{"slug":"figma-trains-ai-search-models-in-5-months-using-amazon-sagemaker-ai","url":"https://findausecase.com/use-cases/figma-trains-ai-search-models-in-5-months-using-amazon-sagemaker-ai","title":"Figma trains AI Search models in 5 months using Amazon SageMaker AI","description":"Design platform Figma built its AI infrastructure on Amazon SageMaker AI, using Amazon EMR with Apache Spark to process billions of design elements and the custom FigmaStep framework on SageMaker Pipelines to orchestrate training. Figma trained and deployed the models behind its AI Search feature within five months, launching it at Config 2024, and ran more than 10,000 SageMaker AI training jobs in 2025 to support features including the AI prompt-to-app tool Figma Make.","company":"Figma","industry":"Technology & Software","aiCapabilities":["Generative AI","AI Model Development & MLOps"],"technology":["Amazon SageMaker AI","Amazon SageMaker Model Training","Amazon SageMaker Pipelines","Amazon EMR","Apache Spark","Amazon S3","AWS Controllers for Kubernetes"],"deployment":"Public Cloud","problemStatement":"Figma needed to rapidly develop and deploy innovative AI features such as AI Search to enhance product development workflows while maintaining the reliability and performance its enterprise customers demanded, but its existing infrastructure, while well suited for traditional workloads, was not optimized for the intensive, dynamic compute and specialized hardware needs of modern AI development.","solutionApproach":"Figma built its AI infrastructure on Amazon SageMaker AI. It established secure data processing pipelines using Amazon EMR with Apache Spark to process billions of design elements from its production systems while maintaining customer privacy controls, storing the processed data in Amazon S3. Figma built AI training infrastructure on Amazon SageMaker Model Training jobs and created the custom FigmaStep framework on top of Amazon SageMaker Pipelines so engineers could compose training pipelines from reusable Python-function steps, scaling from single-machine experiments to large-scale distributed training. For deployment, Figma integrated Amazon SageMaker AI endpoints into its existing Kubernetes-based infrastructure using AWS Controllers for Kubernetes.","businessValue":"By the end of April 2024, five months after starting the project, Figma had trained and deployed the models powering AI Search, which launched at Config 2024 in June. In 2025, Figma's team ran more than 10,000 Amazon SageMaker AI training jobs, with dozens of engineers running nearly 100 workflows per month. At Config 2025, Figma doubled its platform with four new products, including Figma Make, an AI prompt-to-app tool.","evidence":{"band":"high"},"sourceUrl":"https://aws.amazon.com/solutions/case-studies/figma-case-study","dates":{"publishedAt":"2026-08-16T09:15:12.252Z","publishedAtSource":"ledger","updatedAt":"2026-08-18T10:29:36.653Z"},"license":"Open for reading and citing with a link to https://findausecase.com/use-cases/figma-trains-ai-search-models-in-5-months-using-amazon-sagemaker-ai. Bulk republication requires permission."}