What is it

Modal is a high-performance serverless cloud platform engineered specifically for AI workloads. It allows developers to define cloud infrastructure entirely in Python code, deploying functions that scale from zero to thousands of GPUs instantly while handling all environment setup, containerization, and autoscaling automatically.

What it can do

Users can deploy and scale inference for any LLM or multi-modal model, run fine-tuning and reinforcement learning on clusters up to 128 B200s, execute untrusted code in isolated sandboxes for AI agents, and process massive batch jobs—all with sub-second cold starts and pay-per-second billing. Modal supports WebSocket, WebRTC, and token streaming for real-time applications.

Who is it for

AI software engineers, ML researchers, startups, and enterprises who need a lightning-fast, highly scalable, developer-centric platform to build, train, and serve production-grade AI applications without managing servers or containers.

Key Features

Python-First Serverless SDK – Infrastructure as Code

Define your entire cloud environment in Python using composable primitives that specify logic, hardware, and dependencies in a single file. Modal's SDK feels like running code locally while automatically handling containerization, dependency resolution, and deployment—eliminating the need for Dockerfiles, Kubernetes manifests, or manual server provisioning.

Sub-Second Cold Starts with GPU Memory Snapshots

Boot GPU-accelerated functions in under a second using advanced checkpoint/restore technology. CPU memory snapshots introduced in early 2025 slashed startup times by more than half, and GPU memory snapshots (July 2025) extend this to GPU workloads—achieving up to 10x faster cold boots by capturing model weights, CUDA kernels, and compiled artifacts directly in the snapshot.

Elastic GPU Autoscaling – Zero to Thousands Instantly

Scale from zero to 1000+ GPUs automatically based on request volume, with workloads routed across clouds and regions in real time. Modal's serverless architecture eliminates capacity planning and reserved instances—pay only for actual compute time by the CPU cycle, with no charges for idle resources.

Multi-Workload AI Platform – Inference, Training, and Sandboxes

Run the full spectrum of AI workloads on a unified platform. Deploy LLM and multi-modal inference with sub-10ms overhead latency, fine-tune open-source models via SFT, LoRA, or full fine-tunes on B200s and H100s, execute RL with thousands of concurrent trajectories, and spin up isolated sandboxes for coding agents and untrusted code execution.

Use Cases

LLM inference serving

Deploy and scale large language model inference APIs with automatic scale-to-zero between requests and burst capacity during demand spikes. Modal's sub-second cold starts and sub-10ms overhead make it ideal for production chatbots, completion APIs, and embedding services that require both cost efficiency and low latency.

Multi-modal AI inference

Serve image generation, video processing, audio transcription, and embedding models at scale using any framework and hardware configuration. Modal's support for diverse GPU types—from T4s for cost-sensitive workloads to B200s for maximum throughput—allows teams to optimize price-performance for each modality.

Model fine-tuning and training

Run supervised fine-tuning, LoRA adapters, and full fine-tunes on single GPUs or multi-node clusters with up to 128 B200s and 3200 Gbps InfiniBand networking. Gang scheduling and parallel hyperparameter sweeps launch hundreds of experiments simultaneously with a few lines of code, scaling hardware up and down automatically.

Reinforcement learning at scale

Spin up thousands of concurrent rollout environments for RL training, keeping GPU inference resources saturated across every episode. Modal's native integration of sandboxes and training infrastructure on the same stack makes it uniquely suited for RL workflows that require rapid environment iteration and massive parallelization.

Pricing plans

Frequently Asked Questions

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