Adaptyv Biosystems vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
Lausanne Switzerland automated cloud lab for AI protein engineering at $11.2M total ($8M ACE Ventures seed 2025); improved protein success rate from 2.5% to 13% testing 10K+ proteins for 30+ pharma/biotech partners competing with Recursion for AI drug discovery.
About
Adaptyv Biosystems is a Lausanne, Switzerland-based AI-driven protein engineering platform — backed with $11.2 million in total funding including an $8 million seed round in 2025 led by ACE Ventures with participation from ByFounders, Founderful, and LongGame Ventures, following a CHF 2.5 million pre-seed in 2022 — providing pharmaceutical companies, biotechnology firms, and academic AI research groups with fully automated cloud laboratory services for validating and iterating AI-generated protein designs, enabling the experimental testing that transforms computational protein engineering from theoretical exploration to practical drug discovery. Founded in 2021 and having tested over 10,000 proteins in collaboration with 30+ pharma and biotech partners, Adaptyv has improved protein engineering success rates from the industry baseline of 2.5% to 13% — a 5x improvement that represents the breakthrough enabling practical AI-guided therapeutic protein development.
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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