Forecast cloud cost commitment exposure

What it's for

Shows the board how much a cloud commitment could save, waste, or leave exposed across demand futures instead of presenting one utilization average.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "commitment_unit_cost": 0.6,
  "committed_capacity_per_period": [
    100,
    100,
    100,
    100
  ],
  "on_demand_unit_cost": 1,
  "scenarios": [
    {
      "id": "low",
      "probability": 0.2,
      "usage_units": [
        50,
        60,
        70,
        80
      ]
    },
    {
      "id": "base",
      "probability": 0.5,
      "usage_units": [
        90,
        100,
        110,
        120
      ]
    },
    {
      "id": "high",
      "probability": 0.3,
      "usage_units": [
        120,
        140,
        150,
        160
      ]
    }
  ],
  "tail_probability": 0.1
}

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

{
  "assumptions": [
    "Usage paths preserve workload, region, service, family, tenancy, and eligibility constraints that determine whether committed capacity can actually offset usage.",
    "Rates include effective discounts, fees, credits, taxes, term timing, exchange, and opportunity cost consistently; the on-demand path is a comparable counterfactual.",
    "The forecast is conditional on represented joint demand paths and excludes resale, contract modification, and provider behavior unless encoded explicitly."
  ],
  "configuration": {
    "commitment_unit_cost": 0.6,
    "maximum_negative_savings_probability": 0.2,
    "on_demand_unit_cost": 1,
    "tail_probability": 0.1
  },
  "decision": "cloud_commitment_exposure_within_tolerance",
  "method": "cloud_commitment_joint_path_exposure_v1",
  "scenario_diagnostics": [
    {
      "committed_bill": 240,
      "on_demand_counterfactual": 260,
      "probability": 0.2,
      "savings": 20,
      "scenario_id": "low",
      "uncovered_on_demand_cost": 0,
      "unused_commitment_cost": 84,
      "utilization_rate": 0.65
    },
    {
      "committed_bill": 270,
      "on_demand_counterfactual": 420,
      "probability": 0.5,
      "savings": 150,
      "scenario_id": "base",
      "uncovered_on_demand_cost": 30,
      "unused_commitment_cost": 6,
      "utilization_rate": 0.975
    },
    {
      "committed_bill": 410,
      "on_demand_counterfactual": 570,
      "probability": 0.3,
      "savings": 160,
      "scenario_id": "high",
      "uncovered_on_demand_cost": 170,
      "unused_commitment_cost": 0,
      "utilization_rate": 1

How it works

Forecasting & survival — Estimate when something completes or fails, with censoring and unresolved work handled honestly rather than dropped.

1. 1 Freeze the eligible service/region/family boundary, commitment capacity schedule and effective rate, comparable on-demand counterfactual, horizon, and aligned joint usage paths.
2. 2 For each path price paid commitment, unused capacity, uncovered on-demand usage, counterfactual cost, utilization, and savings without netting incompatible workloads.
3. 3 Aggregate expected and tail savings, expose negative-savings probability and worst paths, then stress portability, exchange, fees, credits, and demand before a purchase decision.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Calibrating it to your org

Calibration workflow

1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.

gitrevio_capabilities_search
  { "q": "forecast cloud commitment waste uncovered ondemand" }
  → finds "forecast_cloud_cost_commitment_exposure"

gitrevio_capability_describe
  { "capability_id": "forecast_cloud_cost_commitment_exposure" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "forecast_cloud_cost_commitment_exposure", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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