AWS re:Invent 2025 - Databases made effortless so agents and developers can change the world -INV208

AWS re:Invent 2025 - Databases Made Effortless for Agents and Developers

The Vision: Empowering Developers and Agents with Effortless Databases

• AWS's mission is to be the best long-term partner in enabling customers to build their platform for agentic AI, with data as the foundation.
• The rise of agentic AI tools like ChatGPT is transforming application development, democratizing it and leading to an exponential increase in the number of applications and AI agents.
• To handle this scale, customers need cloud databases that are secure, reliable, and efficient at unprecedented global scales.
• AWS aims to make it effortless for customers to discover insights, make better decisions, and drive innovation across their entire data estate.

Getting Started: Rapid Database Creation and Connectivity

• AWS is focused on enabling developers to "build at the speed of an idea" by providing extremely fast database creation experiences.
  • Aurora DSQL databases can now be created in just a few seconds.
  • Aurora Serverless databases will soon be available with the same rapid creation experience.
• Seamless connectivity from any tool or IDE is enabled through a new internet gateway that supports the Postgres wire protocol and integrates with IAM for secure access.
• The AWS Management Console provides streamlined database connectivity, including code snippets and direct access to tools like AWS CloudShell.
• New AWS free tier offerings, including $200 in credits for new customers, make it easier to get started.

Agentic AI Foundations: Effortless Data Modeling and Agentic Memory

• AWS is collaborating with partners to integrate databases seamlessly into popular developer tools, IDEs, and agentic AI frameworks.
• The DynamoDB MCP server includes a "vibe modeling" tool that uses natural language processing to automatically generate optimized NoSQL data models based on application requirements.
• Agentic memory is critical for agents to maintain context and personalization. AWS enables the use of databases like DynamoDB, Aurora, Neptune, and ElastiCache to power agentic memory for agents built on frameworks like Bedrock AgentCore, LangGraph, Mem0, and Letta.
• Semantic caching with ElastiCache for Valkey can improve the performance and cost-efficiency of agentic AI applications by caching the semantic meaning of queries.

Migration and Management: Effortless Patching, Scaling, and Modernization

• Amazon Aurora now applies patches in seconds and performs blue-green deployments in under 30 seconds, minimizing application impact.
• Upgrade rollout policies allow centralized and flexible management of database patching and upgrades across multiple accounts and clusters.
• Aurora storage capacity has been doubled to 256 TiB, enabling even the most data-intensive applications to scale up without the need to scale out.
• Zero-ETL integrations allow seamless real-time data movement from databases running on-premises or in other clouds into AWS analytics and AI services.
• AWS Transform automates the migration and modernization of legacy .NET and SQL Server applications to .NET Core on Linux and Aurora PostgreSQL, reducing costs by up to 70%.

Open Source Contributions: Optimizing for Modern Hardware and Workloads

• AWS is committed to contributing to open source database projects like PostgreSQL, Redis (Valkey), MariaDB, and MySQL.
• Examples of optimizations include a cache-efficient hash table design in Valkey 8.1 and the "skip scan" feature in PostgreSQL 18 that enables faster queries on secondary indexes.
• These open source contributions help improve the performance and cost-efficiency of databases running on modern hardware and serving agentic AI workloads.

Key Takeaways

• AWS is focused on making databases effortless to get started, migrate, manage, and leverage for agentic AI applications.
• Rapid database creation, seamless connectivity, and intelligent data modeling capabilities enable developers to "build at the speed of an idea."
• Agentic memory and semantic caching are critical for powering effective and personalized agentic AI agents.
• Innovations in database patching, scaling, and modernization reduce operational overhead and unlock cost savings.
• AWS's open source contributions help optimize databases for modern hardware and workloads, benefiting all customers.