Side-by-side comparison of AI visibility scores, market position, and capabilities
Virtual addiction treatment platform for alcohol and opioid use disorder, Ann Arbor MI, raised $118M+. Combines medication-assisted treatment with digital coaching.
Workit Health is an Ann Arbor, Michigan-based virtual addiction treatment company founded in 2015 that provides evidence-based treatment for opioid use disorder (OUD) and alcohol use disorder (AUD) through a telehealth platform. The company has raised over $118 million and operates in multiple US states, delivering medication-assisted treatment (MAT) including buprenorphine prescribing alongside behavioral health coaching and peer support — all accessible from a member's smartphone.\n\nWorkit Health's model addresses the primary barriers to addiction treatment: stigma, geographic access, and cost. By delivering care via telehealth, the platform reaches patients in rural areas and communities where addiction specialists are scarce. The company accepts Medicaid in multiple states, making treatment accessible to lower-income patients who are disproportionately impacted by the opioid crisis. Clinicians on the platform conduct initial assessments by video, prescribe medications electronically, and monitor patient progress through regular virtual check-ins and app-based engagement tools.\n\nThe platform includes a structured digital program with skills-based modules covering triggers, coping strategies, and recovery maintenance, delivered alongside clinical care. Workit Health competes with Bicycle Health, Ophelia, and Boulder Care in the virtual MAT market. The company has expanded to serve employers and health plans as well as direct consumers, positioning it as both a consumer-facing addiction recovery resource and a value-based care solution for payers managing high-cost substance use disorder populations.
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.
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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