FiceCal

Short for FinOps and Cloud Economics Calculator, FiceCal assist you in modeling break-even, pricing floor, cloud efficiency, and optimization actions.

This project is built as a landing-page style tool that guides users through:

1. What the tool does and who it is for
2. Author credibility and context
3. A direct call-to-action into the calculator
4. Live calculator inputs/outputs and chart analysis

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What this tool helps you answer

• How many clients are needed to break even?
• What is the minimum viable price per client at current scale?
• Is cloud spend efficient relative to revenue (CCER)?
• How much can committed use discounts improve cloud economics?
• Which FinOps actions should be prioritized for the current health zone?

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Key features

• Static-first implementation centered on index.html with runtime modules under src/
• Dedicated glossary page (glossary.html) with anchored definitions for formula/KPI terminology
• D3.js interactive chart (show/hide curve controls, hover readouts, zones)
• Real-time recalculation as users type
• Automatic in-page term linking from key calculator/formula sections to glossary anchors
• Modular runtime contracts with feature catalog + manifests (src/features/feature-catalog.json, src/features/*/feature.json)
• Runtime module loading for core economics, AI token economics, and feature gates
• Modularization guardrail CI workflow (.github/workflows/modularization-guardrails.yml)
• Model Assurance section documenting model hardening decisions and validation checks
• Recommendation category filters (All / Infrastructure / Pricing / Marketing / CRM / Governance)
• Output-card "How to address this?" guidance panel with indicator-specific levers and prerequisites
• Quick scenario actions in calculator header (Demo healthy / Demo unhealthy)
• Structured calculator groups:
  • Group A: Business snapshot inputs
  • Group B: Model tuning inputs
  • Group C: Auto-calculated KPIs
  • Group D: Cloud provider selection (with provider badges)
  • Group E: Health zone scoring and recommendation engine
• Formula cards with explanatory tooltips
• Academic and practitioner references section
• Landing-page navigation links to jump to Calculator, Health, Chart, Formulas, and Glossary
• Mobile-friendly responsive behavior

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Current release snapshot

Current release line consolidates product and model-quality improvements delivered across Release 2 and Wave 3:

• Product naming standardization to FiceCal
• Multi-technology glossary and automatic contextual linking across core sections
• Model Assurance section for CFO/FinOps validation workflow
• Health recommendation filtering by operating domain
• Output indicator action guidance panel ("How to address this?")
• Deterministic economic scanning improvements aligned with MCP parity validations
• Public GitHub Pages runtime compatibility updates (module scripts + feature manifests mirrored to duksh.github.io/src/)
• Added docs/model-risk-and-validation.md and Read the Docs build config (.readthedocs.yaml)

Wave 3 foundation updates:

• Added docs/ai-token-economics.md (AI token economics proposal)
• Added docs/modularization-playbook.md (modular architecture + GitOps approach)
• Added docs/roadmap/wave-3-modularization-backlog.md (execution backlog)
• Added docs/roadmap/wave-3-execution-plan.md (current execution plan + Intent-based UI/UX rehauling blueprint)
• Added docs/releases/release-note-template.md (module-aware release note template)
• Added scripts/validate-feature-catalog.py and scripts/sync-public-pages.sh
• Added scripts/validate-doc-links.py (canonical docs-link CI guardrail)

Release 3 closeout and next-phase setup:

• Added Wave 3 closeout acceptance checklist (docs/roadmap/wave-3-acceptance-checklist.md)
• Added Wave 3 runtime regression scripts (scripts/test-wave3-intent-share-state.js, scripts/test-wave3-ui-contracts.js)
• Added browser smoke automation scaffold (scripts/test-wave3-playwright-smoke.js + .github/workflows/wave3-playwright-smoke.yml)
• Added Wave 4 execution planning document (docs/roadmap/wave-4-execution-plan.md)

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Core model concepts

The calculator uses a practical cloud economics model based on:

• Development cost decay with scale
• Super-linear infrastructure growth
• Committed use discount effect
• Break-even thresholding
• Contribution margin and CCER health scoring
• Minimum price floor based on target margin

Representative equations include:

• DC(n) = K * n^-a
• IC(n) = c * n^b
• IC_cud(n) = g * c * n^(b*0.96)
• MP(n) = TC(n) * (1 + m)

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Research foundation for DBA thesis citations

To reinforce the research-backed positioning of recent FiceCal releases (reliability economics, forecast confidence outputs, and cross-cloud optimization guidance), use the references below as the core bibliography set.

Feature-to-reference map

• Cloud economics and scale modeling (power-law style cost behavior, break-even framing)

  • Harms & Yamartino (2010) - The Economics of the Cloud
  • Bayrak, Conley & Wilkie (2011) - The Economics of Cloud Computing
  • Bourreau, de Streel & Graef (2024) - The Economics of the Cloud (TSE)

• FinOps operating model and KPI governance (CCER, optimization playbooks)

  • Bryant (2022) - peer-reviewed FinOps framing in ITNOW
  • FinOps Foundation (2026) - State of FinOps Report
  • Nawrocki & Smendowski (2024) - FinOps optimization study in Journal of Computational Science

• Committed use optimization and multi-cloud comparability

  • Google Cloud CUD documentation
  • GSA/ITVMO cross-cloud discount benchmarking
  • AWS Well-Architected Cost Optimization Pillar
  • Azure Well-Architected Cost Optimization guidance

• Reliability economics (Release 4) (SLA/SLO/SLI cost-of-failure, investment trade-offs)

  • Beyer et al. (2016) - Site Reliability Engineering
  • Jones et al. (2018) - The Site Reliability Workbook

• Forecast bands and confidence interpretation

  • Hyndman & Athanasopoulos (2021) - Forecasting: Principles and Practice
  • Makridakis, Spiliotis & Assimakopoulos (2020) - M4 competition paper
  • JCGM (2008) - Guide to the Expression of Uncertainty in Measurement

• Cost allocation and composite scoring

  • Kaplan & Anderson (2004) - Time-Driven Activity-Based Costing
  • Nardo et al. (2008) - OECD/JRC Composite Indicators Handbook

Practical note for thesis writing

For formal dissertation chapters, pair each practitioner/industry source (FinOps Foundation, cloud vendor docs, Well-Architected guidance) with at least one peer-reviewed source in the same paragraph. This improves methodological defensibility while preserving real-world relevance.

All references are also maintained in the in-app Academic & Primary References section at index.html#references-section.

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Project structure

• index.html - main application page (UI shell, calculator orchestration, chart rendering)
• document.html - reference and documentation companion page
• glossary.html - glossary of complex terms used across calculator outputs and formulas
• perfect-finops-calculator-prompt.md - implementation requirements and specification
• docs/model-risk-and-validation.md - model risk log, remediation history, and validation checklist
• docs/ai-token-economics.md - Wave 3 AI token economics scope, formulas, and MVP criteria
• docs/modularization-playbook.md - modularization strategy and GitOps operating model
• docs/roadmap/wave-3-execution-plan.md - current Wave 3 status and intent-based UI/UX next phases
• docs/roadmap/wave-4-execution-plan.md - Wave 4 post-release execution plan (E2E hardening + runtime extraction)
• docs/releases/release-note-template.md - standard template with module change metadata and mirror verification checklist
• src/core/economics.js - shared core economics runtime module
• src/features/runtime-gates.js - feature activation/runtime gate module
• src/features/ai-token-economics/engine.js - AI token economics runtime engine
• src/features/feature-catalog.json - feature registry for module lifecycle management
• src/features/*/feature.json - module contracts (core economics, normalization, AI token economics)
• scripts/validate-feature-catalog.py - manifest and catalog validation guardrail
• scripts/validate-doc-links.py - canonical docs-link validator for markdown docs
• scripts/sync-public-pages.sh - repeatable source -> public mirror sync helper
• scripts/test-wave3-playwright-smoke.js - browser-level smoke checks for intent flows (Playwright)

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Run locally

No build step is required.

Option 1: Open directly

Open index.html in your browser.

Option 2: Serve with a local static server

python3 -m http.server 8080

Then open http://localhost:8080.

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Deploy

FiceCal is static-host friendly (GitHub Pages, Netlify, Vercel, or any static web server), but current runtime requires both top-level pages and src/ assets.

Public mirror (source repo -> duksh.github.io)

Preferred release command (sync + commit + push public mirror):

./scripts/release-sync-public.sh

Guard check mode (fails if public mirror is out of sync):

./scripts/release-sync-public.sh --check

Configuration:

• Set either PUB (in scripts/sync-public-pages.local.sh) or DUKSH_GITHUB_IO_PATH (in scripts/check-repo-mapping.local.sh) to your local duksh.github.io checkout path.

Manual fallback (if needed):

1. Sync primary pages:

./scripts/sync-public-pages.sh

2. Sync runtime assets required by index.html:

PUB_REPO="/absolute/path/to/duksh.github.io"
rsync -a src/ "$PUB_REPO/src/"

3. Commit/push in public mirror repo:

git -C "$PUB_REPO" add index.html document.html glossary.html src/
git -C "$PUB_REPO" commit -m "sync public pages and runtime assets"
git -C "$PUB_REPO" push

4. Verify live site (hard refresh):

• https://duksh.github.io/#calculator-section
• Ensure /src/features/feature-catalog.json and /src/features/*/feature.json return HTTP 200

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Usage flow

1. Read the landing intro and click Get Started
2. Fill Group A with known business metrics
3. Optionally tune Group B assumptions
4. Review Group C KPI outputs
5. Select cloud providers in Group D
6. Use Group E health score and recommendations
7. Explore the chart section for curve behavior and zone transitions
8. Review formulas for model interpretation

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Author

Duksh Koonjoobeeharry

• FinOps and AWS/GCP cloud solution developer
• DBA researcher
• LinkedIn: https://www.linkedin.com/in/duksh/

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Notes

• This calculator is intended for decision support and scenario modeling.
• Inputs and outputs should be validated against your actual billing, unit economics, and pricing data before operational decisions.

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License

Apache-2.0. See LICENSE.