Infrastructure

To build AI search in Figma, we had to tackle a number of technical hurdles, including the challenge of generating and indexing billions of embeddings to power these features—while also keeping costs down.

Our months-long investigation into slow search speeds led to a solution that not only improved performance but also set the foundation for future scale.

As many focus on the possibilities and limitations of AI tools and their output, Ovetta Sampson reminds us to pay attention to the power of the input.

After years of playing catch-up with Figma’s growth, it was time to fundamentally rethink LiveGraph’s architecture.

When we learned that engineers were losing hours building our C++ codebase, we jumped into investigating the root cause. Here’s how we cut build times in half and shipped a solution for scale.

Our nine month journey to horizontally shard Figma’s Postgres stack, and the key to unlocking (nearly) infinite scalability.

When our permissions system came apart at the seams, everything came to a halt. This is the story of how we fixed it while improving performance, accuracy, and developer ergonomics.

Engineering Manager Alice Ching discusses the parallels between developing gaming interfaces and building Figma and FigJam, and why our tech stack is more similar to a game engine’s tech stack than a web stack.

How the Figma infrastructure team reduced potential instability by scaling to multiple databases.