Keeping Figma Fast | Figma Blog
Figma’s original performance-testing system relied on a single MacBook running a few repeated scenarios, which worked when the product and team were smaller. As Figma expanded into plugins, FigJam, Dev Mode, and many other features, that approach became too limited and fragile—the laptop eventually overheated while the company was remote. Figma concluded it needed a scalable, proactive testing framework capable of catching regressions across its growing codebase. ## From One Laptop to a Growing Product - In 2018, one MacBook repeatedly ran performance scenarios and reported timing changes to a shared dashboard. - A major renderer restructuring and WebAssembly fixes made Figma three times faster. - Over five years, the product grew substantially, adding: - Plugins and Community features - FigJam - Dev Mode - Numerous ongoing product updates - The existing test files could no longer represent the product’s expanding feature set and edge cases. ## Limits of the Original Testing Process - Granular performance tests measure specific behavior at scale, such as panning through a file while 100 users edit layers and type simultaneously. - Creating feature-specific tests became impractical as Figma grew beyond 400 engineers and managers. - Increasing release velocity made it difficult for any individual or single machine to track every performance-affecting change. - During the shift to remote work in 2020, the office MacBook remained running unattended and eventually overheated. - Attempts to reproduce the setup on another laptop were unsuccessful, demonstrating that the system was not operationally scalable. ## Requirements for a New System Figma used the overhaul to define an ideal performance-testing framework: - **Test every proposed code change:** Performance checks should run against changes in the main monorepo, allowing regressions to be found during development rather than after users encounter them. - **Support proactive performance work:** Small delays can significantly disrupt users who spend hours working in Figma. - **Run tests in parallel:** Dozens of stress scenarios would need to execute simultaneously, similar to Figma’s existing cloud-based CI testing. - **Finish quickly:** Performance guardrail checks were required to complete in under 10 minutes. - **Scale across real hardware:** Running every pull request on physical machines could require roughly 100 identical runners at peak capacity. Figma’s experience shows that performance testing must evolve alongside product complexity. A lightweight single-machine setup can be effective initially, but larger teams and faster release cycles require automated, parallel, hardware-aware testing integrated directly into CI.
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