Abridge vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
Pittsburgh ambient clinical AI (founded by cardiologist) at $5.3B valuation Jun 2025; $800M total ($300M a16z/Khosla Series E) deployed at UPMC 12K clinicians, Mayo Clinic, Johns Hopkins competing with Nuance DAX for physician documentation.
About
Abridge is a Pittsburgh, Pennsylvania-based healthcare AI clinical documentation platform — backed with approximately $800 million in total funding including a $300 million Series E in June 2025 led by Andreessen Horowitz and Khosla Ventures at a $5.3 billion valuation, following a $250 million Series D just four months prior at a $2.8 billion valuation — providing physicians, nurses, and care teams at 150+ health systems with AI that automatically converts patient-clinician conversations into structured clinical notes, saving physicians an average of 3 hours daily and generating high-quality documentation from UPMC (scaling to 12,000 clinicians enterprise-wide), Mayo Clinic, Johns Hopkins, and Emory Healthcare. Abridge's AI is trained on a proprietary dataset of over 1.5 million medical encounters, delivering specialty-specific documentation through deep Epic EHR integration. Founded in 2018 by cardiologist Dr. Shiv Rao.
Serverless GPU cloud platform for AI/ML with Python-native deployment and per-second billing; developer-favorite scaling from zero competing with Replicate and Beam for AI compute.
About
Modal is a serverless cloud computing platform purpose-built for AI and machine learning workloads — providing on-demand GPU compute that scales instantly from zero with per-second billing, container management, distributed training support, and a Python-native developer experience that makes running ML workloads in the cloud feel as simple as running code locally. Founded in 2021 in New York City and backed by Redpoint Ventures and other investors, Modal has grown rapidly as AI development has accelerated demand for flexible, developer-friendly GPU infrastructure.\n\nModal's developer experience is its primary differentiator — engineers write Python functions decorated with @modal.function() and deploy them to the cloud with a single command, with Modal handling container building, GPU provisioning, auto-scaling, and execution. The platform supports training jobs that need distributed compute across multiple GPUs, model serving endpoints that scale to zero when unused (eliminating idle GPU costs), and batch inference jobs that process large datasets. The per-second billing model means developers pay only for actual compute time, not provisioned instances.\n\nIn 2025, Modal competes in the AI infrastructure market with Replicate, Beam, Banana, and major cloud providers' managed ML services (AWS SageMaker, Google Vertex AI, Azure ML) for serverless GPU compute. The market for AI-specific cloud infrastructure has grown dramatically as the number of ML engineers deploying models to production has expanded — traditional cloud providers require significant DevOps expertise to use GPU instances effectively, while Modal's Python-native approach reduces the barrier to entry. Modal has attracted a strong developer following among AI researchers and ML engineers building production AI applications. The 2025 strategy focuses on growing the developer community, adding enterprise features (dedicated GPU capacity, private networking, compliance), and expanding the hardware options available (H100 GPUs, custom accelerators).
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