Modal

Modal is a platform for running AI workloads instantly with scalable GPUs and Python-first infrastructure.

Overview

Modal is a programmable AI infrastructure platform that lets you run inference, training, and batch workloads using simple Python code. It handles GPUs, autoscaling, storage, and observability for you, so deploying AI feels fast, local, and effortless.

Key features

Sub-second cold starts and instant autoscaling
Python-first SDK for defining infrastructure as code
Support for any GPU type (B300, B200, H100, A100, L40S, etc.)
Multi-node training with up to 128 B200s
Sandboxes for running untrusted code at scale
Built-in observability with logging and metrics
Global GPU infrastructure across multiple clouds
Pay-per-second billing with no idle costs
Token-based pricing for shared endpoints
SOC2 and HIPAA compliance

Pros

No infrastructure management required
Extremely fast autoscaling and cold starts
Flexible hardware selection and multi-cloud routing
Transparent per-second billing with no reserved capacity
Strong observability and production-ready tooling
Supports full ML lifecycle from training to inference
Isolated sandboxes for secure agent execution

Cons

Requires Python for SDK usage
Learning curve for distributed training and advanced features
Pricing can be high for sustained GPU workloads compared to reserved instances
Limited to serverless model (not suitable for always-on services)

Use this if

You need to run AI models, training jobs, or batch processing at scale without managing infrastructure. Ideal for spiky or unpredictable workloads where serverless autoscaling provides cost savings.

Skip this if

You need always-on services, have predictable sustained GPU usage where reserved instances are cheaper, or require non-Python infrastructure definitions.

Best for

AI teams running inference and training workloadsBatch processing and data-intensive computeBuilding and scaling AI agentsFine-tuning and reinforcement learningTeams needing instant autoscaling without capacity planningGPU-accelerated research and experimentation