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.
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.
| 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. |
| Readily available AI APIs, particularly for diffusion models. |
| Simple image or video generation workflows. |
| Quick experimentation with pre-trained models via a playground. |
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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 | | |
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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 | | |
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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 | | |
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|---|---|---|
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 | | |
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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 | | |
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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 | | |
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