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Calcrivo

Cloud Spend Analyzer

Break a monthly cloud bill into categories, measure month-on-month movement and flag concentration and anomalies.

Inputs

USD/month
USD/month
USD/month
USD/month
USD/month

Observability, security tooling, support and marketplace charges.

USD
USD/month
USD/month
%

Total Monthly Spend

$260,000.00

Change on Last Month

$15,000.00

Change on Last Month as a Percentage

6.1%

Largest Category

Compute

Compute Share of the Bill

46.2%

Share in the Top Five Accounts

73.1%

Unallocated Share

11.5%

Anomaly Check

Within the expected month-on-month band

Step by step

  1. Values used

    Compute spend = 120,000 USD/month; Storage spend = 42,000 USD/month; Network and egress spend = 18,000 USD/month; Managed database spend = 55,000 USD/month; Everything else = 25,000 USD/month; Previous month total = 245,000 USD; Spend in the top five accounts = 190,000 USD/month; Spend with no allocation tag = 30,000 USD/month; Anomaly threshold = 10 %

  2. Cloud Spend Analyzer

    total = Σ category spend; month-on-month change% = (total − prior total) ÷ prior total; concentration% = top five accounts ÷ total; unallocated% = untagged spend ÷ total.

  3. Total Monthly Spend

    = 260,000.00

  4. Change on Last Month

    = 15,000.00

  5. Change on Last Month as a Percentage

    = 6.1

  6. Largest Category

    = Compute

  7. Compute Share of the Bill

    = 46.2

  8. Share in the Top Five Accounts

    = 73.1

How it works

Category shares tell you where an optimisation programme should aim, while month-on-month movement against a threshold is the cheapest anomaly detection there is. Concentration matters separately: if most of the bill sits in a handful of accounts, that is where governance and commitment decisions have leverage, and a small unexplained change there outweighs a large one elsewhere. A single total tells you nothing actionable, whereas share, movement and concentration together point at the account and the service to open first. Pull the category figures from your provider's cost reports, since tagging gaps and credits can make the same bill look different depending on the view.

Formula

Cloud Spend Analyzer

total = Σ category spend; month-on-month change% = (total − prior total) ÷ prior total; concentration% = top five accounts ÷ total; unallocated% = untagged spend ÷ total.

concentration
Share of the bill sitting in the five largest accounts or subscriptions
unallocated share
Portion of the bill that cannot be attributed to a team or product
anomaly threshold
Month-on-month movement large enough to warrant investigation

Frequently Asked Questions

How is Cloud Spend Analyzer calculated?

total = Σ category spend; month-on-month change% = (total − prior total) ÷ prior total; concentration% = top five accounts ÷ total; unallocated% = untagged spend ÷ total. Category shares tell you where an optimisation programme should aim, while month-on-month movement against a threshold is the cheapest anomaly detection there is. Concentration matters separately: if most of the bill sits in a handful of accounts, that is where governance and commitment decisions have leverage, and a small unexplained change there outweighs a large one elsewhere.

Why does Cloud Spend Analyzer matter?

A single total tells you nothing actionable, whereas share, movement and concentration together point at the account and the service to open first. Pull the category figures from your provider's cost reports, since tagging gaps and credits can make the same bill look different depending on the view.

What values do I need to enter?

This calculator takes 9 inputs: Compute spend, Storage spend, Network and egress spend, Managed database spend, Everything else, Previous month total, Spend in the top five accounts, Spend with no allocation tag, Anomaly threshold. The pre-filled defaults are a realistic starting point — replace them with figures from your own environment for a result you can act on.

Why treat a spend drop as an anomaly?

A sudden fall is more often a broken pipeline, a lapsed tag or a delayed usage record than a genuine saving, and it usually corrects itself in the next invoice with a spike. Investigating drops as well as rises is what keeps the forecast trustworthy.

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