Description
Modal is a production cloud platform engineered for AI and data teams, offering high-performance infrastructure that developers love. It enables running inference, training, batch processing, and sandboxes with remarkable speed, featuring sub-second cold starts and instant autoscaling. The platform is built with an AI-native runtime, optimized from the ground up for heavy AI workloads, ensuring super-fast autoscaling and containers that boot instantly.
Developers can leverage Modal's composable primitives to define logic and hardware requirements within their Python code, shipping directly to the cloud without leaving their preferred language. The elastic cloud capacity allows for seamless scaling from zero to over 1000 GPUs instantly, with Modal intelligently routing workloads across clouds and regions in real-time. This eliminates the need for capacity planning or long-term commitments, providing GPUs on demand within seconds.
Modal is production-ready with out-of-the-box observability, including integrated logging and full visibility into every function, sandbox, and container. This empowers teams to build robust, production-ready applications. The platform supports a wide range of workloads, including deploying and scaling inference for LLMs, audio, image/video generation, fine-tuning open-source models, and creating secure, ephemeral sandboxes for running untrusted code.
For inference, Modal supports any model or engine on various GPUs, scaling to zero between requests and bursting to handle demand. It handles multi-modal inference, batch and async processing, and offers online inference with sub-10ms overhead latency globally. For training, it supports the full loop from single-GPU fine-tuning to multi-node runs, including SFT, LoRA, and parallel hyperparameter sweeps. Modal Sandboxes are designed to scale agents, providing isolated, flexible execution layers for coding agents, background agents, and RL rollouts, capable of spinning up hundreds of thousands of concurrent environments in seconds.
Modal's global GPU infrastructure provides access to any GPU, anytime, distributed across clouds, with automated fleet health and scaling that matches demand. Security and governance features include team controls, battle-tested isolation, SOC2 & HIPAA compliance, and data residency controls, empowering teams of all sizes to ship at scale. Real-world applications demonstrate significant latency reductions and faster launch times for ML-driven projects.
Modal: AI Infrastructure's Core Features
Serverless AI infrastructure
High-performance compute (CPU, GPU)
Sub-second cold starts
Instant autoscaling (0 to 1000+ GPUs)
Python-native developer experience
Composable primitives for logic and hardware
AI-native runtime for heavy workloads
Elastic cloud capacity across multiple clouds/regions
Out-of-the-box observability (logging, visibility)
Support for inference, training, and sandboxes
Optimized for LLM inference
Full-stack training capabilities
Scalable sandboxes for agents and untrusted code
Global GPU infrastructure
Security and governance features (SOC2, HIPAA)
How to use Modal: AI Infrastructure?
Configure: Define your AI workload logic and hardware requirements in Python code using Modal's SDK.
Deploy: Ship your code to the Modal cloud for instant execution.
Scale: Leverage automatic scaling from zero to thousands of GPUs based on demand.
Monitor: Utilize integrated observability tools for real-time insights into your applications.
Optimize: Refine your models and workflows with Modal's high-performance training and inference capabilities.
Modal: AI Infrastructure's Use Cases
- LLM Inference
- Model Training
- Batch Processing
- AI Agent Execution
- Multi-modal AI
- Real-time Applications
- GPU-accelerated Research
