How do you catch a cloud cost spike before the invoice does?
An engineering AI Employee connected to your cloud billing and Slack. Every day it keeps a running spend baseline per service and account, flags whatever breaks out of it, attributes the likely driver, and alerts with the delta. Read only and alert only. It never touches a resource or a budget.
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Cloud bills surprise people because nobody is watching spend as it accrues. Cloud billing shows the truth eventually, but by default a spike surfaces at the end of the month, in the invoice, long after the resource that caused it has been running for weeks. A new instance type left on overnight, a service that started scaling past its usual ceiling, a region nobody meant to deploy to: each one is a small decision that turns into a line item no one recognizes. Cloud billing will answer 'how much did we spend' for any window you ask about, but it won't tell you, unprompted, that yesterday's spend on one service was 40% above its normal range. The common approaches don't close the gap. A monthly budget alert fires only after the month's total crosses a threshold, by which point the overspend has already happened many times over. A flat per service alert threshold treats a service that normally costs $50/day the same as one that normally costs $5,000/day, so it's either too noisy or too blind. And none of it tells you why: a dashboard shows that the number moved, not what moved it.
An AI Employee runs on a daily schedule with read only access to your cloud billing, resuming its own memory of what each service and account normally spends rather than starting blind every morning. It pulls the prior day's spend broken out by service and by linked account, updates its running baseline for each, and flags anything that breaks out of its own normal range, not a flat threshold, but a deviation from what that specific service in that specific account usually costs. For each anomaly it works out the likely driver and posts one alert to Slack with the delta and the suspected cause. It writes nothing back: no resource is touched, no budget is changed.
See exactly how the work gets done.
Runs on a daily schedule, resuming its baseline
A daily schedule prompts the AI Employee again, and it resumes its own memory of what each service and account normally spends instead of starting blind. The baseline gets sharper the longer it runs.
Carries your anomaly rules
What counts as a break from baseline, how much history to weigh a service's 'normal' against, and how to read a spike's shape (sudden versus ramping, one account versus many) live as skills and memory that travel with the AI Employee. When a spike turns out to be a planned load test or a known seasonal pattern, that's written down and it stops flagging it.
Connects cloud billing read only, with permissions you set
It reads daily cost and usage by service and linked account, plus resource level detail for the flagged period to support the driver attribution, and posts to Slack. Credentials stay in memory, never written to disk, never exposed to the model.
Attributes the likely driver, not just the delta
A number moving is not an explanation. For every anomaly it checks what changed underneath (a resource that came online in the window, usage consistent with a traffic surge, spend appearing in a region that previously had none) and states its best guess driver alongside the delta, so the alert is something a person can act on immediately.
Alerts with the delta and the suspected cause
Each day brings at most one Slack alert per anomaly: the service and account, the size of the deviation from baseline, and the suspected driver. No anomaly means no message. The engineering team reads it and decides whether to act. Nothing changes on its own.
Runs in your environment
Every run happens in isolation inside your own infrastructure; your data never leaves it. The run can reach only cloud billing, and only the Slack alert leaves.
Permissions you set
The cloud billing credential stays in memory, injected at run time, never written to disk, never exposed to the model or the logs.
Read only, no exceptions
The billing connection is read only. The AI Employee cannot launch, modify, or delete a resource, and it cannot change a budget or any spending control. It can only alert.
Alert only
The Slack post is the only output. No remediation, no auto scaling change, no resource shutdown. A person decides what, if anything, to do about a spike.
You own it
The baseline logic, thresholds, and per system permissions are yours, versioned and changed on your terms, not in a vendor dashboard.
A cost problem that used to surface as a surprising line item at the end of the month now surfaces the next morning, with the service, the account, the size of the spike, and a suspected cause already attached. The AI Employee only reads and alerts; the engineering team decides what to do about each one.
Every day
Spend baselined per service and account against its own history
Read only
Nothing modified, launched, or deleted, ever
1 alert
Delta plus suspected driver, posted only when spend breaks the baseline
Our free AI audit shows you where AI fits, what your security risks are, and gets your first AI employee working.
How do AI Employees keep your docs in sync with the code?
Sweeps the code merged each day, rewrites the docs those changes touched, and opens one reviewable pull request. Publishing waits for a human merge.
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How do AI Employees test every pull request before a human reviews it?
Checks out every pull request, runs the suite, exercises the change through the edge on a test deploy, and posts the result. It stays off production, leaving the merge to a person.
×How do you get a postmortem drafted the moment an incident resolves?
Reconstructs the incident timeline, correlates it against deploys and log spikes, and opens a structured postmortem as a doc PR. It drafts, and the team finalizes.
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