Scale AI Alternatives for Enterprise AI Teams

Meta’s $14.3 billion acquisition of a 49% stake in Scale AI has forced enterprise AI teams to reassess their data annotation partnerships. The June 2025 deal triggered customer departures from Google, OpenAI, and xAI—organizations unwilling to share proprietary training data with a Meta-controlled vendor.

The deeper issue isn’t platform capabilities or vendor ownership. The biggest blocker to AI advancement is a people problem. Annotation platforms have scaled software infrastructure. They haven’t solved access to the engineers, developers, and domain experts required for the work that actually moves models forward: RLHF ranking, code evaluation, safety red-teaming. The constraint is human, not technical.

This guide evaluates the leading Scale AI alternatives across platform capabilities, annotator quality, pricing transparency, and vendor independence.

Scale AI’s Neutrality Is Gone

Scale AI’s transformation from neutral market leader to Meta subsidiary represents the most significant vendor risk event in data annotation history. Founder Alexandr Wang departed to join Meta as Chief AI Officer. Interim CEO Jason Droege now manages a company that cut 14% of its workforce in July 2025. That’s 200 full-time employees and 500 contractors.

Annotation vendors handle proprietary training data that reveals model architecture decisions, strategic priorities, and competitive advantages. Sharing that data with a vendor controlled by a direct competitor is why Google, OpenAI, and xAI left.

Quality Concerns Predate the Acquisition

Meta’s own TBD Labs researchers view Scale AI’s data as low quality, expressing preference for Surge AI and Mercor. This perception, from the company that now controls Scale AI, signals systemic issues beyond ownership structure.

Annotation error rates average 10% across search relevance tasks. MIT CSAIL discovered ImageNet contains a 6% error rate that skewed model rankings for years. Poor annotations create cascading failures. Models train successfully but fail in production.

Defense Contracts Complicate Commercial Relationships

Scale AI serves as prime contractor for the Pentagon’s Thunderforge program with over $440 million in documented military contracts. The company holds FedRAMP High Authorization and operates on classified networks.

Commercial buyers share annotation infrastructure with defense AI development. Some organizations have data handling requirements or geopolitical sensitivities that make this a problem.

Pricing Opacity

Enterprise contracts typically range from $100,000 to $400,000+ annually, with no public pricing schedule. Hidden costs compound unpredictability: quality rework requiring 5-7 revision cycles, and per-label pricing that incentivizes over-annotation. 30% of AI development budgets go to data labeling.

Vendor Comparison

Labelbox

Best for: Enterprise teams seeking Scale AI-comparable capabilities without Meta ownership. labelbox.com

Labelbox has $189 million in funding and near-unicorn valuation. It’s the most direct enterprise alternative to Scale AI.

Platform

Annotation software plus managed labeling services through its Alignerr community of 10,000+ vetted domain experts. Walmart, Procter & Gamble, Genentech, and Adobe use Labelbox for production workflows processing 50+ million monthly annotations.

Labelbox is Google Cloud’s official partner for LLM human evaluations (April 2024). A $950 million US Air Force JADC2 contract demonstrates defense-grade capabilities.

Strengths

• Google Cloud integration for LLM evaluation workflows
• Enterprise customer base across industries
• Government contracts without Meta ownership complications
• Annotation tooling for text, image, video, and audio

Limitations

Labelbox’s LBU (Labelbox Unit) billing model makes monthly spend difficult to forecast. Costs scale quickly with usage. Procurement teams report challenges during contract negotiations.

The platform deprecated its custom editor, DICOM viewer for medical imaging, and image fine-tuning capabilities in late 2024.

Pricing

Custom quotes. $100,000-$400,000+ annually for enterprise scope.

SuperAnnotate

Best for: Teams requiring custom annotation interfaces. superannotate.com

SuperAnnotate differentiates through fully customizable annotation interfaces. A drag-and-drop builder for bespoke workflows, not fixed templates.

Platform

Ranked #1 on G2 for data labeling (98/100 score). November 2024 Series B brought $36 million from NVIDIA, Databricks Ventures, and Dell Technologies Capital.

400+ vetted labeling teams across 18 languages. Strong in computer vision, autonomous driving, and medical imaging. Customers include Databricks, Canva, Motorola Solutions, IBM, and Qualcomm.

Strengths

• Customizable annotation interfaces without engineering requirements
• RLHF workflows, SFT, and agent evaluation support
• Computer vision and medical imaging specialization
• NVIDIA and Databricks as investors

Limitations

Steeper learning curve. Data exploration requires SQL/Python knowledge.

400+ teams is smaller than competitors with 10,000+ annotators. High-volume projects may face throughput constraints.

Pricing

Custom enterprise pricing. Lower entry points than Labelbox or Scale AI.

Snorkel AI

Best for: Data science teams with classification projects. snorkel.ai

Snorkel AI uses programmatic labeling: writing labeling functions that automatically annotate data subsets instead of labeling point-by-point.

Platform

Stanford AI Lab origin. Claims 10-100x faster development for appropriate use cases. $100 million Series D in May 2025 at $1.3 billion valuation. Investors include In-Q-Tel, BlackRock, and Accenture.

Five of the top ten US banks, BNY Mellon, Chubb Insurance, and Intel use Snorkel for document analysis, compliance, and classification. 90%+ cost reduction for suitable projects. Data stays internal.

Strengths

• Dramatically faster for classification problems
• No external annotator access, data remains internal
• Document classification, compliance, structured data
• Financial services validation

Limitations

Programmatic labeling only works for classification. RLHF ranking, code evaluation, and creative judgment still require human annotators.

Requires data science expertise. Teams without ML engineering resources cannot implement labeling functions effectively. Gartner reviews note the platform is “not as reliable or enterprise-ready as expected.”

Pricing

$50,000-60,000+ annually to start. Enterprise contracts scale higher.

Surge AI

Best for: RLHF and LLM training quality. surgehq.ai

Surge AI bootstrapped to $1.2 billion in 2024 revenue, surpassing Scale AI’s $870 million. No external capital.

Platform

Serves OpenAI, Google, Microsoft, Meta, Anthropic, and the US Air Force. Approximately one million annotators, many with advanced degrees. Founder Edwin Chen built the company on premium positioning: higher annotator pay for higher quality outputs.

Current discussions value Surge AI at $15-25 billion for potential 2025 fundraising with Andreessen Horowitz, Warburg Pincus, and TPG.

Strengths

• Highest demonstrated quality for RLHF annotation
• Every major frontier lab as a customer
• Premium annotator compensation attracts qualified talent
• Revenue exceeds Scale AI without venture funding

Limitations

Labor practices transparency concerns. May 2025 class-action lawsuit alleges worker misclassification and improper wage withholding. The company operates multiple subsidiary platforms (DataAnnotation.Tech, TaskUp, GetHybrid) with unclear ownership relationships.

An internal training document left public on Google Docs in July 2024.

Pricing

At or above Scale AI’s enterprise range.

AfterQuery

Best for: Frontier labs requiring expert-curated datasets. afterquery.com

AfterQuery focuses on human-curated datasets impossible to find online or synthetically generate.

Platform

Y Combinator 2024 batch. Partners with domain experts from Berkeley AI Research, Allen Institute for AI, and Stanford AI Laboratory. May 2025 VADER benchmark: 174 real-world software vulnerabilities for LLM evaluation.

Focus areas: finance (private equity, hedge funds, investment banking), legal, medicine, and government.

Strengths

• Expert-first model for specialized domains
• Research partnerships with leading AI institutions
• Data that cannot be synthetically generated
• Frontier model development fit

Limitations

Seed-stage company with $500,000 raised. Limited track record and scale. Best for cutting-edge models requiring unique training data, not standard annotation workflows.

Pricing

Custom, based on data complexity and domain expertise.

Appen Is a Cautionary Tale

Best for: High-volume, multilingual annotation (evaluate financial stability first). appen.com

Platform

One million+ contributors across 170+ countries and 180+ languages. Unmatched for high-volume, language-diverse annotation.

The Collapse

Stock down 99% from 2020 peak. AU$42.44 to roughly $0.56. Market cap shrunk from $4.3 billion to $148 million.

Google terminated its $82.8 million annual contract in March 2024, roughly 30% of Appen’s revenue. Former employees cite weak quality controls, disjointed organization, and failure to pivot for generative AI. Three CEOs in 24 months.

Recommendation

Evaluate financial stability risk before signing. Appen may offer attractive pricing to win business. Vendor continuity is the concern.

Cloud Platforms Serve Narrow Use Cases

Amazon SageMaker Ground Truth

Best for: AWS-native organizations. aws.amazon.com/sagemaker/groundtruth

Three workforce options: Mechanical Turk, private teams, or third-party vendors. Automated labeling can reduce costs by up to 70%.

Pricing: $0.08 per object for the first 50,000 monthly. Free tier of 500 objects monthly for two months. 

Limitations: English-only interface. Limited pre-built templates for specialized domains. Quality depends on workforce selection. Ground Truth Plus provides expert teams for healthcare and autonomous vehicles but requires custom quotes.

Google Vertex AI Data Labeling

cloud.google.com/vertex-ai

Google deprecated its managed human labeling service in July 2024. Users access third-party partners like Labelbox and Snorkel through Google Cloud Marketplace.

Annotation Platforms Have a People Problem

General crowd workers can draw bounding boxes around pedestrians. They cannot do RLHF, code evaluation, or domain-specific annotation.

The Expert Gap Is Quantifiable

Thomson Reuters’ CoCounsel legal AI required 30,000 legal questions refined by lawyers over six months. That’s 4,000 hours of specialized work. Expert STEM annotation commands $40+ per hour, versus $20 for general tasks. Medical data labeling costs 3-5x more than general imagery.

Code Annotation Requires Senior Engineers

Short, simple coding tasks can use junior developers. Longer, complex tasks require senior expertise to catch subtle bugs, evaluate architecture, and assess production-readiness.

AI agent development in professional domains requires dual expertise: coding skills plus domain knowledge in medicine, law, or finance. This combination is scarce.

RLHF Demands Nuanced Judgment

RLHF requires ranking responses on helpfulness, factual correctness, safety, tone, and cultural sensitivity. Safety policies involve interpretation. Increasing helpfulness often conflicts with increasing harmlessness.

OpenAI’s July 2025 ChatGPT Agent System Card describes automated monitors, human-in-the-loop confirmations, and watch modes for high-risk contexts. These workflows require annotators capable of sophisticated reasoning.

Red Teaming Requires Adversarial Expertise

Anthropic used adversarial testing for Constitutional AI. Meta employed internal teams for LLaMA 2 safety testing. Google DeepMind implemented red teaming for Gemini.

Effective red teaming requires annotators who think adversarially while maintaining nuanced judgment. That profile is closer to senior security engineers than crowd workers.

The Talent Gap Platforms Can’t Solve

Expert technical talent for high-stakes annotation doesn’t exist in traditional crowd worker pools. The vendors best positioned for 2026 have access to qualified developers, engineers, and domain specialists, not just platform capabilities.

Evaluating annotation partners now requires assessing their technical talent sourcing strategy alongside their software.

What to Ask Before Signing a Contract

Quality Metrics and QA Processes

Request accuracy metrics for comparable projects. Ask for sample outputs and error analysis. Understand QA workflow: review cycles, inter-annotator agreement thresholds, disagreement resolution.

Annotator Expertise Verification

For RLHF, code evaluation, or domain-specific work: How are annotators vetted? What credentials are required? How is domain expertise validated? What’s the ratio of expert annotators to general crowd workers?

Security Certifications and Data Handling

Minimum: SOC 2 Type II. For regulated industries: HIPAA, GDPR, CCPA compliance documentation. Where is data stored? Who has access? How long is it retained?

Pricing Transparency

Request pricing breakdowns: per-label costs, minimum commitments, overage charges. Who pays for quality rework? Total cost of ownership matters more than unit rates.

Vendor Independence

Ownership structure. Major customer concentration. Conflicts if vendor serves your competitors. Data portability if the relationship ends.

FAQ

The Market Rewards Quality and Independence

Scale AI’s ownership crisis accelerated trends already in motion: quality over scale, expert annotators over crowd workers, vendor independence over platform lock-in. The annotation market is projected at $17-29 billion by 2030-2032. RLHF and domain-specific annotation command premium pricing.

The vendors best positioned for 2026 have access to qualified technical talent for high-stakes annotation. Platform features matter, but the constraint is human expertise. The talent powering annotation workflows is the competitive differentiator.

The question for AI/ML teams has shifted from “which vendor has scale?” to “which vendor can access the developers, engineers, and domain specialists our training data requires?”