The infrastructure behind AI search in Figma: embedding models, vector search, and scale optimizations
Figma users were struggling to locate specific designs or components, especially in large organizations with complex design systems. Component search relied on strict text matching, forcing designers to manually enumerate keywords in descriptions.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · File change or publish triggers indexing
Whenever a library is published, an asynchronous job is kicked off to compute embeddings for each thumbnail.
Tools used
CLIPOpenSearchAWS SageMakerDynamoDBS3llvmpipe
Outcome
Figma launched AI-powered search in early beta, enabling semantic and visual search across designs and components. Infrastructure optimizations reduced indexing data volume to 12% of the original by debouncing, and cut the search index in half by removing duplicates and draft files.
What failed first
The initial indexing approach serialized entire Figma files as JSON and parsed that in Ruby, which was extremely slow and memory-intensive. An early experiment generating embeddings from textual JSON representations also produced worse results than image-based embeddings.