Category: Artificial Intelligence

In this post, we introduce the Kiro Cost Optimizer, an open-source, serverless solution that gives you per-developer visibility into Kiro Enterprise credit usage. It ingests Kiro’s daily per-user activity reports, accumulates month-to-date usage per developer, and delivers a consolidated usage visibility report with tier optimization recommendations via email on a schedule you control. The entire stack deploys in minutes with a single AWS CloudFormation template, costs less than a few dollars a year to operate, and requires zero ongoing maintenance.

This blog post helps you measure what your coding work costs across the models, harnesses, and hosting paths available to you, so you can turn the measurements into a cost-and-quality frontier, and put that frontier in front of developers before they choose a model.

FinOps practitioners used to spend hours on manual cost analysis and reporting. Today, tooling such as the AWS FinOps Agent, AI-powered cost investigations, and Amazon Q Developer cost capabilities can trace costs to a specific resource and surface opportunities in minutes. But without someone providing business context, they’re just numbers. As AI increases both speed […]

At this year’s re:Invent, we’ve lined up a fantastic set of sessions to get you up to speed on the latest AI cost management frameworks, solutions, and real-life success stories. They will help you wrap your head around concepts, such as tokenomics, and get ahead of the AI cost management game. Across tracks, more than 30 sessions fall under the theme of cloud financial management and AI cost management. In this blog post, I highlight nine of them and share where you can find AWS FinOps experts and peers throughout the week.

Tokens are the currency models work in. Every model takes tokens as input and produces tokens as output, and those tokens can come from anywhere: a person typing a prompt, an application making a call, or another AI agent talking to your model through an MCP server. In this blog, we focus on the tokens generated by AI coding tools and AI solutions. If you’re a FinOps practitioner trying to get ahead of AI spend, you need a framework to work from. That’s what this blog is for. We’re going to use the AWS Cloud Financial Management (CFM) pillars you may already know, See, Save, Run, and Plan, but with a tokenomics lens. They will be Visibility & Attribution, Optimization, Governance, and Value/ROI. We have created a landing page going deeper into these subjects on the Tokenomics CFM Tips page.

Past-due balances are easy to see and hard to explain. The AWS Management Console shows the amount, but not the failed payment or the reason for decline. Previously, finding the reason required opening a support case. With payment capabilities in Amazon Q, now available in the AWS Management Console, you can investigate a past-due balance, understand why a payment failed, and take the corrective action in a single conversation. If you work in accounts payable or FinOps, you can eliminate most of the manual reconciliation a past-due notice normally triggers.

AWS introduced the FinOps Agent (currently in public preview) at FinOps X 2026. FinOps automation is still relatively new, and given the sensitivity around cloud and AI cost management, it’s understandable if you’re hesitant to go all-in on autonomous FinOps right away. In this post, we’ll share our mental model for FinOps automation, the four tiers of automation you can follow, and the four built-in trust levers you can rely on to stay in control as automation scales.

If every dollar invested in artificial intelligence (AI) generated a two-dollar return, rising costs would signal positive ROI rather than inefficiency. However, establishing a relationship between AI spend and business value can be complex, leaving you without clear metrics on when to scale or recalibrate initiatives. To calculate ROI (refer to Figure 1), you start by allocating your costs, then you align them to the business outcome they will support. This gives you a Cost per Outcome, the building block for ROI. Once you can measure what each outcome costs, calculating ROI is simply a matter of comparing that cost against the value it delivers. This post outlines a practical methodology for aligning AI investments with business impact. We detail how to classify use cases, attribute costs, and identify the initiatives that drive measurable value.

​​​FinOps practitioners now have access to a growing lineup of AI tools, but matching the right tool to each use case is the difference between accelerating your practice and adding unnecessary complexity. AWS designed each tool in the AI lineup for different context and a different type of work. ​​In this blog we are going to go […]

As you scale your use of large language models (LLMs), a type of foundation model (FM) on Amazon Bedrock, costs increases. But traditional cloud cost tools only tell you how much you spent, not why. That gap between billing data and operational insight is where cost inefficiencies go undetected. Optimizing LLM costs requires visibility into […]