Serving 5 million developers worldwide, Supabase unveils Amazon S3 integrations to remove technical hurdles as apps grow

Early-stage projects that start as weekend experiments can evolve quickly into enterprise-grade apps

At AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), and Supabase, the Postgres development platform, today announced two new Amazon Simple Storage Service (Amazon S3)-based storage innovations and a new ETL¹ feature that make building generative artificial intelligence (AI) agents and apps easier. Built on Apache Iceberg and Amazon S3 Tables, Supabase Analytics Buckets support analytics workloads, while Supabase Vector Buckets provide specialized storage that powers AI features like semantic search and personalization. Supabase ETL automatically moves data from Postgres databases to analytics tools with a single click, eliminating months of coding work. Built on AWS, Supabase has launched more than 10 million databases to date and has become the foundation of choice for startups, with over 60% of each Y Combinator batch building on the platform.

These tools help developers build apps that consumers love and businesses need. Customers can scale apps seamlessly from prototype to production systems, serving millions of users without expensive rebuilds that slow down growing companies. Supabase handles all the behind-the-scenes work that AI code generation tools need to create fully functional apps, with PostgreSQL, one of the world's most widely used databases, as a single point of control. The platform, which serves 5 million developers worldwide and runs on AWS, has become a key enabler of the vibe coding movement, where developers stay in a creative flow state while AI tools handle the complexity of building production-ready applications.

"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."

Supabase currently operates across 17 global AWS Regions, including Asia Pacific (Singapore), Asia Pacific (Tokyo), Asia Pacific (Sydney), Europe (London), and US West (Northern California), enabling developers to create databases closer to customers for faster response times. This means an app responds instantly whether a user in Tokyo searches for a product or a gamer in Sydney joins a match—AWS's global infrastructure delivers the split-second performance that makes apps feel seamless and responsive. Supabase also runs exclusively on AWS Graviton processors, delivering improved performance with lower operational costs.

Key details of today's launch include:

• Supabase ETL automatically moves data from a Postgres database to a unified data layer that powers both analytics and AI features. With a single click, ETL copies data to both Supabase Analytics Buckets and Supabase Vector Buckets, giving dashboards and AI applications clean, organized data.
• Supabase Analytics Buckets support the Apache Iceberg format on Amazon S3 Tables, which means analytical data gets stored in a format that Amazon and third-party services can read natively. When a customer wants to run dashboards or reports, Supabase ETL replicates data from a user’s main Postgres database into an Analytics Bucket. Customers can then query data from Amazon Athena, Amazon Redshift, Amazon EMR, or Amazon Quick Sight without putting load on production databases.
• Supabase Vector Buckets let users store large vector datasets in Amazon S3 instead of Postgres databases. This matters for features like recommendation engines and semantic search. When a customer searches for "summer dresses" in a shopping app, traditional search looks for exact word matches, while vector search understands concepts and can find related items even if they use different words (like "sundress" or "warm weather outfits"). You still query Vector Buckets from Postgres using the same interface as before, but the storage sits in S3 where it is more cost-efficient and can scale to millions of embeddings without stressing your database.

This architecture uses PostgreSQL as the core transactional database for live business operations like processing orders. Supabase ETL continuously replicates data to Supabase Analytics Buckets for historical reporting and business intelligence while Supabase Vector Buckets handle AI-powered smart recommendations and semantic search. Everything syncs in near real-time, so an e-commerce company can write one query to show a customer their current order (PostgreSQL transactional data), analyze their buying history (Supabase Analytics Buckets), and suggest personalized products (Supabase Vector Buckets) – all from a single interface instead of 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."

In the third quarter of 2025 alone, more projects were created on Supabase than in the first four years of the company combined. Startups like Lovable, Figma Make, and Bolt rely on Supabase to scale rapidly on AWS. Lovable, an AI website builder, uses Supabase to autonomously spin up databases whenever users create new applications — showcasing the platform's ability to power agentic workloads at scale.

About AWS

Amazon Web Services (AWS) is guided by customer obsession, pace of innovation, commitment to operational excellence, and long-term thinking. By democratizing technology for nearly two decades and making cloud computing and generative AI accessible to organizations of every size and industry, AWS has built one of the fastest-growing enterprise technology businesses in history. Millions of customers trust AWS to accelerate innovation, transform their businesses, and shape the future. With the most comprehensive AI capabilities and global infrastructure footprint, AWS empowers builders to turn big ideas into reality. Learn more at aws.amazon.com and follow @AWSNewsroom.

About Supabase

Supabase is the Postgres development platform. It has emerged as the preferred backend for AI-driven development. 5 million developers choose to use Supabase, often alongside Cursor and Claude Code, because it allows them to quickly spin up a backend that instantly updates itself with commands from AI. This simplicity in workflow has driven Bolt, Figma, and Lovable to choose Supabase as the default backend for all projects on their platforms.

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