Twenty years in, Amazon S3 finds itself at the center of AWS’ push beyond storage
Summary
Amazon S3 is celebrating its 20th anniversary on March 14, 2026, marking its evolution from an internal Amazon storage utility to a foundational cloud infrastructure service, increasingly critical for AI systems. Andy Warfield, VP and distinguished engineer at AWS, and Rob Strechay of theCUBE Research discussed S3's journey during the AWS Pi Day 20th Year Celebration. S3's origins addressed Amazon's internal need for elastic, scalable web-facing storage, leading to its public offering. Over two decades, S3 has achieved 11 nines of reliability, powers over a million data lakes, and processes quadrillions of requests annually. Recent innovations like S3 Tables and S3 Vectors are expanding its role beyond simple object storage, integrating it more deeply with computational tasks and emerging AI development tools, while maintaining a focus on cost reduction, with intelligent tiering alone saving customers over $6 billion.
Key takeaway
For CTOs and VPs of Engineering building modern data architectures, S3's evolution into a data substrate for AI, with features like S3 Tables and S3 Vectors, means your teams can consolidate data storage and processing. You should evaluate how these new S3 capabilities can simplify your data pipelines, reduce operational overhead, and accelerate AI/ML initiatives by providing a unified, high-performance foundation for diverse data types, rather than relying on disparate storage solutions.
Key insights
Amazon S3 has evolved into a foundational data substrate for AI and analytics, moving beyond basic object storage.
Principles
- Elastic, scalable storage is critical for web-facing applications.
- Shared data substrates foster developer velocity and tool flexibility.
- Reducing friction through consistency and cost savings drives adoption.
Method
S3's evolution involves adding new data types like S3 Tables and S3 Vectors, integrating with open table formats like Apache Iceberg, and enhancing performance for higher-throughput applications.
In practice
- Utilize S3 Tables for structured data storage.
- Explore S3 Vectors for AI/ML applications.
- Implement intelligent tiering to optimize storage costs.
Topics
- Amazon S3
- AI Infrastructure
- Object Storage
- Data Lakes
- S3 Vectors
Best for: CTO, VP of Engineering/Data, Director of AI/ML, Data Engineer, MLOps Engineer, AI Architect
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Editorial summary, takeaway, and curation by AIssential. Original article published by AI – SiliconANGLE.