English 
Supabase and AWS are showing how early-stage ideas can turn into apps that serve millions. Supabase, the PostgreSQL-powered development platform that has a user base of 5 million developers all over the world, introduced storage tools backed by Amazon S3 and ETL capability during AWS re:Invent to accelerate and ease the AI-based app creation process.
Supabase Analytics Buckets, built on Apache Iceberg and S3 Tables, handle analytics workloads. Vector Buckets store large vector datasets needed for AI features like semantic search and recommendations. Supabase ETL transfers data from Postgres to the storage of analytics and AI with a single click which saves months of coding work. The instruments provide the developers a possibility of evolving their applications from just ideas to complete production systems without going through the costly process of redoing.
Supabase depends on AWS (Amazon Web Services) in 17 different regions around the globe, which include Singapore, Tokyo, Sydney, London, and Northern California, thereby providing quick response of applications to users located anywhere. The platform also uses AWS Graviton processors for better performance at lower cost.
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The new features work together seamlessly: ETL copies Postgres data to Analytics Buckets for reporting and to Vector Buckets for AI features. Developers can query transactional data, analytics, and AI recommendations all from Postgres without juggling multiple systems.
Startups like Lovable, Figma Make, and Bolt already rely on Supabase to scale fast. Lovable, an AI website builder, spins up new databases automatically for each user project, showing how Supabase supports agentic workloads at scale.
Supabase has now become the backbone for startups building apps that grow from a weekend experiment into enterprise-grade systems, handling millions of users while keeping developers in a flow state without worrying about infrastructure complexity.
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