Cost Optimization & FinOps

Build a culture of accountability for cloud spend.

3 sessions at the summit4 external resources

Overview

FinOps is the operational practice of bringing financial accountability to the variable spend model of cloud. AWS provides AWS Cost Explorer, AWS Budgets, AWS Cost & Usage Reports (CUR), and AWS Compute Optimizer to find waste. Savings Plans and Reserved Instances commit to usage for steep discounts. Tagging strategy and account structure (AWS Organizations, AWS Control Tower) are the foundation. The FinOps Foundation defines a phased maturity model: Inform → Optimize → Operate.

Key concepts

  1. Right-sizing, scheduling, and Spot for variable workloads
  2. Savings Plans (Compute, EC2 Instance, SageMaker) vs. RIs
  3. Tagging strategy and showback/chargeback
  4. Architecting for cost: serverless, Graviton, S3 storage classes
  5. Cost anomaly detection and forecasting

Key AWS services

  • AWS Cost Explorer
  • AWS Budgets
  • AWS Compute Optimizer
  • Savings Plans
  • AWS Trusted Advisor

Learn more — curated resources

Hand-picked official docs, foundational papers, and the best community guides for going deeper on this topic.

Sessions on this topic

3 sessions from the Summit covered this topic. Each is a self-contained mini-lesson.

  1. AIM201Intermediate

    From demo to deployment: solving agentic AI's toughest challenges

    Most AI agent projects stall when moving from prototype to production. This session tackles the top challenges builders face when deploying agentic AI at scale. You'll learn how to answer the fundamental question of whether to build custom agents or leverage pre-built agents for DevOps, security, development, and business productivity use cases. Then you'll discover how to address the critical production challenges of reliability, observability, cost management, security, and evaluation. Drawing from real customer deployments and AWS's portfolio of agentic AI capabilities, you'll gain actionable approaches for building agents that don't just demo well but ship and scale.

  2. COP302Advanced

    Applying AI for FinOps and FinOps for AI

    Explore the intersection of AI and FinOps in this advanced session. First, discover how Kiro CLI can simplify AWS cost management by analyzing trends, explaining spend, and recommending optimizations like rightsizing and Savings Plans. Then, dive into FinOps for AI- learn how to track and control generative AI costs across Amazon EC2, Amazon SageMaker, Amazon Bedrock, and more. We'll share architecture patterns, cost-saving strategies, and real-world examples to help you build scalable, production-ready AI solutions while staying on budget. Whether you're optimizing existing workloads or launching new AI initiatives, you'll leave with practical tools to maximize value.

  3. DAT401Expert

    Real-Time DataLakes with Apache Iceberg, Amazon MSK, and Amazon S3

    Learn how to optimize Apache Iceberg data lakes on Amazon S3 for cost-effectiveness while enabling real-time analytics. This session explores S3 Tables deployments, focusing on streaming data from Apache Kafka via Amazon MSK into Iceberg format. Discover practical approaches for real-time table maintenance, metadata optimization for high-velocity writes, and data compaction strategies. Implement cost-effective retention policies using S3 Lifecycle configurations while maintaining sub-minute data freshness. See how MSK's native Iceberg integration eliminates pipeline overhead, reducing latency and operational costs. Gain actionable insights for balancing streaming performance with cost optimization at scale.

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Non-obvious insights

From the Playbook

One sharp, contrarian insight per session — the things teams don't think of unprompted.

The single highest-leverage practice in agent ops is the offline eval suite. It's tedious to build but it unlocks everything downstream — model upgrades, prompt iteration, regression testing, vendor swaps. Teams that skip evals end up trapped on a single model and prompt forever. ---AIM201 — From demo to deployment: solving agentic AI's toughe…