Paige AI vs Insitro

Insitro has a disclosed valuation of $2.2B, while Paige AI's valuation is not publicly available, making a direct size comparison difficult. Insitro employs 300 people.

Insitro has raised more in total funding at $743M, compared to Paige AI's $241M — a gap of $502M. Combined, the two companies have completed 4 known funding rounds.

Insitro leads with an Awaira Score of 73/100, while Paige AI sits at 65/100. That 8-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Paige AI was founded by Leo Grady in 2018. Insitro was founded by Daphne Koller in 2018. Visit each company's profile on Awaira for a full founder biography.

Paige AI: Paige AI develops AI-powered pathology software that assists pathologists in identifying cancer and other diseases in digital whole-slide images. The platform received the first-ever FDA authorization for an AI-based primary diagnostic tool in pathology, a milestone that establishes regulatory precedent for clinical AI deployment in histopathology workflows.\n\nThe company raised approximately 125 million USD and partners with major academic medical centers and pathology laboratories to integrate its diagnostic AI into clinical workflows. Paige has expanded beyond prostate cancer detection into additional cancer types and is building a multi-cancer detection platform intended to function as a comprehensive AI layer across pathology departments.\n\nDigital pathology combined with AI represents one of the clearest near-term commercialization opportunities in medical AI, as the transition from glass slides to whole-slide imaging creates a digital substrate that AI can operate on at scale. Paige FDA clearance creates a significant regulatory moat that competitors must match before selling comparable diagnostic tools in the US market, giving the company a durable first-mover advantage in the AI-augmented pathology workflow. Insitro: Insitro is an AI-driven drug discovery company founded in 2018 that applies machine learning to accelerate pharmaceutical development. The company combines computational biology, artificial intelligence, and wet-lab experimentation to identify and validate drug targets more efficiently than traditional methods. Insitro's platform uses proprietary algorithms to analyze complex biological data, predict drug efficacy and safety profiles, and optimize candidate selection across therapeutic areas including metabolic disease, oncology, and immunology. The company has raised $743 million across multiple funding rounds, achieving a valuation of $2.2 billion as of its Series C stage. This positions Insitro among well-capitalized AI healthcare startups addressing the structural inefficiencies in drug discovery. The company partners with pharmaceutical institutions to apply its technology to their pipelines, utilizing both internal discovery programs and collaborations. Insitro's competitive approach differs from pure software platforms by integrating experimental validation, reducing the translation gap between computational predictions and real-world drug performance. The company operates in a crowded but expanding market of AI drug discovery platforms competing against both established pharma AI initiatives and other venture-backed startups. Its growth trajectory reflects investor confidence in AI-enabled drug discovery models, though clinical validation remains ongoing for internally developed candidates. Insitro uniquely combines machine learning with integrated wet-lab capabilities rather than operating as pure software, bridging computational predictions to experimental validation.

Both Paige AI and Insitro launched in 2018. Same year, but even a few months' head start matters in AI — early movers lock in data, talent, and customer relationships fast.

Paige AI has about 100-500 employees; Insitro has about 300. A bigger team usually means more revenue or heavier VC backing, but in AI, small teams can build at massive scale.

Yes — they're direct rivals. Both Paige AI and Insitro compete in AI Healthcare, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.