Optimize cloud reserved capacity

What it's for

Moves cloud commitment from a utilization dashboard to an auditable integer purchase plan that prices demand coverage, fallback scarcity, unused capacity, and catastrophic under-provisioning together.

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.

{
  "demand_scenarios": [
    {
      "demand_units_by_period": [
        10,
        10
      ],
      "id": "high",
      "on_demand_capacity_limit_by_period": [
        20,
        20
      ],
      "on_demand_unit_cost_by_period": [
        1,
        1
      ],
      "probability": 0.5,
      "reserved_capacity_realization_fraction_by_period": [
        1,
        1
      ],
      "spot_capacity_limit_by_period": [
        0,
        0
      ],
      "spot_unit_cost_by_period": [
        2,
        2
      ]
    },
    {
      "demand_units_by_period": [
        5,
        5
      ],
      "id": "low",
      "on_demand_capacity_limit_by_period": [
        20,
        20
      ],
      "on_demand_unit_cost_by_period": [
        1,
        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.

{
  "decision": "purchase_governed_reserved_capacity_plan",
  "failed_gates": [],
  "guardrails": [
    "Demand, reservation realization, spot interruption, on-demand limits, prices, credits, taxes, regions, currencies, and contract eligibility must be represented on one coherent service boundary.",
    "The optimizer never invents fallback capacity: unserved units remain visible and are priced with the submitted business value, while the coverage gate can still veto a cheap plan.",
    "Heuristic mode returns a deterministic supported plan without a global optimality certificate; material purchases require quote validation, sensitivity analysis, and FinOps, architecture, finance, security, legal, and procurement approval.",
    "A reservation recommendation is not authority to sign a contract, migrate workloads, reduce reliability capacity, attribute waste to a person, or infer vendor misconduct."
  ],
  "method": "stochastic_integer_cloud_reserved_capacity_plan_v1",
  "scenario_diagnostics": [
    {
      "on_demand_units": 0,
      "probability": 0.5,
      "scenario_id": "high",
      "spot_units": 0,
      "total_cost": 8,
      "unserved_units": 0,
      "unused_reserved_units": 0
    },
    {
      "on_demand_units": 0,
      "probability": 0.5,
      "scenario_id": "low",
      "spot_units": 0,
      "total_cost": 8,
      "unserved_units": 0,
      "unused_reserved_units": 10
    }
  ],
  "selected_reservation_offers": [
    {
      "capacity_units_per_contract": 10,
      "contract_count": 1,
      "offer_id": "annual-reservation",
      "start_period": 0,
      "term_periods": 2
    }
  ],
  "solver": {
    "evaluated_plan_count": 2,
    "global_optimality_certificate": true,
    "maximum_exact_states": 100000,
    "method": "exact_integer_enumeration",

How it works

Constrained optimization — Pick the best feasible option under real limits — budget, headcount, dependencies, capacity — rather than ranking a list and hoping it fits.

1. 1 Freeze reservation quotes with start, term, capacity, upfront/recurring price and purchase limit, plus coherent period demand, reservation realization, spot/on-demand price and fallback-capacity scenarios.
2. 2 Enumerate integer contract counts inside the state boundary or construct a deterministic forward-addition supported set; allocate realized reserved capacity first and then the cheapest genuinely available fallback without inventing supply.
3. 3 Price commitments, fallback, unused reservation, and unserved business loss; apply coverage and CVaR gates and compare the selected plan with the unreserved fallback portfolio.

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": "choose an integer portfolio of dated" }
  → finds "optimize_cloud_reserved_capacity"

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

gitrevio_capability_run
  { "capability_id": "optimize_cloud_reserved_capacity", "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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