Key points
- On $5M a year of cloud spend, a typical 12 percent of addressable waste is $600,000, and cleaning it at T minus 6 months means the forecast the vendor prices is the estate you intend to run, not the one you accidentally accumulated.
One export, no agents, four deliverables
The optimizer's input is deliberately humble: the billing export your cloud provider already produces, AWS CUR, the Azure or GCP cost export, or the OCI report. No agents to deploy, no IAM roles to request, no six week integration before the first insight, which means the analysis can happen at procurement's speed as well as engineering's. Upload the file and four things come back.
Savings by workstream. Findings grouped the way work is actually assigned: rightsizing, idle and orphaned resources, storage tiering, scheduling, and commitment coverage, each stream priced, so the conversation starts at "which of these five efforts do we staff" instead of at line 40,000 of the export.
The executive brief. The two page version for whoever funds the engineering time, in the house exec-brief standard: the total on the table, the top workstreams, and what the first quarter of effort returns.
The full report. The evidence layer beneath it, finding by finding, for the platform team that will do the work and rightly distrusts summaries.
The phased action plan. The part dashboards never ship: findings sequenced into phases by effort and payback, with owners, and tracked as work completes, so realized savings accumulate against the plan the way the savings proof discipline demands, instead of evaporating into "we did some rightsizing."
app.isvcosell.com/tooling/cloud
The optimizer: savings by workstream, the brief, the evidence, and the plan that gets tracked to done.
THE SAME JOB, TWICE
TODAY, BY HAND
The FinOps dashboard shows the oversized instances, the wrong-tier storage, and the weekend-running dev environments, in vivid color, for six consecutive quarters.
The findings have no owners, no sequence, and no payback order, so the engineering time never gets staffed.
Engineering optimizes on its own rhythm while procurement signs the committed-spend deal on the renewal's rhythm, and the two never meet.
The company commits to spend it has not cleaned, locking the waste in at a discount.
The waste is observed daily and removed never
WITH ISVCOSELL
Upload one billing export, the AWS CUR, the Azure or GCP cost export, or the OCI report, no agents, no IAM roles, no six-week integration.
Read the savings by workstream, rightsizing, idle resources, storage tiering, scheduling, and commitment coverage, each stream priced.
Work the phased action plan, findings sequenced by effort and payback with owners, tracked as work completes, plus the two-page exec brief that funds it.
Before the commit renewal, benchmark the committed-spend discount against modelled deal cohorts and run the cross-provider workload comparator.
One upload to a funded, phased plan
What changes: the gap between seeing and done closes, and the sequence becomes optimize first, commit second. On $5M a year of cloud spend, a typical 12 percent of addressable waste is $600,000, and cleaning it at T minus 6 months means the forecast the vendor prices is the estate you intend to run, not the one you accidentally accumulated.
"Committing to spend you have not cleaned locks the waste in at a discount, which is the most expensive way to buy garbage."
PART TWO
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The commit renewal is why the timing matters
For most enterprises the cloud negotiation is the committed-spend agreement, the AWS EDP, the Azure MACC, the GCP commit, and its central input is a forecast of your own consumption. Every dollar of waste in the baseline inflates that forecast, and an inflated commit is a double loss: you either burn real engineering effort later to hit a number you never needed, or you fall short and face the shortfall conversation. The sequence that avoids both is mechanical: optimize first, commit second. Run the optimizer, execute the fast phases, and size the commitment from the estate you intend to run, not the one you accidentally accumulated.
Then negotiate the commit itself like the deal it is. Committed-spend discounts have a market, the library benchmarks them against modelled deal cohorts, and the percentile tells you whether the offered tier is generous or merely presented that way. The cloud workload comparator adds the cross-provider check, pricing the same inventory on the other clouds, less because you will move than because a priced alternative changes what the incumbent offers, the same credible-at-the-margin logic that works everywhere else. Optimization sets the size, the benchmark sets the rate, and both belong in the war room as one position.
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The desk around the optimizer: workload comparison and commit sizing, so the cleanup feeds the negotiation.
PART THREE
Running it as a rhythm, in four rules
1 Always before a commit. The optimizer runs mandatory at T minus 6 months on every committed-spend renewal, so the forecast the vendor sees is the cleaned one. This single rule captures most of the tool's lifetime value.
2 Phase by payback, not by purity. The plan front-loads the findings that are cheap to execute and large to bank, scheduling and idle cleanup before architectural virtue. Early banked savings buy the political capital the later phases need.
3 Track realized against planned. Completed actions reconcile against the next month's bill, verified the same way negotiated savings are. A plan whose phase one visibly landed is a plan whose phase two gets staffed.
4 Re-run quarterly, because waste regrows. Cloud estates drift the way license estates do: every quarter of shipping recreates idle resources and oversized defaults. The re-run is an upload, so the cadence actually survives contact with busy quarters.
The honest limit: a billing export sees what billing sees. It will find the oversized fleet, the unattached volumes, the weekend-running non-production, and the commitment coverage gaps, which is where most of the money is. It will not redesign your architecture, see inside a Kubernetes cluster's bin-packing, or make the microservice stop chattering across regions, and findings that require re-architecture are labeled as such rather than dressed as quick wins. The optimizer's job is the first, largest, most stalled tranche of cloud savings, and the plan that finally moves it from the dashboard to the bank.
About the author
Fredrik Filipsson, Cofounder, ISVCOSELL
Fredrik has spent more than twenty years in enterprise software, with time at Oracle, IBM, SAP, and Salesforce before moving to the buy side. He structured and priced the kind of large agreements most buyers only see once or twice in a career, which taught him where the leverage sits and how far a vendor will actually move. He started ISVCOSELL to hand that knowledge to every sourcing team.
More posts by Fredrik Connect on LinkedIn →
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