Modal

Overview

Modal is a serverless cloud platform designed specifically for running AI, ML, and data applications. The platform makes it trivial to run Python code in the cloud with access to GPUs, parallelization, and auto-scaling. Modal abstracts away infrastructure complexity, allowing developers to deploy from their Python code with minimal configuration.

The platform is particularly popular for running ML inference, batch jobs, and data processing workloads. Modal's approach of "infrastructure from code" means you define compute requirements directly in Python, and the platform handles provisioning and scaling automatically.

Key Features

**Serverless GPUs**: Access GPUs on-demand

**Python-Native**: Define infrastructure in Python

**Auto-Scaling**: Automatic parallelization and scaling

**Fast Cold Starts**: Sub-second container startup

**Shared Storage**: Persistent storage across functions

**Scheduled Jobs**: Cron-style scheduling

**Web Endpoints**: Deploy APIs easily

**Local Development**: Test locally before deploying

When to Use Modal

Modal is ideal for:

ML inference and batch processing

AI applications needing GPU access

Data processing pipelines

Developers wanting to avoid DevOps

Rapid prototyping and deployment

Applications with variable compute needs

Pros

Extremely easy to use

Python-first approach

Fast cold starts

Pay only for what you use

Good for ML workloads

Free tier available

Excellent developer experience

Modern, well-designed API

Cons

Python-only (no other languages)

Newer platform with less track record

Vendor lock-in concerns

Limited compared to full cloud platforms

Some features still in beta

Pricing can add up at scale

Smaller ecosystem than AWS/GCP

Not suitable for all workloads

Pricing

**Free**: $30 credits monthly

**Usage-Based**: CPU $0.000231/second, GPU varies

**A100 GPU**: ~$1.50/hour

**No Monthly Fees**: Pay only for compute