Modal vs Fal

Modal is a serverless GPU platform built for production-scale deployments of custom AI models. Fal is an AI inference platform that provides easy-to-use off-the-shelf APIs, primarily focused on image and video generation. Modal is ideal for code-first users needing flexibility and deep customization, while Fal provides quick access to pre-built AI APIs.

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What is Modal?

Modal is a serverless compute platform for AI inference, fine-tuning, and batch data processing. Modal's proprietary container stack enables blazing fast cold starts for rapid developer iteration and production-grade scalability. This is accessed via Modal's simple Python SDK, which allows users to run any function in the cloud without managing infrastructure. Try it out for yourself in the Modal Playground.

What is Fal?

Fal is an AI inference provider, primarily focused on offering APIs for diffusion models. It provides quick access to foundational models and allows for scalable inference without extensive infrastructure setup, though customization for proprietary models requires more engagement.

Flexibility


Support for the latest open-source image and video models
Support for adapters like LoRAs
Support for fully customized ComfyUI workflows
Support for custom inference stacks

Performance and cold starts


State-of-the-art, fully transparent inference optimizations
Ability to autoscale custom models to thousands of GPUs in minutes
Sub-second GPU container cold starts for custom models
Proprietary Rust-based container stack built for performance
Memory snapshotting for fast container imports
Multi-region support
White-glove engineering support to optimize your AI workloads

Developer experience


Complete control over code for custom models
Python-defined infrastructure
No YAML or Dockerfiles needed for custom models
Attach GPUs and define custom images with one line of code
One-step creation of API endpoints for model deployments
Seamless integration between dev and prod environments
Hot reloading
Insanely fast image builds and model deployments for fast iteration
Notebooks for fast prototyping
API endpoints for out-of-the-box models

OpenArt uses Modal to power
compound image generation pipelines

OpenArt is a popular platform for AI image generation and editing. They use API providers for their vanilla text-to-image feature, but their proprietary image generation pipelines are all deployed on Modal.

"As a startup, you need to iterate on things quickly. So it's really helpful when the developer experience and development speed is suddenly like 5x or 10x."

— Coco Mao, CEO and Co-founder at OpenArt

With Modal's performant container stack, OpenArt was able to save hours on every redeploy compared to setting up their own infrastructure.

Read the full OpenArt case study here

Use cases


Real-time inference
Fully customizable fine-tuning
Model training
Offline inference
Parallelized batch data jobs for pre- or post-processing
Cron jobs
Compound AI systems
Sandboxed code execution for agents

Pricing


Usage-based pricing with no reservations or minimums
$30/mo in free compute credits
Transparent, public pricing
No reservations required for competitive pricing
Volume discounts for enterprises

Security and enterprise readiness


SOC 2 Type II
Dedicated model deployments
HIPAA
State-of-the-art security and container isolation with gVisor runtime
Fine-grained observability down to individual inputs and containers

Choose Modal for:

Low-latency highly-scalable deployments of custom AI models or workflows.

A versatile platform supporting both AI and general compute workloads.

A streamlined developer experience with fast iteration and comprehensive observability.

Choose Fal for:

Readily available AI APIs, particularly for diffusion models.

Simple image or video generation workflows.

Quick experimentation with pre-trained models via a playground.