SteadyMD vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
SteadyMD built B2B telehealth infrastructure with 600+ clinicians in 50 states (~5M revenue); acquired by DocGo (Oct 2025) to become the clinical backend for digital health platforms.
About
SteadyMD was founded in 2016 in St. Louis with a mission to build the infrastructure layer that enables healthcare companies to deliver telehealth services without building clinical operations from scratch. The company recognized that the fastest-growing segment of the telehealth market was not direct-to-consumer care but B2B infrastructure — the clinical staffing, licensing, credentialing, compliance, and workflow systems that other healthcare companies need to operate virtual care programs at scale. SteadyMD built this stack as a managed service, enabling digital health companies, health systems, and employers to launch and scale telehealth programs rapidly.\n\nSteadyMD's platform provides healthcare organizations with access to a network of more than 600 clinicians — physicians, nurse practitioners, and physician assistants — who are licensed across all 50 states and available for virtual care delivery. Clients integrate SteadyMD's clinical workforce and telehealth operating system into their own products, using SteadyMD as an outsourced clinical operations partner rather than building in-house clinician networks. This white-label infrastructure model serves clients across consumer health, chronic disease management, occupational health, and behavioral health — wherever organizations need scalable, compliant clinical capacity without the overhead of direct employment.\n\nSteadyMD was acquired by DocGo in October 2025, a publicly traded mobile health and medical transportation company, in a transaction that brings SteadyMD's telehealth infrastructure capabilities under a larger multimodal healthcare services platform. Prior to the acquisition, SteadyMD generated approximately $25 million in annual revenue, demonstrating durable commercial traction in the B2B telehealth infrastructure segment. The DocGo acquisition positions SteadyMD's technology and clinician network as a foundation for DocGo's expansion into virtual care delivery, combining telehealth infrastructure with DocGo's existing in-person mobile health operations.
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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