Nabla vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
AI clinical copilot. 85K+ clinicians, 130+ organizations. 20M patient interactions/year. Raised $70M Series C. Evolving to Adaptive Agentic Platform. Founded in Paris.
About
Nabla is a Paris-based clinical AI company founded in 2018 with a mission to reduce the documentation burden that is one of the primary drivers of clinician burnout across health systems globally. The company developed an AI clinical copilot that listens to patient-physician conversations and automatically generates structured clinical notes in the background, allowing clinicians to focus entirely on the patient rather than on documentation. Nabla's core technology applies large language models fine-tuned on medical language and SOAP note structure to produce accurate, editable documentation in real time.\n\nNabla's platform serves more than 85,000 clinicians across 130-plus organizations spanning hospitals, ambulatory practices, and digital health providers. It has facilitated over 20 million patient interactions per year, making it one of the highest-volume ambient AI documentation platforms in the market. The product supports more than 45 medical specialties, with specialty-specific note templates that reflect the distinct documentation requirements of fields ranging from psychiatry to orthopedics. Nabla is evolving its platform toward an Adaptive Agentic architecture, enabling the AI to take actions beyond note-taking — such as ordering follow-ups, drafting referrals, and surfacing clinical decision support.\n\nNabla raised a $70 million Series C to fund US expansion and platform development, following strong adoption in the European market. The company competes in the ambient clinical documentation space alongside Nuance DAX, Suki, and Abridge, but differentiates through its European regulatory expertise, multi-specialty coverage, and its strategic shift toward a full agentic clinical platform. With 20 million annual patient interactions and a clear product roadmap toward autonomous clinical workflow automation, Nabla is well positioned to grow into a broader clinical AI infrastructure role.
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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