How to Build a Production AI Image Generation Pipeline with fal.ai and Inngest

Building a production AI image app involves handling two main challenges: inference and workflow orchestration. The inference aspect, efficiently managed by fal.ai, requires running models quickly and reliably at scale, offering access to numerous models through a single API while managing GPU provisioning and scaling. Workflow orchestration, addressed by Inngest, involves coordinating the processes around the inference call, ensuring tasks like resuming execution after inference and retrying failed uploads are handled without redundant computations. The integration of these tools facilitates the creation of a seamless media production pipeline, where fal.ai manages asynchronous image generation via a queue API, and Inngest coordinates the execution steps with full observability and minimal compute costs. This setup allows for efficient image generation and storage, protects inference costs through step-level retries, and maintains per-user fairness by dynamically managing queues, ensuring that large batch jobs don't hinder real-time requests. As the product grows in complexity, additional steps such as post-processing or custom model training can be added without altering the underlying architecture, highlighting the adaptability and scalability of the system.

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