General Proximity vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
Cambridge MA YC W20 induced proximity medicines (molecular glues/PROTACs) targeting undruggable proteins; $16M Series A Jan 2025 Tomales Bay Capital competing with Arvinas and C4 Therapeutics for protein degradation oncology platform.
About
General Proximity is a Cambridge, Massachusetts-based biotechnology company — backed by Y Combinator (W20) with $16 million in Series A funding in January 2025 led by Tomales Bay Capital — pioneering induced proximity medicines that work by bringing together disease-causing proteins to trigger their degradation or modulation, representing a next-generation therapeutic modality beyond traditional small molecule inhibitors and biologics. Founded in 2020, General Proximity combines computational chemistry, medicinal chemistry, and structural biology to design molecular glues and PROTACs (proteolysis-targeting chimeras) for previously undruggable targets in oncology and other disease areas — targeting the protein degradation mechanisms that cells use naturally to eliminate aberrant proteins.
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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