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AWS Re:cap 11: What's New with AWS Lambda (CNS376)

Speaker: AWS re:Invent 2025

Session: 11

Session
Summit Dev Lounge2026 Re:cap
01 Encode Architecture as Steering for AI Agents
Summit Dev Lounge2026 Re:cap
02 Agent Harness Is the Real Engineering Moat
Summit Dev Lounge2026 Re:cap
03 Ask Observability Data in Plain Language
Summit Dev Lounge2026 Re:cap
04 Serverless AR Game with Bedrock AgentCore
Summit Dev Lounge2026 Re:cap
05 Multi-Agent Quant Backtesting on AgentCore
Summit Dev Lounge2026 Re:cap
06 Blog to Slides in Three Minutes with Kiro
Summit Dev Lounge2026 Re:cap
AWS Community Day Hong Kong 2025 Re:cap
02 AWS Compliance with Terraform
AWS Community Day Hong Kong 2025 Re:cap
03 Beginner to Builder An AWeSome Cloud Journey
AWS Community Day Hong Kong 2025 Re:cap
04 Team-First Serverless Engineering with Laravel & Bref
AWS Community Day Hong Kong 2025 Re:cap
05 Event Opening - AWS Community Day Hong Kong 2025
AWS Community Day Hong Kong 2025 Re:cap
06 Agent-to-Agent: Building Interoperable AI on AWS
AWS Community Day Hong Kong 2025 Re:cap
07 Utilize another telemetry data for faster improvement with AI agent
AWS Community Day Hong Kong 2025 Re:cap
08 Graduating from Vibe Coding: Spec-Driven Development with Kiro
AWS Community Day Hong Kong 2025 Re:cap
09 Automated Testing using MCP & AI Agents
AWS Community Day Hong Kong 2025 Re:cap
10 Modernizing Telecom Security ML Powered Approach
AWS Community Day Hong Kong 2025 Re:cap
11 Rethinking GenAI Agent: RAG & MCP
AWS Community Day Hong Kong 2025 Re:cap
12 Disaster and Emergency Response with TAK and AWS
AWS Community Day Hong Kong 2025 Re:cap
13 Rethinking Serverless Application Workflows from a Testing Perspective
AWS Community Day Hong Kong 2025 Re:cap
14 Practical AWS FinOps for Cloud Success
AWS Community Day Hong Kong 2025 Re:cap
15 AI-Powered Global Pure-Alpha Macro Trades on AWS: Revolutionizing Risk-Adjusted Asset Returns
AWS Community Day Hong Kong 2025 Re:cap
FSI Recap
01 Modern Trade Lifecycle: Trading to Settlement
FSI Recap
02 Goldman Sachs: Fast Track your applications onto Cloud - AWS Re:cap Q1/2023
FSI Recap
03 Zurich Insurance Group: Building an Effective Log Management Solution on AWS
FSI Recap
04 FSI Meetup 2025 Q4 - Brex Database Disaster Recovery
FSI Recap
05 FSI Meetup 2025 Q4 - A Graviton Migration Success Story
FSI Recap
06 FSI Meetup 2025 Q4 - Stifel Modern Data Platform
FSI Recap
07 FSI Meetup 2025 Q4 - Financial Transaction Data Reconciler PayPal
FSI Recap
08 FSI Meetup 2025 Q4 - Scaling Resilience
FSI Recap
09 Maximizing AI Inference Cost Efficiency: Strategic Adoption of AWS GPU Instances
FSI Recap
10 Advanced Agentic AI Design Patterns
FSI Recap
11 Build New Modern Apps on AWS
FSI Recap
AWS re:Invent 2025
01 Coinbase re:Invent Recap (IND3312)
AWS re:Invent 2025
02 Building the Future Trading Platform Leveraging AI and AWS
AWS re:Invent 2025
03 Trading Innovation: Jefferies' AI Assistant on Amazon Bedrock (IND3315)
AWS re:Invent 2025
04 How FSI Revolutionized HFT Analytics with Agentic AI (GBL302)
AWS re:Invent 2025
05 Improving Distributed Systems with Amazon Time Sync Featuring Nasdaq
AWS re:Invent 2025
06 Amazon Aurora HA and DR Design Patterns for Global Resilience (DAT442)
AWS re:Invent 2025
07 Building Agentic AI: Amazon Nova Act and Strands Agents in Practice (DEV327)
AWS re:Invent 2025
08 Deep Dive into Amazon Aurora and Its Innovations (DAT441)
AWS re:Invent 2025
09 Deep dive on Amazon S3 (STG407)
AWS re:Invent 2025
10 Nasdaq: Build Resilient Infrastructure for Global Financial Services (HMC327)
AWS re:Invent 2025
11 What's New with AWS Lambda (CNS376)
AWS re:Invent 2025
12 Spec-Driven Development with Kiro (DEV314)
AWS re:Invent 2025
13 Amazon's finops: Cloud cost lessons from a global e-commerce giant (AMZ308)
AWS re:Invent 2025
14 Tick to trade latency trading platforms on aws
AWS re:Invent 2025
Government data
01 The AI Era: The Boundary Between Development and Design Is Disappearing
Government data
02 On-Device Multimodal AI and Smart-City Practice
Government data
03 Large-Model Capability Evaluation and a Method for Landing AI Projects
Government data
04 Controlled End-to-End Automation of Government Development with Cloud Agents
Government data
05 A New Software Ecosystem for the Agent Era, Seen Through Multi-Agent Systems
Government data
06 AI-Driven Macro Quantitative Research and Smart Governance
Government data
07 Authorized Operation of Public Data and Smart-Government Practice
Government data
08 Putting Data Assetization into Practice: Rights, Compliance, Engineering Governance, and Digital-Government Cases
Government data
09 AI for Mental-Health Public Welfare: Governance, Architecture, and Practice of a Trustworthy Platform
Government data
Amarathon 2025 Recap
01 A Developer’s Roadmap to Architecting for Agents
Donnie Prakoso
02 Amazon Bedrock Data Automation
Hafiz Syed Ashir Hassan
03 Multi-Agent on AgentCore
Tan Xin
04 Building Agentic AI Nova Act and Strands Agents in Practice
Haowen Huang
04 Accelerating Migration Projects with Kiro using Spec-Driven Development
Sanchit Dilip Jain
06 From Matching to Understanding: Personalized AI Search Practice Driven by AgentCore Memory
Liu Cao
07 Observe to Optimize – LLM Observability to AIOps Turning real-time insights into intelligent automation
Jimmy Soh
08 Deploying TEAM and Building the Best Engineering Team
Yuji Oshima
09 Five Hard Lessons from Five Years of So-Called Serverless Databases
Renato Losio
14 What if AI does my job How Q Developer CLI and Kiro have changed my daily routine
Miguel Angel Muñoz
16 Velocity with Vigilance: Security Essentials for Amazon Bedrock Agent Development
Brian Tarbox
26 Run OSS LLMs on a Single H100 Smarter, Cheaper, Faster
Adit Modi
28 A Modern Unified Metadata Architecture: New Approaches to Breaking Down Data Silos
Shaofeng Shi
29 Serverless MediaOps: Automating Video Workflows with AI on Amazon Web Services
Luis Valdivia
30 Architecting for Efficiency and Reliability with Performance Testing at Scale
Luis Guirigay
31 Connecting the World Through Open Source: Practical Journey of Technology, Community and Global Developer Relations
Richard Lin
33 Building Streaming Iceberg Tables for Real-Time Logistics Analytics
Fahad Shah
34 Accelerating Large-Scale Robot Strategy Training: An Automated Closed-Loop Architecture Based on Kiro, Trainium, and EKS
Junjie Tang
35 From Vibe to Viable with spec driven development
Ricardo Sueiras
36 Making Cloud Cost Analysis Smarter: Building FinOps Intelligent Agents with Strands and AgentCore
Xiaofei Li
37 Transform Conversational Agentic AIOps for K8s Using CNCF Kagent, K8sGPT, and Nova Sonic
Shaoyi Li

Video: https://www.youtube.com/watch?v=Ce9lJzpHBJ0

Lambda's Key Differentiation: Speed

● Main aspiration: Provide speed to customers and builders

● Speed encompasses code generation and the entire shipping cycle

● Faster shipping leads to more engaging applications and user feedback

● Applications are well-architected by default, handling scalability, availability, and reliability

● Primary benefit: No infrastructure to manage, reducing operations and increasing speed

Launches Bucketed into 3 Cohorts

1 New Primitives

● Introduction of Lambda's managed instances

2 Managed Instances

● Mental model: Lambda shines with needlepoint workloads (high traffic spikes)

● For steady-state traffic, builders seek to optimize performance and costs

● Allows choice of compute instances, network, or memory for performance optimization

● Retains full surveillance operations and developer experience

● Event source integrations, patching, routing, and scaling are handled by Lambda

● Introduces multi-concurrency for more efficient request handling

● Incorporates EC2’s pricing incentives (savings plans, reserved instances) for cost

optimization

3 Capacity Provider

● Additional requirement: Create a capacity provider

10.Specify preferences for compute instances, memory instances, or scaling profile

11.Optionally, let Lambda choose and continuously improve price performance

Use Cases and Features

● Steady State Applications: Seamlessly handled by Lambda

● Needlepoint Bursts/Traffic Management: Leave on Lambda for functionality

● Steady State/Popularity: Move to managed instances for optimization

● Performance Critical Apps: Specialized instances available

● Variety of Applications: Media data processing, web applications, event-driven applications

● Regulatory Requirements: Managed instances allow preferences for compute zones

Tenant Isolation Feature

● Use Case: scenario needing isolation between requests

● Benefit: Pass unique tenant ID or JWT token for clean, isolated execution environment

● Improves: CICD cycles and eliminates need for custom tooling

Well-Architected by Default

● Value Prop: Latest programming languages with performance and security fixes

● Recent Additions: Python 3.14, Java 2.5, Node.js 24

● Benefit: Improves developer productivity and enables safe, faster software shipping

● Mental Model: Make new runtimes available within 90 days of community release

Additional Features and Benefits

Runtime Upgrading

● Automated Patches: Handles vulnerabilities like Log4j

● Challenge: High effort to upgrade runtime for large function counts

● Solution: AWS Transform Custom (Gen AI-based upgrades)

● Benefit: Reduces tech debt by up to 85%, seamless integration into dev cycles

● Snapstart:

● Problem: Cold starts during function initialization

● Solution: Snapshots execution environment for faster subsequent invokes

● Benefit: Reduced cold start times, no code changes or custom tooling required

● Fault Injection Service (FIS) Integration:

● Purpose: Test application resilience under stress conditions

● Features: Specify conditions like increased latency or unavailable downstream services

● Benefit: Helps plan and prevent outages, increases confidence in production scenarios

● Improved Observability:

● Challenge: Lack of visibility into event source polling mechanisms

● Solution: Enabled additional CloudWatch metrics for count, log, and throttles

● Benefit: Instant issue detection and improved time to resolution, no custom tooling required

AWS Transform custom

AWS-managed, out-of-the-box transformations that are pre-built, AWS-vetted transformations

for common upgrade scenarios. These are ready to use without any additional setup.

Currently available transformations

● Java 8 to 17 migrations (for both Gradle and Maven)

● Node.js 12 to 22 upgrades (including Lambda environments)

● Python runtime updates to 3.11/3.12/3.13 (standard and Lambda)

● AWS SDK migrations (v1 to v2)

● Key characteristics:

● Validated by AWS - These transformations are vetted by AWS to be high quality

● Ready to use - No additional setup required

● Continuously growing - Additional transformations are continually being added

● Customizable - Pre-built transformations can be enhanced with specific rules for your organization's needs (e.g., adding rules for handling internal libraries or coding standards)

● Experimental support - Some transformations may be marked as experimental as they undergo further testing

Additional Features and Benefits

Additional CloudWatch Metrics

● Problem: Lack of visibility into event source polling mechanisms (Kafka, SQS)

● Solution: Enabled additional metrics for polar count, lag, and throttles

● Benefit: Instant issue detection and improved time to resolution, no custom tooling required

● Schema Registry Support for Avro Format:

● Problem: Builders had to manually add boilerplate code for Avro serialization/deserialization

● Solution: Added capability to auto serialize and deserialize Avro events

● Benefit: Less code, fewer errors, schema evolution support, no custom tooling required

● Provision Mode for SQS:

● Problem: Customers needed faster scaling and control over concurrency for polling

● Solution: Provision mode allows pre-warming capacity to handle spikes instantly

● Benefit: Eliminates delays during spikes, helps meet SLAs, optimizes costs, no custom tooling required

● Accelerating Developers:

● Goal: Help developers ship software faster

● Developer Preference: Develop in local machines/IDE after initial console testing

● Focus: Streamline development process to reduce time to market

Features to Accelerate Development Cycles

Seamless Console to IDE Transition

● Feature: Build bare-bones application on console with single click

● Benefit: Packages dependencies, lights up on local IDE for immediate coding without manual work

● Local Testing with LocalStack:

● Partner: LocalStack emulates AWS services (storage, database, networking)

● Benefit: Develop and test offline fully on local machine, leading to faster iterations of business logic without custom tooling

● Remote Debugging:

● Feature: Enable checkpoints/breakpoints with two clicks

● Benefit: Analyze variables and source code locally while code runs in production, test IAM policies, roles, database connectivity, VPC, and network configurations without custom tooling

● MCP Server for Best Practices:

● Feature: Bakes in best practices (input validations, error handling, status codes)

● Benefit: Generates better quality, consistent code, reduces code review cycles, accelerates velocity

● Support: Web apps and event-driven applications (Kafka triggers, event-driven architectures)