Amazon Web Services, and Supabase, the Postgres development platform, unveiled two new Amazon Simple Storage Service (Amazon S3)-powered storage solutions and an ETL feature designed to simplify how developers build generative AI agents and applications. Supabase Analytics Buckets, built on Apache Iceberg and Amazon S3 Tables, bring modern analytics storage to Postgres environments, while Supabase Vector Buckets offer specialized vector storage for AI capabilities such as semantic search, recommendations, and personalization. Supabase ETL provides one-click data movement from Postgres databases to analytics tools, eliminating months of custom engineering work. Operating on AWS, Supabase has provisioned more than 10 million databases and has become a preferred platform for early-stage founders—serving over 60% of each Y Combinator batch.
These new capabilities give developers the end-to-end foundation they need to build high-performing applications that scale seamlessly from prototype to large-scale production environments. Supabase manages the operational complexity behind modern AI-driven development, enabling AI code generation tools to build complete applications using PostgreSQL—the world’s most trusted open-source relational database—as the unified point of control. With more than 5 million developers building on Supabase, the platform has become a central force behind the “vibe coding” movement, where developers stay in their creative flow while AI handles heavy lifting.
“Before Supabase, building an app meant juggling multiple separate services—one for your database, another for user logins, a third for file storage—each with its own dashboard and way of working,” said Paul Copplestone, CEO and co-founder, Supabase. “Today, Supabase brings all of these together in one platform, all built on top of Postgres. This means developers work in one place instead of five, with the confidence that AWS‘s scale will handle everything from their first user to their millionth without missing a beat.”
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Supabase operates in 17 AWS Regions worldwide-including Singapore, Tokyo, Sydney, London, and Northern California-allowing developers to deploy databases close to users for lightning-fast responsiveness. Whether it’s a shopper in Tokyo performing a search or a gamer in Sydney joining a match, AWS’s global infrastructure ensures sub-second performance. Supabase also exclusively runs on AWS Graviton processors, providing improved efficiency and reduced operational cost.
This architecture positions PostgreSQL as the central transactional engine—handling real-time operations such as order processing-while Supabase ETL replicates data to Analytics Buckets for historical analysis, and Vector Buckets deliver AI-driven recommendations and semantic understanding. With near real-time synchronization, companies can surface a customer’s current order, analyze lifetime purchasing trends, and generate personalized product suggestions-all from a single query interface instead of managing three separate systems.
“Every modern business is a data business and Amazon S3 is foundational for developers,” said Mai-Lan Tomsen Bukovec, vice president of Technology, AWS. “By bringing together S3’s scale and reliability with Supabase’s integrated platform, we’re making it easier for developers to work with their data and move from AI experimentation to applications in production.”
“Imagine a retailer trying to analyze customer behavior across their website, mobile app, and physical stores. They’d need to collect data from multiple systems, clean it up, translate it into a common language, and deliver it to where analysts can use it — all while keeping it updated in near real-time,” added Copplestone. “What we’ve done with AWS is turn this entire process into something as simple as ticking a box, allowing businesses to focus on using their data rather than struggling to access it.”