Who this is for
FinOps teams, SaaS founders, engineering leaders, and anyone validating cloud unit economics before scaling.
User guide and technical reference
This document explains each FiceCal section, how the model derives outputs (including AI token economics and Release 4 SLA/SLO/SLI reliability economics), and how to operationalize the same logic via MCP for assistant-driven workflows and automations. Current UI inventory: 6 capability lanes, 4 scenario demos, and reliability overlays/outputs for resilience decisioning across pricing, investment, and growth decisions.
FinOps teams, SaaS founders, engineering leaders, and anyone validating cloud unit economics before scaling.
Calculator sections, formulas, guided workflows, 4 scenario demos, AI token economics, Release 4 reliability economics (10 inputs, 8 output cards, 2 chart overlays), health logic, share-state links, and MCP tools.
Start with Quick Start, then move through calculator usage, then MCP setup and examples.
| Section | What you provide | What the model computes |
|---|---|---|
| Group A - Core Inputs | Current clients (n), dev cost/month, infra cost/month, ARPU, and startup planning alternatives. | Normalizes baseline assumptions for all downstream calculations. |
| Group B - Optional Tuning | CUD discount %, target margin %, max chart clients, monthly budget, forecast growth/efficiency/drift, identified savings, realized savings, and cost avoidance. | Applies commitment savings, pricing constraints, budgeting variance checks, forecast scenarios, and value-realization ledger signals. |
| Group B - AI Token Economics | Enable AI mode, select pricing mode, enter token rates/volumes, retry/premium mix, shared overhead, and allocation policy. | Computes AI token cost, allocated AI monthly spend, and AI cost/client outputs for blended unit-economics decisions. |
| Group B - Reliability Economics (SLA/SLO/SLI) | Enable reliability mode, provide target/observed availability, incident profile, penalty assumptions, and reliability investment. | Computes expected downtime, reliability failure-cost lanes, reliability-adjusted cost, reliability-adjusted profit/loss, ARPU uplift needed, extra clients needed, risk band, and data confidence. |
| Group B - Multi-technology Overlay | Tech domain scope plus optional monthly SaaS/licensing/private cloud/data center/labor costs. | Computes scoped normalized technology cost, coverage, and confidence to align with multi-domain FinOps analysis. |
| Group C - Auto Outputs | No direct input; values are derived from Groups A/B. | Break-even clients, min price, contribution margin, CCER, CUD savings, startup targets, budget variance, forecast margin/confidence bands, value realization ratio/gap, normalization confidence outputs, AI token economics outputs, and 8 reliability economics outputs. |
| Group D - Provider Curves | Select cloud provider scenarios and visible lines (including reliability overlays when reliability data is active). | Compares scaling behavior across 9 curves: dev, infra raw, infra CUD, total cost, total + reliability, revenue, profit, profit + reliability, and revenue target. |
| Group E - Health + Recommendations | Derived from current assumptions. | Zone score plus prioritized FinOps actions (with category filters and provider context), including reliability-aware remediation guidance when applicable. |
The calculator uses deterministic formulas and scan-based thresholds to turn your inputs into decision-ready outputs for viability, pricing, growth planning, and reliability trade-off analysis.
Revenue(n) = ARPU * nTotalCost(n) = DevCost(n) + InfraCost(n)BreakEven = first n where Revenue(n) >= TotalCost(n) (deterministic scan over modeled range)ContributionMargin/client = ARPU - VCPUCCER = Revenue / ModeledInfraSpendNTC/client = sum(alpha_d * C_d) / n using selected domain scopeBudgetVariance = Budget - ModeledCost (headroom if positive)ExpectedReliabilityFailureCost = SLA Penalty + Incident Labor + Revenue-at-Risk + Churn RiskReliabilityAdjustedCost = ExistingModeledCost + ReliabilityInvestment + ExpectedReliabilityFailureCostReliabilityAdjustedProfit = Revenue - ReliabilityAdjustedCostRequiredARPU_with_rel = (ReliabilityAdjustedCost / n) * (1 + marginTarget)ExtraClients_with_rel = first n where CurrentARPU * n >= (BaseCost(n) + ReliabilityLoad) * (1 + marginTarget)none|low|medium|high) and report data-confidence quality.ForecastClients = n * (1 + growth%)BaselineMargin = ForecastRevenue - ForecastCostBestMargin = ForecastRevenue - (ForecastCost * (1 - efficiency%))WorstMargin = ForecastRevenue - (ForecastCost * (1 + drift%))ForecastSpread = BestMargin - WorstMarginTotalRealizedValue = RealizedSavings + CostAvoidanceRealizationRatio = TotalRealizedValue / IdentifiedSavingsResidualValueGap = IdentifiedSavings - TotalRealizedValueThis glossary is consolidated here to keep public documentation in one place and avoid cross-page fragmentation.
The projected monthly technology cost generated by the model for a given client volume. It combines the development decay component and infrastructure growth component for scenario analysis.
Forecast / CFOAverage revenue generated per client per month. In this calculator, ARPU is the core unit revenue assumption used to derive break-even, contribution margin, and CCER.
Unit economicsThe operating point where total monthly revenue equals total monthly cost. Above this threshold, the business becomes contribution-positive; below it, operations are loss-making.
ViabilityThe first client count in the modeled scan range where monthly revenue meets or exceeds monthly total cost. This is shown as the key threshold in KPI cards and chart insights.
Scale thresholdThe minimum viable per-client price required to recover modeled cost and target margin at a given scale. Used for pricing floor and startup planning outputs.
PricingPer-client share of variable infrastructure cost. It indicates how much incremental cloud cost each additional client introduces under current assumptions.
Cost structureARPU minus VCPU. It measures the amount each client contributes to fixed-cost recovery and profit after covering their own variable infrastructure load.
Unit marginRevenue divided by modeled on-demand cloud infrastructure cost in this calculator. A higher ratio means stronger revenue productivity per euro of modeled infra spend; low values indicate weak efficiency posture.
FinOps KPICommitment-based cloud discounts (e.g., 1-3 year commitments) that reduce unit infrastructure cost versus on-demand rates when workloads are predictable.
Optimization leverContractual reliability commitment with service-credit or penalty implications when delivered availability falls below agreed thresholds.
Reliability contractInternal reliability target used by teams to plan investment and operations before contractual SLA breaches occur.
Reliability targetMeasured reliability outcome (for example observed availability) used to evaluate objective compliance and expected cost impact.
Reliability signalExpected monthly loss lane that combines SLA penalties, incident labor, direct revenue-at-risk, and churn-risk expected value.
Risk-adjusted costCategorical risk posture (none, low, medium, high) derived from breach gap and failure-cost share relative to adjusted cost.
A commitment discount mechanism (primarily AWS) that lowers compute pricing for committed usage levels. Equivalent in intent to commitment models on other clouds.
Cloud pricingPre-purchased cloud capacity commitments that trade flexibility for lower unit cost. Used in optimization strategies alongside Savings Plans/CUDs.
Cloud pricingThe profit margin objective applied above modeled cost in pricing equations. It defines how much profit buffer you require beyond pure cost recovery.
Pricing policyMonthly revenue level required for the selected startup planning assumptions (target clients or target price) while meeting cost and margin objectives.
Startup planningDifference between technology budget and modeled/scoped monthly cost. Positive values indicate headroom; negative values indicate overrun risk.
CFO controlBase, best, and worst margin outcomes generated from growth plus efficiency/drift assumptions. This is a deterministic scenario band, not statistical probability.
Scenario planningDistance between best and worst forecast margins, often expressed versus revenue. Wider spread implies greater planning uncertainty and lower confidence.
Uncertainty signalTotal monthly savings opportunity discovered by analysis (rightsizing, commitments, waste reduction), before realization has occurred.
Value pipelineMonthly savings already captured in spend outcomes, not just identified. This is the achieved value component used in realization tracking.
Value deliveryFuture spend prevented through proactive design, governance, or procurement decisions. Unlike realized savings, this often represents avoided increases.
FinOps valuePercentage of identified value that has been achieved through realized savings plus cost avoidance. Tracks execution quality of FinOps initiatives.
Execution KPIRemaining difference between identified value target and achieved value. A positive gap indicates value still pending capture.
Execution KPIProcess of converting monthly costs into a comparable per-client basis across selected domains. Enables fair comparison of mixed technology cost structures.
ComparabilityAggregate selected-domain monthly cost divided by client count. This provides a unified per-client technology cost signal across cloud and non-cloud domains.
Portfolio metricBaseline mode where selected domains are included with neutral weighting for transparent reporting. Used to anchor governance discussions on actual scoped spend.
Governance modePolicy-weighted prioritization lens used to emphasize selected dimensions when governance requires scenario emphasis over neutral baseline representation.
Governance modeShare of selected technology domains that currently have cost data provided. Higher coverage improves confidence in normalization outputs.
Data qualityConfidence level for scoped normalization quality (e.g., Low/Medium/High), based on data completeness and domain coverage.
Data qualityThe current client baseline used for calibrating model coefficients from your present-day cost and usage conditions.
Model calibrationMaximum client count shown in chart/scanning ranges. It controls projection horizon for break-even search and curve visualization range.
Projection horizonThe planning panel that summarizes budget checkpoints, margin scenarios, and realization trajectories for month-by-month finance review.
Finance reportingThe Health section continuously evaluates your model posture and labels it as a zone with an associated score and guidance.
All, Infrastructure, Pricing, Marketing, CRM, Governance) let users focus on a specific execution lane.The same FiceCal model is available through a Model Context Protocol server so AI assistants can call the model directly in workflows, audits, and planning automations.
finops.calculate supports multi-technology scope through techDomains and optional non-cloud monthly cost inputs, plus reliability inputs/outputs for SLA/SLO/SLI parity.uiIntent, uiMode) in generated state tokens.finops.recommend supports category-aware filtering and can include strategic pricing/marketing/CRM recommendations when inputs are supplied.| Tool | Purpose | Typical output |
|---|---|---|
finops.calculate |
Full model execution with normalized inputs and optional UI context. | Calculated outputs (including normalization and reliability snapshots), health, recommendations, and optional state token that can preserve ui/um context. |
finops.health |
Posture-only analysis for the same assumptions. | Zone, score, and failed checks. |
finops.recommend |
Action planning output. | Prioritized recommendations filtered by zone/provider/category, with optional strategic business recommendations when inputs are supplied. |
finops.state.encode |
Create share-state tokens from assumptions plus optional role/mode context. | Encoded token for URL embedding with normalized ui, um, inputs, domains, providers, and hidden curves. |
finops.state.decode |
Decode and validate share-state tokens. | Restored assumptions payload. |
finops-calculator or finops-calculator-mcp.index.html.# Example command path for MCP client config
node /absolute/path/to/mcp/server/index.js
Use the MCP connection examples from the finops-calculator-mcp repository (for example, github.com/duksh/finops-calculator-mcp) for Cursor, Windsurf, and Claude Desktop integration.
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "finops.calculate",
"arguments": {
"inputs": {
"nRef": 120,
"devPerClient": 500,
"infraTotal": 2400,
"techDomains": ["cloud", "saas"],
"costSaaS": 600,
"reliabilityEnabled": "on",
"sloTargetAvailabilityPct": 99.9,
"sliObservedAvailabilityPct": 99.7,
"incidentCountMonthly": 3,
"mttrHours": 1.2,
"incidentBlendedHourlyRate": 100,
"criticalRevenuePerMinute": 25,
"arrExposedMonthly": 70000,
"slaPenaltyRatePerBreachPointMonthly": 3500,
"reliabilityInvestmentMonthly": 1500,
"startupTargetPrice": 35,
"cudPct": 30,
"margin": 15,
"nMax": 2000
},
"providers": ["aws"],
"uiIntent": "operations",
"uiMode": "operator",
"options": {
"includeHealth": true,
"includeRecommendations": true,
"includeStateToken": true
}
}
}
}
{
"jsonrpc": "2.0",
"id": 3,
"method": "tools/call",
"params": {
"name": "finops.state.decode",
"arguments": {
"stateToken": "..."
}
}
}
{
"jsonrpc": "2.0",
"id": 3,
"result": {
"state": {
"v": 1,
"ui": "operations",
"um": "operator",
"i": { "infraTotal": "2400", "ARPU": "30" },
"td": ["cloud", "saas"],
"p": ["aws"],
"h": []
}
}
}
{
"jsonrpc": "2.0",
"id": 1,
"result": {
"outputs": {
"breakEvenClients": 51,
"minPricePerClient": 27.79,
"reliability": {
"expectedReliabilityFailureCostMonthly": 10887.5,
"reliabilityAdjustedCostMonthly": 15487.5,
"reliabilityRiskBand": "medium",
"reliabilityDataConfidence": "high"
},
"normalization": {
"selectedDomains": ["cloud", "saas"],
"coveragePct": 100,
"normalizedTechCostPerClient": 25,
"confidence": "High"
}
},
"health": {
"zoneKey": "yellow",
"zoneTitle": "Yellow Zone - Needs Improvement",
"score": 72
},
"recommendations": [
{
"title": "CCER below 3x",
"category": "governance",
"priority": "high"
}
],
"stateToken": "..."
}
}
{
"jsonrpc": "2.0",
"id": 2,
"method": "tools/call",
"params": {
"name": "finops.recommend",
"arguments": {
"zoneKey": "red",
"providers": ["aws"],
"category": "marketing",
"inputs": {
"nRef": 80,
"infraTotal": 2400,
"startupTargetPrice": 35
}
}
}
}
?state= is copied.ui/um are captured.finops-calculator-mcp repository.reliabilityEnabled to on and provide at least SLO/SLI baseline assumptions.npm run test:parity in finops-calculator-mcp/server and inspect drift in share-state version or UI option constants.