BMAD Method

BMAD Method

Business Model and Architecture Design methodology for aligning technical architecture with business model sustainability and scalability

7estrelas
5forks
Atualizado 1/21/2026
SKILL.md
readonlyread-only
name
BMAD Method
description

Business Model and Architecture Design methodology for aligning technical architecture with business model sustainability and scalability

version
1.0.0

BMAD Method (Business Model and Architecture Design)

Overview

The BMAD Method bridges business strategy and technical architecture. It ensures your technical decisions support long-term business sustainability, not just immediate feature delivery.

Core Insight: Your architecture IS your business model in code form.

When to Use This Skill

Use BMAD when:

  • Starting a new product or major architectural redesign
  • Technical decisions have direct revenue/cost implications
  • Scaling challenges intersect with business model constraints
  • Evaluating build vs. buy for core capabilities
  • Transitioning from MVP to sustainable growth
  • Business model changes require architectural shifts

Key Capabilities

  • Map business model to technical architecture decisions
  • Identify architectural implications of revenue models
  • Design for cost sustainability at scale
  • Align technology investments with business value
  • Evaluate infrastructure costs vs. revenue potential
  • Plan architecture evolution alongside business growth

Workflow

Step 1: Business Model Analysis

Understand the Revenue Engine:

  • How does money flow? (One-time, subscription, usage-based, marketplace, advertising)
  • What's the unit economics? (CAC, LTV, gross margin, payback period)
  • What are the scale expectations? (10 users? 10k? 10M?)
  • What's the competitive moat? (Network effects, data, tech, brand)

Key Questions:

  • Is this B2B or B2C?
  • What's the pricing model?
  • What drives costs? (Infrastructure, support, sales, dev)
  • What's the target gross margin?

Step 2: Architecture Alignment

Map Business Model to Architecture:

Subscription SaaS (B2B):

  • Multi-tenancy architecture
  • Pay-as-you-grow infrastructure (starts cheap)
  • Enterprise features (SSO, RBAC, audit logs)
  • 99.9%+ uptime SLA requirements
  • Data isolation and security compliance

Usage-Based (API/Platform):

  • Serverless/metered infrastructure
  • Rate limiting and quota management
  • Detailed usage tracking and billing
  • Developer experience (docs, SDKs)
  • Predictable per-request costs

Marketplace/Network:

  • Support dual-sided interactions (buyers/sellers)
  • Transaction processing and escrow
  • Search, matching, and discovery algorithms
  • Trust and safety systems
  • Commission-based cost structure

Freemium/Consumer:

  • Scales to millions of users efficiently
  • Clear free vs. paid feature boundaries
  • Low marginal cost per user
  • Conversion funnel optimization
  • Viral/growth mechanics

Step 3: Cost Modeling

Infrastructure Cost Analysis:

Calculate Unit Economics:

  • Cost per user/month
  • Cost per transaction
  • Cost per API call
  • Infrastructure overhead vs. variable costs

Example (SaaS):

Target: $20/user/month subscription

Acceptable costs:
- Infrastructure: <$2/user/month (10% COGS)
- Support: <$4/user/month (20%)
- Sales/Marketing: <$60 CAC (3-month payback)

Architecture decisions:
- Shared infrastructure (not dedicated per customer)
- Self-service onboarding (reduce sales cost)
- In-app support tools (reduce support tickets)
- Efficient database design (reduce storage costs)

Step 4: Scalability Planning

Design for Growth Stages:

Stage 1: MVP (0-100 users)

  • Goal: Validate product-market fit
  • Architecture: Simple, monolithic, managed services
  • Cost: Fixed low monthly ($100-500/month)
  • Trade-off: Speed over scalability

Stage 2: Growth (100-10k users)

  • Goal: Prove unit economics work
  • Architecture: Modular monolith, scale vertically first
  • Cost: Linear with users ($0.50-5/user/month)
  • Trade-off: Optimize for margin over features

Stage 3: Scale (10k-1M users)

  • Goal: Efficient scaling without rewrites
  • Architecture: Microservices for bottlenecks, caching, CDN
  • Cost: Sublinear growth ($0.10-1/user/month)
  • Trade-off: Operational complexity vs. efficiency

Stage 4: Enterprise (1M+ users)

  • Goal: Dominant market position
  • Architecture: Multi-region, custom infra, dedicated teams
  • Cost: Economies of scale (<$0.10/user/month)
  • Trade-off: Long-term investment over short-term agility

Step 5: Build vs. Buy Framework

Evaluate Core vs. Context:

Build when:

  • It's your competitive differentiator
  • You need specific customization
  • Recurring costs exceed build cost
  • You have expertise in-house
  • Control/security is critical

Buy/Use SaaS when:

  • It's commodity functionality
  • Time-to-market is critical
  • You lack expertise
  • Maintenance burden is high
  • Cost predictability matters

Examples:

Capability Decision Rationale
Payment processing Buy (Stripe) Commodity, compliance heavy
Core algorithm Build Competitive moat
Email delivery Buy (SendGrid) Commodity infrastructure
Analytics Buy (Mixpanel) Faster than building
Custom AI model Build Unique to your data
Auth infrastructure Buy (Auth0) initially Build later at scale

Step 6: Business Constraints Documentation

Capture Non-Negotiable Requirements:

Regulatory/Compliance:

  • GDPR, HIPAA, SOC2, PCI-DSS
  • Data residency requirements
  • Audit trail and retention policies

Business Commitments:

  • SLA commitments (uptime, response time)
  • Data portability guarantees
  • Security certifications required
  • Integration promises to customers

Financial Constraints:

  • Burn rate and runway
  • Target gross margin
  • Pricing commitments made
  • Investor expectations

Examples

Example 1: B2B SaaS Analytics Platform

Business Model:

  • $99-$499/month subscription
  • Target: 1,000 customers = $1.5M ARR
  • Target gross margin: 80%
  • Max COGS: $3/customer/month

Architecture Decisions:

  • Multi-tenant database (shared PostgreSQL)
  • Serverless data processing (AWS Lambda)
  • Managed infrastructure (AWS RDS, S3, CloudFront)
  • No dedicated resources per customer (kills margin)

Build vs. Buy:

  • Build: Core analytics engine (differentiator)
  • Buy: Auth (Auth0), Email (SendGrid), Support (Intercom)

Outcome: $2.50/customer/month COGS, 83% margin


Example 2: Usage-Based API Platform

Business Model:

  • $0.01/API call pricing
  • Target: 10M calls/month = $100k MRR
  • Target gross margin: 70%
  • Max COGS: $0.003/call

Architecture Decisions:

  • Serverless architecture (AWS Lambda + API Gateway)
  • Pay-per-use infrastructure (no idle costs)
  • Aggressive caching (CloudFlare + Redis)
  • Efficient algorithms (cost per call matters)

Build vs. Buy:

  • Build: Core API logic (differentiator)
  • Buy: API gateway (AWS), CDN (CloudFlare), Monitoring (Datadog)

Outcome: $0.0025/call COGS, 75% margin


Example 3: Consumer Marketplace

Business Model:

  • 10% commission on transactions
  • Target: $1M GMV/month = $100k revenue
  • Target gross margin: 60%
  • Max COGS: $40k/month

Architecture Decisions:

  • Scalable to millions of users (serverless + CDN)
  • Transaction processing (Stripe Connect)
  • Search and matching (Algolia or Elasticsearch)
  • Low marginal cost per user (<$0.01/user/month)

Build vs. Buy:

  • Build: Matching algorithm (differentiator)
  • Buy: Payments (Stripe), Search (Algolia), Chat (Stream)

Outcome: Scales to 100k users at <$35k/month


Best Practices

1. Start with Business Model, Not Tech Stack

Don't choose React/Node/AWS first. Choose after understanding:

  • Revenue model
  • User scale
  • Unit economics
  • Margin targets

2. Design for Current Stage +1

Build for where you are now, but don't lock yourself out of next stage.

Bad: Hard-coded single-tenant that can't scale
Good: Multi-tenant from day 1 (even at 10 users)

3. Measure Infrastructure Cost Per User

If you can't calculate cost per user, you can't predict profitability.

Track monthly:

  • AWS/GCP/Azure spend
  • Third-party SaaS costs
  • Divide by active users

4. Align Architectural Investments with Revenue

If feature doesn't drive revenue/retention, defer expensive architecture.

Example: Don't build multi-region before proving PMF.

5. Plan for Architectural Pivot Points

Know when you'll need to refactor:

  • 1,000 users → Optimize database queries
  • 10,000 users → Add caching layer
  • 100,000 users → Microservices for bottlenecks
  • 1M users → Multi-region, custom infra

Common Pitfalls

1. Over-Engineering for Scale You Don't Have

Building for 1M users when you have 100 wastes time and money.

Antipattern: Microservices + Kubernetes at MVP stage
Better: Monolith on Railway/Heroku, scale later

2. Under-Engineering for Business Model

Not building multi-tenancy in B2B SaaS kills margins at scale.

Antipattern: Dedicated database per customer
Better: Multi-tenant architecture from day 1

3. Ignoring Unit Economics

Not tracking cost per user means surprises at scale.

Antipattern: "We'll figure out costs later"
Better: Model costs before building

4. Building Everything In-House

Commodities don't need custom solutions.

Antipattern: Build custom auth, payments, email
Better: Buy Stripe, Auth0, SendGrid; build differentiation

5. Misaligned Tech Investments

Spending on features that don't drive business value.

Antipattern: Perfect CI/CD before proving PMF
Better: Ship fast, optimize later


Related Skills

  • mvp-builder - Rapid MVP development (BMAD guides what to build)
  • product-strategist - Product-market fit validation (BMAD aligns architecture)
  • deployment-advisor - Infrastructure and CI/CD (BMAD sets cost targets)
  • api-designer - API design (BMAD determines pricing model)
  • performance-optimizer - Optimize costs at scale (BMAD identifies when to optimize)

Deliverables

When using BMAD Method, produce:

  1. Business Model Canvas

    • Revenue streams, cost structure, value proposition
    • Unit economics (CAC, LTV, margin)
  2. Architecture Alignment Document

    • How architecture supports business model
    • Cost per user calculations
    • Scalability plan for growth stages
  3. Build vs. Buy Decision Matrix

    • Core capabilities to build
    • Context capabilities to buy
    • Cost-benefit analysis
  4. Architectural Roadmap

    • Current stage architecture
    • Planned refactors at scale milestones
    • Investment priorities

Success Metrics

You've successfully applied BMAD when:

  • Infrastructure costs are predictable and within target margins
  • Architecture supports current stage without over-engineering
  • Clear plan exists for next scale milestone
  • Build vs. buy decisions are justified by business value
  • Technical debt is strategic, not accidental
  • Team understands business implications of technical choices

Remember: The best architecture is the one that makes your business model sustainable and profitable.

You Might Also Like

Related Skills

coding-agent

coding-agent

179Kdev-codegen

Run Codex CLI, Claude Code, OpenCode, or Pi Coding Agent via background process for programmatic control.

openclaw avataropenclaw
Obter
add-uint-support

add-uint-support

97Kdev-codegen

Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.

pytorch avatarpytorch
Obter
at-dispatch-v2

at-dispatch-v2

97Kdev-codegen

Convert PyTorch AT_DISPATCH macros to AT_DISPATCH_V2 format in ATen C++ code. Use when porting AT_DISPATCH_ALL_TYPES_AND*, AT_DISPATCH_FLOATING_TYPES*, or other dispatch macros to the new v2 API. For ATen kernel files, CUDA kernels, and native operator implementations.

pytorch avatarpytorch
Obter
skill-writer

skill-writer

97Kdev-codegen

Guide users through creating Agent Skills for Claude Code. Use when the user wants to create, write, author, or design a new Skill, or needs help with SKILL.md files, frontmatter, or skill structure.

pytorch avatarpytorch
Obter

Implements JavaScript classes in C++ using JavaScriptCore. Use when creating new JS classes with C++ bindings, prototypes, or constructors.

oven-sh avataroven-sh
Obter

Creates JavaScript classes using Bun's Zig bindings generator (.classes.ts). Use when implementing new JS APIs in Zig with JSC integration.

oven-sh avataroven-sh
Obter