Modal provides serverless cloud infrastructure engineered specifically for AI and machine learning workloads. The platform enables developers to deploy Python functions to the cloud using infrastructure-as-code patterns, defining custom container images and hardware requirements directly in code without YAML configuration files. Modal's runtime environment is built from the ground up for heavy AI workloads with sub-second container cold starts and instant autoscaling capabilities.

The platform supports diverse AI workload types including inference serving for large language models and generative AI, model training and fine-tuning on single or multi-node GPU clusters, batch processing that scales to thousands of containers, and secure sandboxed environments for running untrusted code. Modal provides access to thousands of GPUs across multiple cloud providers without quotas or reservations, enabling elastic scaling from zero to maximum capacity based on actual demand.

Modal's infrastructure includes a globally distributed storage system optimized for high throughput and low latency, designed for fast model loading and training data access. The platform integrates with existing cloud storage buckets, MLOps tools, and telemetry vendors while providing unified observability with integrated logging and full visibility into every function and container. Teams benefit from real-time metrics, deployment rollbacks, custom domains, webhooks, and scheduled jobs alongside Python-native development workflows.