Modal

Modal positions itself as cloud infrastructure purpose-built for production AI. Rather than a general-purpose cloud, it targets teams that need to develop, train, and serve AI applications at scale, with a platform rebuilt from the container runtime and storage layer up through a multi-cloud capacity pool.

The product surface is organized around three core areas. Inference covers serving LLMs, image, video, and audio models across real-time and batch workloads. Sandboxes provide isolated environments for coding agents, RL rollouts, and untrusted code. Training supports fine-tuning, reinforcement learning, and model evaluation on GPUs that spin up in seconds.

Modal is aimed at AI engineering teams and startups running production workloads, particularly those working with agentic systems, reinforcement learning, or multi-modal model serving. The emphasis on fast GPU provisioning and secure isolation suggests it fits teams that need elastic capacity without managing cluster infrastructure themselves.

For evaluation, the GPU Glossary and LLM Engine Advisor are useful starting points for understanding hardware choices and model selection. Teams should review the docs and pricing to map sandbox and inference usage to their workload patterns before committing, since costs scale with compute consumption.

In short, Modal is a focused infrastructure layer for AI teams that want production-grade serving, training, and sandboxing without building the underlying platform themselves.