Twenty years of Amazon S3 and building what’s next | Amazon Web Services

Amazon S3 began in 2006 as a simple web service for storing and retrieving objects, but its emphasis on security, durability, availability, performance, and elasticity enabled it to become foundational infrastructure. Over two decades, it scaled from roughly one petabyte to hundreds of exabytes while preserving API compatibility, reducing prices, and expanding beyond object storage. Amazon’s long-term vision is for S3 to serve as a universal foundation for data, analytics, and AI workloads.

The Original S3 Philosophy

• S3 introduced two basic operations:
  • PUT to store an object
  • GET to retrieve it
• The service abstracted away complex infrastructure so developers could focus on applications.
• Its five enduring design principles are:
  • Security: Data is protected by default.
  • Durability: Designed for 11 nines of durability, with a lossless operating model.
  • Availability: Failure is assumed and handled throughout the system.
  • Performance: Storage capacity can grow without degrading performance.
  • Elasticity: Capacity expands and contracts automatically.

From One Petabyte to Hundreds of Exabytes

• At launch, S3 had approximately:
  • One petabyte of capacity
  • 400 storage nodes across 15 racks and three data centers
  • 15 Gbps of bandwidth
  • A maximum object size of 5 GB
  • A price of $0.15 per GB
• Today, S3:
  • Stores more than 500 trillion objects.
  • Serves over 200 million requests per second.
  • Operates across 123 Availability Zones in 39 AWS Regions.
  • Supports objects up to 50 TB—10,000 times larger than the original limit.
• Storage prices have fallen by roughly 85%, to slightly above 2 cents per GB.
• S3 Intelligent-Tiering has saved customers more than $6 billion in storage costs.
• The S3 API has become an industry standard, with many other storage systems offering compatible interfaces.

Backward Compatibility and Long-Term Reliability

• Code written against S3 in 2006 still works without modification.
• AWS has repeatedly replaced disks, storage systems, and request-processing code while preserving access to older data.
• This compatibility reflects S3’s goal of remaining infrastructure that “just works” despite continuous internal change.

Engineering for Durability and Scale

• Microservices continuously inspect every byte across the fleet.
• Auditor services detect degradation and automatically trigger repair and re-replication.
• Automated formal methods mathematically verify correctness in areas such as:
  • The index subsystem
  • Cross-Region replication
  • Access policies
• AWS has progressively rewritten performance-critical components in Rust over the past eight years.
• Rust improves performance while preventing memory-safety bugs and other classes of errors at compile time.
• S3 follows the principle that scale should improve the service: larger, more distributed workloads become increasingly decorrelated, improving reliability for all customers.

S3 as a Foundation for Data and AI

Amazon’s future vision is for customers to store data once in S3 and work with it directly, avoiding costly copies and specialized systems.

• S3 Tables provides managed Apache Iceberg tables with automated maintenance to improve query performance and reduce storage costs.
• S3 Vectors supports semantic search and retrieval-augmented generation, with up to 2 billion vectors per index and sub-100 ms query latency.
  • Within five months of launch, customers created over 250,000 indexes, ingested more than 40 billion vectors, and executed over 1 billion queries.
• S3 Metadata enables centralized, faster data discovery without recursively listing large buckets.

These additions extend S3 from inexpensive object storage into a broader platform for analytics, search, and AI while retaining its scale and cost advantages.