Modal AI

Modal is an AI infrastructure platform that helps developers run AI models, machine learning workloads, and compute-heavy applications in the cloud without managing complex servers. It provides access to GPUs, serverless computing, containers, notebooks, and secure sandboxes. Modal is designed for AI developers, researchers, startups, and businesses that need flexible and scalable infrastructure for building AI applications.

Uses of Modal AI

Modal helps developers run AI models and applications on powerful cloud GPUs without setting up or maintaining their own infrastructure. It can be used for AI model inference, model training, fine-tuning, large-scale batch processing, image and video generation, speech processing, and other machine learning workloads. Developers can choose GPUs such as A100, H100, H200, B200, and other supported options based on their requirements.

The platform is also useful for building AI agents and applications that need secure code execution. Modal Sandboxes provide isolated environments where developers can safely run AI-generated code, test software, execute Git repositories, or perform automated tasks. Its serverless Functions can automatically scale based on demand and scale down when they are not being used, helping developers avoid paying for idle compute.

Modal AI Models & Sub-Models

Modal Functions are the core building blocks for running code in the cloud. Developers can turn Python functions into scalable cloud workloads, and Modal automatically handles containers, resource allocation, scaling, logging, and execution. Functions can also use GPUs for AI and machine learning tasks.

Modal Inference provides infrastructure for running AI models with low latency. Developers can deploy open-weight or custom models and automatically scale them according to demand. This is useful for chatbots, image generation, speech processing, recommendation systems, and other real-time AI applications.

Modal Training provides cloud GPU infrastructure for training and fine-tuning AI models. Developers can run demanding machine learning workloads without purchasing or maintaining physical GPUs, making it easier to experiment with different model sizes and training requirements.

Modal Sandboxes are secure, isolated containers designed to run arbitrary or AI-generated code. They can be used by coding agents, AI applications, testing systems, and automated workflows that need a safe environment for executing code.

Modal Notebooks provide cloud-based Jupyter notebooks that run on Modal's infrastructure. Users can write and execute Python code in their browser while accessing CPUs, GPUs, popular machine learning libraries, and collaborative development features.

Modal Servers are designed for applications that need low-latency HTTP communication. They can run GPU-backed workloads, automatically scale when configured, and provide public or authenticated endpoints for applications that need to communicate with AI services.

Pricing

Modal uses a usage-based pricing model, meaning users generally pay for the computing resources they actually use rather than paying a fixed monthly fee for the platform. The platform currently provides $30 per month in free compute for new users, while additional CPU, memory, GPU, and other resources are charged according to usage.

Free: Yes, $30/month in free compute

Paid: Yes, usage-based pricing

Free Trial: Free compute available to get started

Best Use Cases

Modal is an excellent platform for AI developers, machine learning engineers, researchers, startups, and businesses building compute-intensive applications. It can be used for LLM inference, AI image and video generation, speech-to-text, model training, fine-tuning, AI agents, coding agents, large-scale data processing, batch jobs, and research experiments. Its serverless architecture, flexible GPU options, automatic scaling, and secure execution environments make Modal especially useful for teams that want to build and deploy AI applications without managing complex cloud infrastructure themselves.