Modal
Serverless GPU compute platform for Python AI workloads - deploy ML inference and fine-tuning jobs on H100 and A100s with sub-second cold starts and zero infrastructure.
Modal is a serverless cloud compute platform founded in 2021 by Erik Bernhardsson that enables Python developers to run GPU-accelerated AI workloads by decorating functions with a single Python decorator rather than managing servers, containers, or GPU instances. The platform cold-starts GPU containers in under one second and scales from zero to thousands of parallel containers automatically. Modal supports H100, A100, A10G, T4, and L4 GPU types and includes built-in persistent volumes for storing model weights across runs, scheduled jobs, async task queues, and a live-reload development mode. The pay-per-second pricing model with a free starter credit means teams pay only for actual compute rather than maintaining idle GPU servers.
Key Features
- Serverless GPU execution via Python decorator - no server provisioning, Docker, or Kubernetes required
- Sub-second cold starts from zero to running GPU container for any registered function
- H100, A100, A10G, T4, and L4 GPU type selection per function based on workload requirements
- Persistent volumes for storing model weights and datasets between runs without re-downloading
- Automatic horizontal scaling from zero to thousands of parallel containers on demand
- Scheduled jobs and async task queues built into the framework for batch and pipeline workloads
- Live-reload development mode for iterating on GPU code locally with remote execution
Use Cases
- ML engineers running model inference endpoints that need to scale from zero without idle GPU costs
- Researchers running batch inference on large datasets without managing cloud VM clusters
- Developers fine-tuning open-source models on custom datasets without GPU server administration
- AI startups building GPU-dependent products without upfront infrastructure investment or DevOps overhead
Pros
- Zero infrastructure management - GPU workloads deploy with a single Python decorator annotation
- Sub-second cold starts enable true serverless scaling without pre-warming or idle capacity
- Pay only for seconds of compute used - no idle GPU costs between requests or batch jobs
Cons
- Learning curve for teams accustomed to traditional VM-based or Docker container deployment
- Costs can become unpredictable for sustained high-volume workloads without concurrency limits set
- Best suited for bursty or batch GPU workloads - less optimal for continuously running services
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