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Protégé Pricing & Plans 2026

Complete pricing guide for Protégé. Compare all plans, analyze costs, and find the perfect tier for your needs.

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💎1 Paid Plans
⚡No Setup Fees

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Enterprise

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mo

  • ✓Custom data sourcing across healthcare, video, audio, speech, and spatial intelligence domains
  • ✓Full AI lifecycle coverage: pre-training, post-training, fine-tuning, and evaluation datasets
  • ✓Consultative engagement with dedicated data sourcing team
  • ✓Data provenance tracking and rights protection
  • ✓Access to Healthcare AI Evaluation Datasets and Benchmarks
  • ✓Two-sided marketplace participation for data providers
  • ✓Pricing tailored to data volume, modality, exclusivity, and engagement scope
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Pricing sourced from Protégé · Last verified March 2026

Is Protégé Worth It?

✅ Why Choose Protégé

  • • Backed by $55M in Series A funding (including $30M extension led by a16z) signaling strong investor confidence and runway
  • • Trusted by enterprise customers including Siemens Healthineers, validated by named testimonials from medical imaging leadership
  • • Powers third-party benchmarks including Vals AI healthcare evaluations for clinical documentation and medical coding
  • • Covers four distinct AI lifecycle stages (pre-training, post-training, fine-tuning, evaluation) rather than focusing on just one
  • • Strong focus on uncontaminated evaluation data — datasets explicitly designed not to overlap with training data
  • • Specializes in non-public proprietary data, addressing the actual bottleneck for frontier model improvements

⚠️ Consider This

  • • Enterprise-only pricing with no transparent tiers, making it inaccessible to indie developers or small startups
  • • No self-serve data catalog — every engagement appears to require a sales conversation and custom data sourcing
  • • Domain coverage is broad but uneven; healthcare appears far more mature than other verticals like spatial/physical intelligence
  • • Relatively young company (Series A stage) with shorter operating history than incumbent data platforms like Scale AI
  • • Limited public documentation about technical integration, dataset formats, or API access on the marketing site

What Users Say About Protégé

👍 What Users Love

  • ✓Backed by $55M in Series A funding (including $30M extension led by a16z) signaling strong investor confidence and runway
  • ✓Trusted by enterprise customers including Siemens Healthineers, validated by named testimonials from medical imaging leadership
  • ✓Powers third-party benchmarks including Vals AI healthcare evaluations for clinical documentation and medical coding
  • ✓Covers four distinct AI lifecycle stages (pre-training, post-training, fine-tuning, evaluation) rather than focusing on just one
  • ✓Strong focus on uncontaminated evaluation data — datasets explicitly designed not to overlap with training data
  • ✓Specializes in non-public proprietary data, addressing the actual bottleneck for frontier model improvements

👎 Common Concerns

  • ⚠Enterprise-only pricing with no transparent tiers, making it inaccessible to indie developers or small startups
  • ⚠No self-serve data catalog — every engagement appears to require a sales conversation and custom data sourcing
  • ⚠Domain coverage is broad but uneven; healthcare appears far more mature than other verticals like spatial/physical intelligence
  • ⚠Relatively young company (Series A stage) with shorter operating history than incumbent data platforms like Scale AI
  • ⚠Limited public documentation about technical integration, dataset formats, or API access on the marketing site

Pricing FAQ

What types of data does Protégé provide?

Protégé sources real-world, proprietary data across five primary domains: healthcare (including multimodal patient journey data, clinical documentation, and medical imaging), video, audio and speech, spatial and physical intelligence (including motion capture), and other industry-specific verticals. The platform supports all four stages of the AI development lifecycle, from massive diverse pre-training datasets to narrowly curated fine-tuning data and uncontaminated benchmark datasets. Unlike public scrape-based corpora, Protégé focuses specifically on private and proprietary data that is not otherwise available.

How much does Protégé cost?

Protégé uses enterprise pricing that is not published on its website, meaning all engagements require direct contact with their sales and partnerships team. Pricing is presumably tailored to the volume, modality, and exclusivity of the data being licensed, as well as the scope of the consultative work needed to source and prepare it. This model is consistent with other premium AI data platforms targeting frontier labs and enterprise customers, though it makes the platform inaccessible to smaller teams and individual researchers. Prospective buyers should expect a custom quote process rather than a public pricing page.

Who founded Protégé and how is it funded?

Protégé operates under the corporate name Protege Health, Inc. and is headquartered at 169 Madison Ave, New York, NY. The company announced a $25 million Series A in February 2026 to expand its AI training data platform, followed by a $30 million Series A extension led by Andreessen Horowitz (a16z), bringing total Series A funding to approximately $55 million. The extension was driven by rapid adoption across healthcare, media, audio, motion capture, and other verticals as AI companies increasingly need high-quality, non-public data.

How does Protégé differ from Scale AI or other data labeling platforms?

Based on our analysis of 870+ AI tools, Protégé differs from labeling-first platforms like Scale AI, Labelbox, and SuperAnnotate by focusing on data sourcing rather than annotation of existing data. Its core value proposition is connecting model builders with genuinely proprietary, non-public data held by hospitals, studios, and enterprises, with rights and provenance protections built in. Customer testimonials describe Protégé as a hands-on internal partner that helps identify the right data for specific problems, rather than a self-serve data catalog. This makes it more comparable to a specialized data brokerage than to a labeling tools vendor.

Can data providers monetize their datasets through Protégé?

Yes — Protégé runs a dedicated 'For Data Providers' program that allows organizations holding proprietary datasets or content to generate revenue by licensing that data to AI builders. The platform emphasizes maintaining clear rights protections and provenance tracking throughout the exchange, which is particularly important for regulated domains like healthcare. Data providers can participate across the same five domains the platform serves: healthcare, video, audio and speech, spatial and physical intelligence, and other domains. This two-sided marketplace model is one of the platform's distinguishing features compared to pure-buy-side data vendors.

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Scale AI Pricing

Scale AI provides a data-centric infrastructure platform that accelerates AI development by combining human-in-the-loop data labeling with advanced automation. The platform supports the full AI data lifecycle—from annotation and curation to RLHF (Reinforcement Learning with Human Feedback) and model evaluation—serving enterprise customers including Meta, Microsoft, OpenAI, Toyota, and the U.S. Department of Defense. Scale's platform integrates with major ML frameworks and cloud providers (AWS, GCP, Azure), offers programmatic APIs for pipeline automation, and provides specialized workflows for computer vision, NLP, sensor fusion, and generative AI fine-tuning. Unlike competitors such as Labelbox or Snorkel AI, Scale differentiates through its managed workforce of over 240,000 contractors combined with proprietary quality-assurance algorithms, enabling high-throughput labeling at enterprise scale with configurable accuracy guarantees.

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