Value worksheet

What would KloudMate actually save you?

Answer a few simple questions to find the ROI on your observability spend. Every number is editable, and the whole thing fits on a link you can send to your CFO.

What kind of business is this?

Roughly how much do you sell online in a year?

or type it (₹)

How many engineers touch production?

or type it

How often does something break badly enough that customers notice?

What do you spend on monitoring today, per year?

or type it (₹/yr)

What are we monitoring?

How many machines?

How busy is it?

or enter monthly volumesGBM metrics

The short version

You'd spend ₹62.6 L less a year on monitoring than you do now.

That's before counting a single hour of downtime you avoid. Everything below is on top of it.

Scenario

Spend

What you pay for monitoring now, against what you'd pay us. Your finance team can check both against invoices.

Monitoring tools you pay for now

₹60.0 L

Your own time keeping it running

₹6.00 L

engineers on it, at (₹12.0 L) each a year.

KloudMate

-₹3.40 L

Estimated for a setup your size, on the ₹40 per GB and ₹30 per million metrics rate card. We confirm the exact price before you sign anything.

What you'd save

+₹62.6 L

Losses you avoid

Money you probably won't lose, which isn't the same as money in the bank.

Downtime you'd avoid each year

9.8 hrs

You lose about 30 hrs a year now, if each incident runs hours. We assume % fewer incidents and fixes that are % faster.

What those hours were costing you

₹9.35 L

An hour of downtime costs you about ₹3.84 L: ₹300 Cr of sales spread over 8,760 hours, times because things break when you're busiest, less the % of orders that come back later. But a typical incident only hits % of your customers, so we count ₹95,890 an hour × 9.8 hrs.

Slow but not down

₹8.98 L

A checkout that still works but crawls loses you sales too. We count 29.2 hrs of it a year, at 8% of what a full outage hour costs.

Losses you'd avoid

₹18.3 L

Time you get back

Fewer tickets to handle and faster answers when something does break, so support needs fewer people.

Alerts your team sees now

36,000 a year

50 machines firing about each a month. Kubernetes and busy systems fire more. Large fleets get tuned harder, so the rate per machine drops.

KloudMate AI/ML alert grouping

7,200 left

% fewer tickets. Correlation folds duplicates and alerts sharing a root cause into one.

Hours not spent handling tickets

2,400 hrs

28,800 fewer, at minutes of attention each on average.

Hours not spent investigating

504 hrs

864 real investigations a year (% of what's left), minutes by hand against with the AI assistant.

Support people needed

81% less

About 2 people carry this support load today, dropping to under one afterward. 3,576 hours a year of ticket and investigation work drops to 672. The rest of the team goes back to building.

An engineer costs

₹667 an hour

Working back from (₹12.0 L) a year, across 1,800 working hours.

What that time is worth

₹19.4 L

First year, after paying for KloudMate

₹90.9 L

soft value ramped 75% in year one

Pays for itself in

0.4 mo

within the first year

Three-year net

₹2.91 Cr

cash plus soft value, steady state

The number above assumes issues get resolved 25% faster and 10% fewer things break. If issues are only resolved 10% faster and nothing breaks any less often, the first year comes to ₹80.6 L instead of ₹90.9 L.

Assumptions

How much of the benefit lands in year one. Nothing improves on day one, so we don't count the full amount.%

What one engineer costs you a year, including benefits and overhead.(₹12.0 L)

Hours one engineer works in a year.1,800 hrs

Blast radius and the IT-caused share are load-bearing. Without them the model assumes every incident takes down the whole business, and the number stops being defensible. Every figure in the lines above is editable, and your edits travel with the link.

These are estimates based on what you told us, not a quote. We'll give you a real price once we know how much data you send us.