Calculate feature cost to serve

What it's for

Shows which capabilities remain expensive to operate per genuinely adopting customer after build, infrastructure, usage, and support costs are reconciled.

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.

{
  "features": [
    {
      "adopted_accounts_scenarios": [
        10,
        20,
        30
      ],
      "amortized_build_cost_per_period": 100,
      "fixed_run_cost_scenarios": [
        50,
        60,
        70
      ],
      "id": "analytics",
      "support_cost_scenarios": [
        20,
        30,
        40
      ],
      "usage_volume_scenarios": [
        100,
        200,
        300
      ],
      "variable_cost_per_usage_unit": 0.1
    }
  ],
  "maximum_expected_cost_per_adopted_account": 20,
  "scenario_probabilities": [
    0.2,
    0.5,
    0.3
  ]
}

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": [
    "Build amortization, fixed run, variable usage, and support cost share one accounting period, currency, capitalization/allocation policy, and feature boundary without omitted shared cost.",
    "Adopted accounts represent verified business use rather than license assignment, feature exposure, commits, tickets, or raw event volume.",
    "Cost per adopted account is undefined in zero-adoption scenarios; their probability remains explicit rather than silently dividing by a small constant."
  ],
  "configuration": {
    "maximum_expected_cost_per_adopted_account": 20,
    "scenario_count": 3,
    "tail_probability": 0.1
  },
  "decision": "feature_cost_to_serve_within_threshold",
  "feature_diagnostics": [
    {
      "cvar_cost_per_adopted_account": 18,
      "exceeds_cost_threshold": false,
      "expected_adopted_accounts": 21,
      "expected_cost_per_adopted_account": 11.25,
      "expected_total_cost": 213,
      "feature_id": "analytics",
      "probability_zero_adopted_accounts": 0
    }
  ],
  "method": "fully_loaded_feature_cost_to_serve_scenarios_v1",
  "summary": {
    "feature_count": 1,
    "portfolio_cvar_cost_per_adopted_account": 18,
    "portfolio_expected_adopted_accounts": 21,
    "portfolio_expected_cost_per_adopted_account": 11.25,
    "portfolio_expected_total_cost": 213,
    "portfolio_probability_zero_adopted_accounts": 0
  },
  "truncated_feature_count": 0
}

How it works

Decision analysis — Turn uncertainty, cost and risk appetite into a defensible choice, with the reasoning left inspectable.

1. 1 Freeze the feature/accounting boundary, amortized build cost, fixed run and support paths, variable usage economics, and verified adopted-account paths over one period and currency.
2. 2 Calculate scenario total cost and cost per adopted account only where adoption is positive, retaining zero-adoption probability explicitly, then aggregate portfolio unit cost and CVaR.
3. 3 Expose threshold breaches and reconcile shared-cost allocation and adoption evidence before comparing features or feeding product/pricing decisions.

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": "calculate fully loaded feature cost and" }
  → finds "calculate_feature_cost_to_serve"

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

gitrevio_capability_run
  { "capability_id": "calculate_feature_cost_to_serve", "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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