# From FileUploadWorkflow to creative OS: How Layer scaled on Temporal

DevFeed: [From FileUploadWorkflow to creative OS: How Layer scaled on Temporal](<https://devfeed.tech/articles/from-fileuploadworkflow-to-creative-os-how-layer-scaled-on-temporal-35833.md>)

Original publisher: [Read original article](<https://temporal.io/blog/fileuploadworkflow-creative-os-layer-scaled-temporal>)

Author: Alex Engel

Published: 2026-04-28T00:00:00Z

Content type: article

Language: en

Sources: [Temporal Blog](<https://devfeed.tech/sources/temporal-blog.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [async](<https://devfeed.tech/topics/async.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [lora](<https://devfeed.tech/topics/lora.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Canvas](<https://devfeed.tech/topics/canvas.md>), [Mobile](<https://devfeed.tech/topics/mobile.md>), [render](<https://devfeed.tech/topics/render.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [article](<https://devfeed.tech/tags/article.md>), [canvas](<https://devfeed.tech/tags/canvas.md>), [community](<https://devfeed.tech/tags/community.md>), [developer](<https://devfeed.tech/tags/developer.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [inference](<https://devfeed.tech/tags/inference.md>), [lora](<https://devfeed.tech/tags/lora.md>), [mobile](<https://devfeed.tech/tags/mobile.md>), [node](<https://devfeed.tech/tags/node.md>), [pipeline](<https://devfeed.tech/tags/pipeline.md>), [production](<https://devfeed.tech/tags/production.md>), [render](<https://devfeed.tech/tags/render.md>)

## AI overview

Layer describes how it expanded from a small set of Temporal workflows into about 50 workflow types running in production. The article covers the product and architecture changes behind its creative operating system, including image generation, LoRA fine-tuning, multimodal pipelines, billing synchronization, and large-scale parallel asset generation.

## Source excerpt

How Layer went from a handful of Temporal Workflows to 50 in production. The architecture, the lessons, and what broke along the way.