Modal

Modal is a serverless cloud platform designed specifically for AI and data-intensive workloads. It allows developers to run Python code at scale across CPU, GPU, and memory-intensive tasks without managing underlying infrastructure. The platform focuses on providing sub-second cold start times, instant autoscaling from zero to thousands of GPUs, and a local-like developer experience through its Python SDK. At its core, Modal treats cloud infrastructure as code. Developers define compute resources, dependencies, and hardware requirements using Python primitives within a single script or module. This eliminates the need to separate infrastructure configuration from application logic. For example, a user can specify that a function requires an A100 GPU, 32 GB of memory, and a specific container image, all within the same code block that defines the function’s behavior. Modal then automatically provisions the required resources, runs the function, and scales it up or down based on request volume. The platform is built for speed. Containers boot in under a second, which enables real-time inference and interactive workloads that traditionally required persistent servers. Autoscaling is reactive to traffic patterns—Modal can scale from zero to over 1,000 GPUs in seconds and scale back to zero when idle, so users only pay for what they use. This elasticity makes it suitable for batch processing jobs, such as large-scale data transformations or scientific simulations, where compute demand spikes unpredictably. Modal’s runtime is optimized for AI workflows. It supports popular frameworks like PyTorch, TensorFlow, and JAX out of the box, with pre-built container images that include CUDA, cuDNN, and other optimized libraries. For inference serving, Modal handles load balancing, req