What is Modal?
Modal is a serverless compute platform aimed at Python and ML workloads. Developers annotate functions with Modal's SDK and deploy them to autoscaling infrastructure, including GPU instances, without managing servers, containers or Kubernetes directly. Modal takes your code, puts it in a container and executes it in the cloud. The introduction describes an AI infrastructure platform for low latency inference, massively parallel batch jobs, training and fine-tuning on GPUs, and thousands of isolated Sandboxes that execute AI generated code. Workloads scale to zero when idle.
Python is the primary language for building Modal applications, and JavaScript, TypeScript or Go can be used to call Functions, run Sandboxes and manage resources. Getting started takes pip install modal and modal setup to authenticate. Everything is hosted by Modal, which pools capacity over all major clouds and says its infrastructure spans 20+ clouds. Using a GPU requires a valid payment method on file.
Pricing is usage based, charged per second. The Starter plan costs $0 plus compute per month and includes $30 of monthly free credits. The Team plan costs $250 plus compute per month, and Enterprise is custom. Modal states that it is SOC 2 Type II compliant and HIPAA ready, and it offers credit grants for early-stage startups. Typical uses are model inference, batch processing, fine-tuning and sandboxed code execution for agents.
Key capabilities
- Inference Serve LLMs, image, video and audio models, real-time or batch, with OpenAI-compatible endpoints.
- Sandboxes Isolated environments for coding agents, RL rollouts and untrusted code.
- Training Fine-tune, run RL and evaluate models on GPUs that spin up in seconds.
- Per-second billing Pay for the compute used.
- Starter credits $30 per month in free credits on the Starter plan.
Where Modal fits
- Inference OpenAI-compatible endpoints give out-of-the-box performance, and you can bring your entire inference stack while Modal handles the infrastructure.
- Sandboxes Secure, flexible environments for long running RL rollouts and interactive coding agents, built to scale.
- Training Fine-tune with RL, SFT, LoRA or full fine-tunes, with nearly instant access to single and multi-node GPU capacity.
- Functions Fan out over thousands of GPUs in parallel with no job orchestration, or turn any function into an HTTPS endpoint that idles at zero.
- Distributed storage A distributed filesystem loads container images, model weights and datasets at full bandwidth wherever Functions run.
- Security controls Workload isolation, granular RBAC, audit logs and data residency controls are listed for enterprise teams.
- Pay per second Resources are charged per second of usage, so idle capacity is not billed.
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Alternatives to Modal
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DeepInfraDeepInfra is an AI inference cloud that serves 200+ open and hosted models through OpenAI- and Anthropic-compatible APIs, billed per token or execution time, plus dedicated GPU deployments, GPU instances, sandboxes and batch jobs.