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? Pick as many as apply.

How many machines?

How busy is the backend?

Web, mobile apps, or both?

How many sessions a month? About the same as your monthly users.

or type it

How fast does it load for real users?

How often does an error stop someone finishing?

or enter monthly volumesGBM metrics

The short version

You'd spend ₹53.4 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 for your backend

-₹3.43 L

180 GB of logs and traces and 713 M metrics a month, on the ₹40 per GB and ₹30 per million metrics rate card. We confirm the exact price before you sign anything.

KloudMate for real user monitoring

-₹9.14 L

5,000,000 sessions a month. You ingest % of them, and each one sends about KB of spans and errors. Recording % of them for replay adds KB each. That comes to 1,904 GB a month. Replay is the expensive part. Turn either one down and this line drops.

What you'd save

+₹53.4 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

Around every incident, checkout still works but crawls for hours, and that loses you sales too. We count 29.2 hrs of it a year, at 8% of what a full outage hour costs. This is incident hours only. We count everyday slowness further down.

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

Revenue in the browser

None of this shows up as downtime. Slow loads, and errors that only hit some devices or some releases, lose you orders while every backend dashboard stays green.

Revenue that runs through the site

₹300 Cr

% of the ₹300 Cr you sell online.

Conversion you'd win back by getting faster

₹90.0 L

The pages that matter sit at s LCP p75 today. Getting them to s is 500 ms. Across Zalando, Amazon, Walmart, Farfetch and Vodafone, 100 ms is worth about % more conversions, so that is 5%. We apply it to the % of that revenue running through those pages. Then we count % of the result, because seeing the problem is not the same as fixing it. That comes to 0.3% of the revenue above.

Orders lost to errors nobody sees

₹78.7 L

About % of sessions hit a JavaScript error that stops them finishing, and % of those give up instead of retrying. That is ₹1.57 Cr a year at risk, and none of it pages anyone, because the API returned 200. KloudMate groups those errors by page, release, and device, so you find them. We count the % you'd actually fix.

Revenue you'd recover

₹1.69 Cr

First year, after paying for KloudMate

₹2.08 Cr

cash in full, 75% of the rest

Pays for itself in

0.6 mo

within the first year

Three-year net

₹7.28 Cr

cash plus the rest, at steady state

The number above assumes issues get resolved 25% faster and 10% fewer things break, and that you act on 20% of what real user monitoring shows you. If issues are only resolved 10% faster, nothing breaks any less often, and you act on a tenth of it, the first year comes to ₹1.41 Cr instead of ₹2.08 Cr.

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.

The conversion figure is borrowed. It is the median of published studies from Zalando, Amazon, Walmart, Farfetch, Vodafone and Aberdeen, not something we measured on your traffic. Those studies are correlational: the people whose pages load fast also tend to be on better devices and better connections, so some of the gap was never about speed. That is why we default the attribution figure low, and why we cap the uplift we count at 10% however much speed you win back. The per-session data volumes come from real production ingest, but from one customer across three sites, so treat 250 KB as a good default rather than a law. Replay runs from about 0.6 MB a session on a light content site to 1.8 MB on a busy storefront. Its heaviest 5% of sessions account for a third of all replay bytes.

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.