FinOps · Spend, in plain terms

See where
the money goes.

Cloud spend hides in small places. Cindy finds it and adds it up: what is bigger than it needs to be, what has been forgotten, and what could be bought differently — no new dashboard to learn. Useful numbers, in plain words.

cindy · counting LIVE
01
Last month's bill is read
every line, every service
READ
02
Baseline traffic recognized
what you actually need
KNOWN
03
Looking for the bored compute
sized for traffic that never came
LOOKING
04
And the forgotten storage
attached to nothing, quietly billed
NOTING
05
A plan, when ready
each step reversible
WAITING

Your cloud spend, finally clear.

Insight and action in one place: what to do, how to do it, and who owns it.

Idle
capacity, found and reclaimed
Cindy spots capacity that's bigger than it needs to be and right-sizes it on approval — instead of leaving the recommendation in a dashboard while the meter runs.
“The capacity report acts on itself now.”
Same day
from cost spike to decision
A spike is flagged, explained, and paired with a fix the day it appears — before it compounds or locks the workload into a quarterly contract.
“The bill made sense before finance had to ask.”
Every
recommendation, ready to execute
Right-sizing moves come with the action attached — apply it on approval, no ticket, sprint, or change-window required.
“It tells me what to do, then does it.”

A clear answer to "where is the money?"

Cost dashboards tell you what you spent. Cindy tells you what you could stop spending without anyone noticing. Two different questions; Cindy answers the second one.

Bored compute, named

The services running far below capacity for weeks. The expensive ones, the small ones, the ones nobody got around to looking at.

The forgotten corners

Storage attached to nothing. Replicas nobody has spoken to. Addresses reserved years ago. Boring on their own; together, real money.

Things bought retail

Baseline spend that could be on a plan. Cindy sizes the commitment that fits — and the risk if traffic shifts.

Where AI spends money

ML workloads, where most of the cost lives in the least visible places. Where to spot, where to batch, where to size differently.

Variance, when it appears

Spend that suddenly moved. Cindy will tell you why, and whether you want to do anything about it. Sometimes the answer is no.

Plans, not lectures

When Cindy proposes a saving, it comes with a small plan. Reversible. Independently approvable. Each move tells you what it would unwind, before anything happens.

Outcomes a CFO measures

10–25%
cloud spend reduction by day 90
2–5%
margin lift on revenue-bearing workloads
Hours
from recommendation to applied change
Recovered
idle capacity, put back to work
Proprietary analytics. Fine-grained recommendations. One-click execution. The shortest path from cloud bill to cloud action.

"Find me $200k a month." And four moves to get there.

A finance lead asks a real question. Cindy looks in the four places waste tends to hide, draws a short plan, and waits to be told yes. The plan is staged; each step would be reversible if it surprised anyone.

cindy · in conversation LIVE
> find me $200k a month.
[LOOKING] Four places waste usually hides: things bigger than they need to be, things forgotten in corners, things bought retail that should be on a plan, and the expensive thinking.
> what did you find?
Roughly a quarter-million a month, scattered across a few dozen places. [STAGED] Four moves. None touch anything customer-facing. Each one reversible inside the day.
> approve all four.
[APPLIED] All four landed on your approval, no downtime. Applied across the estate through your existing automation. Witness filed; reversible until tonight if anything surprises us.
$247K
Found
4
Moves
0
Downtime
Where the money was hiding
compute bored for weeks · sized for a Tuesday that never came~$48K/mo
a replica nobody has spoken to in eleven days~$22K/mo
storage attached to nothing · last touched a month ago~$14K/mo
steady load paying retail · the same premium every month~$163K/mo
Sovereignty by design
Our own purpose-built LLM, hosted in your data center.
Your data remains yours
Your spend, capacity, and consumption data never leave your perimeter.
Zero hallucination
Cindy answers operational questions from your own data, grounded in what is real.
Bring your own tools
your existing cost tooling and infra automation stay in place. EveryOps sits on top.

What EveryOps gives back.

FinOps · spend recovered

The cloud spend EveryOps recovers.

Move the dials. Cindy shows how much EveryOps recovers from oversized compute, idle resources, and on-demand pricing that belongs on a plan.

in your browser
Your shape
Monthly AWS spend $500,000
$50K$1M$5M
Workload mix
Active regions3
ML / inference workloads
Steady traffic (vs spiky)
Cindy
waiting
RESTING
What I think we could find
$0
Move the dials and I'll tell you
Bigger than they need to becompute, databases, the usual
$0
Forgotten in the cornersstorage, replicas, addresses
$0
Paying on-demand instead of committed pricingyour steady baseline
$0
ML workload overheadyour ML workloads
$0
Runs entirely in your browser Private to this session Directional estimate, not a quote

See EveryOps run on your operations.

You just watched the scenario. Book a demo and we will point Cindy at a slice of your real stack. You decide nothing until you have seen exactly what Cindy recommends.