How Biotech companies build with AI — which foundational models they run and how directly, ranked by proximity to the model. Avg Foundation Proximity Score 100/100.
Biotech · AWS · Native (own / self-hosted weights)
EvolutionaryScale operates ESM3 as a proprietary, GPU-accelerated protein LLM stack on AWS, exposing commercial access primarily via AWS SageMaker, AWS HealthOmics, and soon Amazon Bedrock, while distributing a smaller open version of ESM3 and earlier ESM models with code and some weights on GitHub and Hugging Face for non-commercial and open use.[3][5][7][11] Internal architecture details beyond this cloud/HPC and PyTorch-centric GPU cluster setup are not publicly documented.[3][10][12]
Biotech · AWS · Native (own / self-hosted weights)
Cell-programming foundry training its own biological/sequence models over massive lab data.
Biotech · Multi-Cloud · Native (own / self-hosted weights)
Generative-chemistry drug discovery on its own target-discovery + molecule-generation models.
Biotech · GCP · Native (own / self-hosted weights)
Public materials indicate Isomorphic Labs runs its production AI/drug-design engine on Google Cloud, using Google Cloud AI Hypercomputer with TPU/GPU fleets orchestrated by Google Kubernetes Engine for autoscaling inference workloads. The company publicly emphasizes its own drug-design engine and AlphaFold-derived models rather than exposing a foundational LLM-centric stack.
Biotech · GCP · Native (own / self-hosted weights)
Drug discovery on its own phenomics foundation models trained over petabytes of cellular imaging.