language models and multimodal systems, where the gap between lab performance and real-world success often comes down to how well they're trained, aligned, and evaluated with the right feedback.

Why the right people matter for the right tasks

Not all human feedback is created equal. A senior software engineer reviewing code generation outputs provides different insights compared to a Spanish language expert evaluating translation accuracy. The most successful AI teams match evaluators to tasks by finding people who understand the context of what they're evaluating and can identify problems that matter in the real world.

Domain experts bring technical knowledge that general evaluators are more likely to miss. Representative users provide real-world perspectives that benchmarks can't capture, revealing when technically accurate responses miss cultural context or when edge cases are actually common in daily use.

The hidden costs of cutting corners

Compromising on human feedback quality creates technical debt that compounds over time. Models trained on low-quality feedback develop unpredictable behaviors and become overfitted to their evaluators' biases rather than learning patterns that hold up in the real world.

Poor quality undermines trust and slows everything down – rollouts stall, sign-offs take longer, and competitive advantages slip away. The companies seeing the biggest AI wins have built reliable pipelines for getting high-quality human intelligence into their development process.

What is Scale AI’s approach?

Scale AI built its business on a full-stack platform with managed annotation services. Scale operates as a one-stop solution for AI teams that want to outsource their data operations.

The company handles high-volume production workloads with established processes that can manage complex annotation projects across the full AI development lifecycle. Its managed service model means dedicated teams handle everything from workforce management to quality control.

Scale’s approach comes with trade-offs, however. Operations run as a black box with limited visibility into who's actually doing the work or how decisions are made. The model typically requires enterprise-scale contracts and longer engagement timelines, which can be challenging for teams that need to move fast or start small.

Scale works for organizations that prefer to fully outsource their human data operations and have the budget for managed services. The recent Meta investment and subsequent customer departures show why many teams are exploring alternatives that offer more flexibility, transparency, and control over their human feedback processes.

How to evaluate your options

When you're choosing a human data provider, there are five factors that will make or break your AI development process.

Expertise and coverage

Can they actually find the people you need? A provider's value lies in their ability to connect you with healthcare professionals for medical AI, software engineers for code generation models, or STEM experts for scientific evaluations. The depth and breadth of their participant pool determines what's possible.

Speed and scalability

AI development moves fast. While some providers take weeks to deliver initial data, others can start in hours. Look for providers who can handle both a 100-person pilot today and scale to thousands tomorrow across specialties, skills, and backgrounds.

Integration and workflow

Choose vendors that fit into your existing workflow. Providers with seamless API integrations let you continue working with your current tools, whereas others might require you to restructure your processes around their platforms.

Operational efficiency

Operational efficiency is the hidden cost everyone forgets to calculate. How much time does your team spend managing the vendor versus actually using the data? Some providers need constant hand-holding while others just work.

Commercial flexibility

Can you start with a small budget and scale up, or do they demand six-figure commitments upfront? Flexible platforms let you iterate more quickly without getting locked into rigid contracts—this is particularly important where AI development moves quickly and roadmaps evolve with greater frequency.

Increasingly, organizations are prioritizing responsible AI development. If you're serious about ethical practices, you'll want to examine how providers treat and compensate their workforce, as not all operate to the same standards.

Alternatives to Scale

1) Prolific

Prolific provides access to over 200,000 verified participants across more than 40 countries and 80-plus languages. Teams can work via a self-serve platform or can opt for managed services depending on their workflow needs.

Strengths

• Speed: Access production-grade data in hours, not weeks
   
• Expertise: Domain Experts (e.g. cybersecurity, finance) and representative users for real-world evaluation
   
• Targeting: Precisely matched personas for alignment, fine-tuning, and specialised AI tasks
   
• Integration: APIs connect directly into your existing tools
   
• Flexibility: Switch between managed and self-serve modes depending on your use case
   
• Pricing: Transparent, pay-as-you-go pricing for self-serve platform access, with customized quotes for managed services

What sets Prolific apart is its ability to match the right participants to the task. The verification process ensures access to real people with relevant expertise, reducing noise and improving the quality of feedback used to train and evaluate models.

2) Surge AI

Surge AI claims the largest RLHF (Reinforcement Learning from Human Feedback) platform and focuses on advanced AI model training. It offers a fully managed service with annotation teams and hands-on project management.

Strengths

• Specialization: Strong focus on RLHF and LLM alignment
   
• Service model: Dedicated account teams and project oversight
   
• Scale: High-volume capabilities with consistent task execution
   
• Quality controls: White-glove service, SLAs, and an "elite" contributor network
   
• Focus areas: Complex NLP and generative AI use cases

Surge is a fit for teams seeking RLHF expertise and full-service support, typically requiring enterprise-scale budgets and longer engagement timelines.

3) Labelbox

Labelbox offers annotation infrastructure with a platform-first approach. Teams can use its software to build internal workflows and access its workforce marketplace when needed.

Strengths

• Tooling: Advanced annotation tools, version control, and automation features
   
• Workforce options: Choose between managing your own labelers or using Labelbox’s marketplace
   
• Integration: Strong API and workflow integration support
   
• Flexibility: Works across a range of plans, from startup to enterprise
   
• New additions: 'Alignerr Connect' for hiring expert AI trainers directly

Labelbox may suit teams that want to maintain internal control over their annotation pipeline but still need access to software infrastructure and workforce options when required.

4) Appen

Appen provides managed annotation services using a large-scale crowd workforce and operates across a wide range of languages and project types.

Strengths

• Reach: Global contributor network across 170+ countries
   
• Language support: Covers 235+ languages for international projects
   
• Platform: ADAP platform handles diverse data types (text, image, audio, video, geospatial)
   
• Project types: Geared towards high-volume annotation and multilingual workloads

Appen is suitable for teams looking to fully outsource large-scale annotation projects, particularly when multilingual coverage is important or broad geographic reach is required.

5) Invisible Technologies

Invisible builds dedicated teams for complex annotation workflows that fall outside typical platform models. It focuses on bespoke operations and high-touch service.

Strengths

• Custom workflows: Tailored for non-standard or multi-step tasks
   
• Dedicated teams: Operates without crowdsourcing, using assigned teams
   
• Edge case handling: Focus on specialized, less predictable tasks
   
• Project design: White-glove service with fully managed setup
   
• Engagement model: Typically geared toward longer-term enterprise projects

Invisible is worth considering for teams working on unique, high-complexity annotation problems that require a hands-on approach and custom process design.

Choose the right provider

Start by assessing your primary use cases against the evaluation criteria covered, such as expertise needs, speed requirements, integration preferences, operational efficiency, and commercial flexibility. This will help you narrow down which providers match your specific situation.

Request demos from two-to-three providers that align with your needs and run small pilot projects to test how well they work with your team and data. Don't commit to long-term contracts without proving the fit first. The human data infrastructure you choose will shape what your AI can achieve, so take the time to get it right.

Data annotation and evaluation solutions with Prolific

The human data infrastructure you choose will shape what your AI can achieve. With the recent changes in the market, now is the perfect time to evaluate your options and find a provider that matches your specific needs.

• Learn about Prolific's approach to human data: Discover how our Human Intelligence Layer integrates verified domain experts and representative users into your AI development process
• Talk to an expert about how Prolific can help: Get personalized guidance on building the right human feedback pipeline for your team's goals

Whether you're just getting started with human data or looking to diversify away from existing providers, Prolific's combination of speed, expertise, and flexibility makes it easy to get the quality feedback your AI needs to succeed.