About Modal
Modal is a serverless compute platform built for running Python workloads on GPUs at scale. Designed for AI engineers, Modal lets you deploy LLM inference endpoints, batch processing jobs, and long-running training pipelines without provisioning servers, writing Dockerfiles, or wrestling with Kubernetes. Cold starts measured in seconds, automatic scaling to thousands of GPUs, and a Python-native developer experience make it a favorite for teams shipping production AI applications.
Pros & Cons
Pros
- Sub-second cold starts via custom container runtime
- Python-first DX eliminates DevOps overhead
- Generous free tier and per-second billing
Cons
- Python-only — no native support for other languages
- Newer platform with smaller community than AWS/GCP
Key Features
Serverless GPUs
Spin up A10G, A100, H100, and H200 GPUs on demand with sub-second container starts via Modal's custom runtime
Python-Native SDK
Deploy functions, web endpoints, and long-running jobs by decorating Python code — no YAML, no Dockerfiles required
Auto-Scaling
Scales to zero when idle and to hundreds of GPUs under load, with per-second billing for actual compute consumed
Built-In Storage
Network file systems, distributed dictionaries, and queues for stateful workloads without external services
Web Endpoints
Turn any Python function into an HTTPS API with FastAPI integration, WebSockets, and streaming response support
Sandboxes
Spawn isolated containers for running untrusted user code or agentic tool execution
Pricing
- $30 free compute credits/month
- All GPU types available
- Community support
- $100 included credits
- Multi-user workspaces
- Email support
- Higher concurrency limits
- Volume discounts
- Dedicated support
- SOC 2 Type II
- VPC peering