A quarter to a third of all cloud spend is wasted, about $44.5 billion in 2025 alone. And it isn't a visibility problem: the dashboards work and the recommendations are there. The waste is easy to see; it just never gets fixed.
Why don't cloud cost recommendations get implemented?
Because finding a saving and safely applying it are two different jobs, and the second is slow, manual, and always behind. Someone has to read the data, check what might break, and decide what's safe. A FinOps engineer can spend 20+ hours a week on this in AWS Cost Explorer. The cloud changes by the minute, reviews happen monthly, and the fix always loses to the roadmap.
What does the shift from human to agentic workflows look like?
It's already happening in software development: people stop doing the manual work and direct agents that do it. More than 40% of new code is now written by AI, and Gartner expects a third of enterprise software to be agentic by 2028. The engineer doesn't vanish. They move from writing code to reviewing it. Cloud operations follows the same path: agents collect the data, people make the call.
How do AI agents reduce cloud waste?
By running the analysis non-stop and handing you the context plus a safe, ready-to-apply runbook in seconds instead of weeks. This is the idea behind AlertD. The screenshots below are from a real AWS customer environment, with names anonymized.
Agents scan EC2, RDS, S3, EBS, EKS, DynamoDB, and more around the clock, so what's on screen is a finished result, not a starting point.
Every recommendation comes with evidence (idle time, usage, and CloudWatch metrics), so you judge the risk in seconds. One database here had been idle for 322 days, worth about $800 a month.
Many resources have more than one safe option (decommission, migrate, or right-size), each with its own saving, and each row shows how long it has waited.
Tags are never clean, so AlertD classifies resources from the data itself.
Then the step most tools skip: pick an action and get a ready-made runbook (Terraform, CloudFormation, or AWS CLI) for your own pipeline. AlertD stays read-only and never changes anything itself.
You also see what landed: here, $9,705 saved and a 42.7% lower bill. Next to it, Missed to date shows the savings still slipping away, about $2,500 a month while recommendations wait.
The Outstanding fleet shows every open recommendation, colored by how long it has waited, so an aging backlog can't hide inside a healthy-looking bill.
One idle database here had already lost about $1,000 while its fix waited 36 days.
Or just ask: AlertD Flappy answers questions on any report, using its real data.
Is agentic cloud cost optimization only about cost?
No, but cost gets the most attention from leaders right now. The same agents and data also cover security, compliance, performance, and drift. AlertD is built to operate your cloud at machine speed, not just watch it. The waste was never hard to see, only hard to act on fast enough. That's what always-on agents change.
AlertD is available on AWS Marketplace as AI for cloud operations. Learn more →
Frequently asked questions
What is agentic cloud cost optimization?
Always-on AI agents analyze your cloud, find savings with evidence, and generate the steps to apply them. That turns hours of work into seconds, while a human approves the final action.
How much cloud spend is typically wasted?
About a quarter to a third. A 2025 report put cloud infrastructure waste at $44.5 billion, mostly the gap between finding savings and applying them.
Why do most FinOps recommendations go unimplemented?
Applying them is manual, risky, and slow. It loses to the roadmap, and the cloud changes faster than reviews can keep up, so they pile up unused.
What are "outstanding" and "missed" cloud savings?
Outstanding savings are recommendations no one has applied yet. "Missed to date" is what they've already cost while waiting, since savings add up every day a fix is delayed.
Do AI agents change your cloud automatically?
No. AlertD stays read-only and never changes anything itself. It generates a runbook (Terraform, CloudFormation, or AWS CLI) that you review and apply through your own pipeline.
Sources: State of FinOps 2025 (FinOps Foundation); "FinOps in Focus," $44.5B cloud waste projection (Harness, 2025); cloud cost analysis benchmarks (Cloudaware, 2026); Gartner, AI code assistants and agentic AI forecasts (2024–2025); State of Code 2025, AI-generated code share.
A quarter to a third of all cloud spend is wasted, about $44.5 billion in 2025 alone. And it isn't a visibility problem: the dashboards work and the recommendations are there. The waste is easy to see; it just never gets fixed.
Why don't cloud cost recommendations get implemented?
Because finding a saving and safely applying it are two different jobs, and the second is slow, manual, and always behind. Someone has to read the data, check what might break, and decide what's safe. A FinOps engineer can spend 20+ hours a week on this in AWS Cost Explorer. The cloud changes by the minute, reviews happen monthly, and the fix always loses to the roadmap.
What does the shift from human to agentic workflows look like?
It's already happening in software development: people stop doing the manual work and direct agents that do it. More than 40% of new code is now written by AI, and Gartner expects a third of enterprise software to be agentic by 2028. The engineer doesn't vanish. They move from writing code to reviewing it. Cloud operations follows the same path: agents collect the data, people make the call.
How do AI agents reduce cloud waste?
By running the analysis non-stop and handing you the context plus a safe, ready-to-apply runbook in seconds instead of weeks. This is the idea behind AlertD. The screenshots below are from a real AWS customer environment, with names anonymized.
Agents scan EC2, RDS, S3, EBS, EKS, DynamoDB, and more around the clock, so what's on screen is a finished result, not a starting point.
Every recommendation comes with evidence (idle time, usage, and CloudWatch metrics), so you judge the risk in seconds. One database here had been idle for 322 days, worth about $800 a month.
Many resources have more than one safe option (decommission, migrate, or right-size), each with its own saving, and each row shows how long it has waited.
Tags are never clean, so AlertD classifies resources from the data itself.
Then the step most tools skip: pick an action and get a ready-made runbook (Terraform, CloudFormation, or AWS CLI) for your own pipeline. AlertD stays read-only and never changes anything itself.
You also see what landed: here, $9,705 saved and a 42.7% lower bill. Next to it, Missed to date shows the savings still slipping away, about $2,500 a month while recommendations wait.
The Outstanding fleet shows every open recommendation, colored by how long it has waited, so an aging backlog can't hide inside a healthy-looking bill.
One idle database here had already lost about $1,000 while its fix waited 36 days.
Or just ask: AlertD Flappy answers questions on any report, using its real data.
Is agentic cloud cost optimization only about cost?
No, but cost gets the most attention from leaders right now. The same agents and data also cover security, compliance, performance, and drift. AlertD is built to operate your cloud at machine speed, not just watch it. The waste was never hard to see, only hard to act on fast enough. That's what always-on agents change.
AlertD is available on AWS Marketplace as AI for cloud operations. Learn more →
Frequently asked questions
What is agentic cloud cost optimization?
Always-on AI agents analyze your cloud, find savings with evidence, and generate the steps to apply them. That turns hours of work into seconds, while a human approves the final action.
How much cloud spend is typically wasted?
About a quarter to a third. A 2025 report put cloud infrastructure waste at $44.5 billion, mostly the gap between finding savings and applying them.
Why do most FinOps recommendations go unimplemented?
Applying them is manual, risky, and slow. It loses to the roadmap, and the cloud changes faster than reviews can keep up, so they pile up unused.
What are "outstanding" and "missed" cloud savings?
Outstanding savings are recommendations no one has applied yet. "Missed to date" is what they've already cost while waiting, since savings add up every day a fix is delayed.
Do AI agents change your cloud automatically?
No. AlertD stays read-only and never changes anything itself. It generates a runbook (Terraform, CloudFormation, or AWS CLI) that you review and apply through your own pipeline.
Sources: State of FinOps 2025 (FinOps Foundation); "FinOps in Focus," $44.5B cloud waste projection (Harness, 2025); cloud cost analysis benchmarks (Cloudaware, 2026); Gartner, AI code assistants and agentic AI forecasts (2024–2025); State of Code 2025, AI-generated code share.
