I built an open-source ERP implementation cost calculator (vanilla JS, MIT)

ERP budgets are notoriously hard to estimate. Ask three vendors "how much does a Microsoft Dynamics 365 implementation cost?" and you'll get three wildly different numbers — because the real figure depends on user count, modules, integrations, deployment model, and industry compliance load.

So I built a small, dependency-free calculator that turns those inputs into a defensible first-year budget range. It's vanilla HTML/CSS/JS, runs entirely in the browser, and is MIT-licensed — fork it, embed it, rip the model out.

• 🔗 Live demo (Hugging Face Space): https://huggingface.co/spaces/trangotechd365/erp-implementation-cost-calculator
• 📦 Full source: https://huggingface.co/spaces/trangotechd365/erp-implementation-cost-calculator/tree/main

What it does

Seven inputs — industry, company size, full/light users, modules, integrations, deployment, timeline — produce a lean-to-complex range plus a component breakdown across the four cost drivers that actually move an ERP budget:

No build step, no framework, no network calls. One index.html.

The interesting part: a transparent cost model

Instead of a black box, the estimate is a readable formula calibrated on published Microsoft pricing and benchmark ranges. Here's the core:

function calc() {
  const full  = Math.max(0, +fullInput.value || 0);
  const light = Math.max(0, +lightInput.value || 0);
  const users = Math.max(1, full + light);
  const isBC  = users <= 300; // Business Central vs F&O/SCM

  // 1) Licensing (annual) — BC vs F&O per-user rates
  const rateFull  = isBC ? 100 : 210; // $/user/mo
  const rateLight = isBC ? 8   : 30;
  const licensing = (full * rateFull + light * rateLight) * 12;

  // 2) Implementation (one-time) — scales with modules + users
  let base = isBC ? 45000 : 220000;
  if (users > 2000) base += 120000;
  const moduleFactor = 1 + 0.12 * (numModules - 1);
  const userFactor   = 1 + users / (isBC ? 260 : 900);
  const implementation = base * moduleFactor * userFactor;

  // 3) Integrations — per-connection cost
  const integrations = numIntegrations * (isBC ? 22000 : 48000);

  // 4) Training & change management (~10% of subtotal)
  const subtotal = licensing + implementation + integrations;
  const training = subtotal * 0.10;

  let tco = subtotal + training;

  // Real-world modifiers
  tco *= industryMult;   // regulated sectors +20–40%
  tco *= deployMult;     // hybrid / on-prem premium
  tco *= timelineMult;   // rush (<3 mo) +15–30%
  if (multiEntity) tco *= 1.12;

  // Lean-to-complex range, clamped to size-band benchmarks
  const [lo, hi] = band(users);
  const low  = Math.max(lo * 0.75, tco * 0.82);
  const high = Math.min(hi * 1.25, tco * 1.40);

  render(low, high);
}

A couple of design decisions worth calling out:

• Clamping to benchmark bands. A naive component sum can spit out absurd numbers for edge inputs. Each result is clamped to a published size-band range (e.g. small business $60K–$250K, mid-market $400K–$1.5M) so the output stays credible.
• Modifiers as multipliers, not hardcoded tiers. Industry compliance, rush timelines, and multi-entity scope are the things that quietly blow up ERP budgets, so they're explicit multipliers you can audit and tweak.
• Zero dependencies. The whole thing is one file. That's deliberate — it drops straight into a static host (I used a Hugging Face Static Space) or an <iframe> with no toolchain.

Why vanilla?

For a single-purpose embeddable widget, a framework is pure overhead. No hydration, no bundle, instant first paint, and anyone can read the source and trust the math. The entire app — markup, styles, and logic — is ~18KB uncompressed.

Grab it

• Live: https://huggingface.co/spaces/trangotechd365/erp-implementation-cost-calculator
• Source (MIT): https://huggingface.co/spaces/trangotechd365/erp-implementation-cost-calculator/tree/main

It's maintained by Trango Tech, a Microsoft Dynamics 365 partner — the model draws on real Business Central implementation project data. PRs and forks welcome. If you've built cost estimators for other platforms, I'd love to compare modeling approaches in the comments.