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