Detect common technical and organizational anti-patterns in proposals, architectures, and plans. Use when strategic-cto-mentor needs to identify red flags before they become problems.
日本語の概要は準備中です。原文の説明を表示しています。
Infrastructure and development cost estimation for technical projects. Use when planning budgets, evaluating build vs buy decisions, or projecting TCO for architecture choices.
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Provides frameworks for estimating infrastructure costs, development effort, and total cost of ownership (TCO) for technical projects.
TCO = Infrastructure + Development + Operations + Opportunity Cost
┌─────────────────────────────────────────────────────────────────┐
│ TOTAL COST OF OWNERSHIP │
├─────────────────────────────────────────────────────────────────┤
│ │
│ Infrastructure Development Operations Opportunity │
│ ──────────────── ──────────── ────────── ──────────── │
│ • Compute • Engineering • Support • What else │
│ • Storage • QA • Monitoring • could team │
│ • Network • DevOps • On-call • be building? │
│ • Third-party • Management • Training │
│ APIs/SaaS • Contractors • Incidents │
│ │
└─────────────────────────────────────────────────────────────────┘
| Instance | vCPU | RAM | Monthly Cost | Best For |
|---|---|---|---|---|
| t3.micro | 2 | 1GB | $8 | Dev/test |
| t3.medium | 2 | 4GB | $30 | Small apps |
| t3.large | 2 | 8GB | $60 | Light production |
| m6i.large | 2 | 8GB | $70 | General production |
| m6i.xlarge | 4 | 16GB | $140 | Medium workloads |
| m6i.2xlarge | 8 | 32GB | $280 | Heavy workloads |
| c6i.2xlarge | 8 | 16GB | $250 | CPU-intensive |
| r6i.2xlarge | 8 | 64GB | $370 | Memory-intensive |
| Instance | GPU | VRAM | Monthly Cost | Best For |
|---|---|---|---|---|
| g4dn.xlarge | T4 | 16GB | $380 | Inference |
| g5.xlarge | A10G | 24GB | $730 | ML training/inference |
| p4d.24xlarge | 8x A100 | 320GB | $23,000 | Large model training |
| Plan | Savings | Commitment |
|---|---|---|
| On-Demand | 0% | None |
| Reserved (1yr) | 30-40% | 1 year |
| Reserved (3yr) | 50-60% | 3 years |
| Spot Instances | 60-90% | Can be interrupted |
| Instance | vCPU | RAM | Monthly Cost | Connections |
|---|---|---|---|---|
| db.t3.micro | 2 | 1GB | $15 | 50 |
| db.t3.medium | 2 | 4GB | $50 | 100 |
| db.m6g.large | 2 | 8GB | $120 | 200 |
| db.m6g.xlarge | 4 | 16GB | $240 | 400 |
| db.r6g.xlarge | 4 | 32GB | $350 | 500 |
| db.r6g.2xlarge | 8 | 64GB | $700 | 1000 |
Add for storage: $0.115/GB/month (gp3) Add for IOPS: $0.02/IOPS/month (over 3000 baseline)
| Node Type | RAM | Monthly Cost |
|---|---|---|
| cache.t3.micro | 0.5GB | $12 |
| cache.t3.medium | 3GB | $50 |
| cache.m6g.large | 6.4GB | $100 |
| cache.r6g.large | 13GB | $175 |
| Service | Cost | Use Case |
|---|---|---|
| S3 Standard | $0.023/GB | Frequently accessed |
| S3 Infrequent | $0.0125/GB | Backups, archives |
| S3 Glacier | $0.004/GB | Long-term archive |
| EBS gp3 | $0.08/GB | Block storage |
| EBS io2 | $0.125/GB + IOPS | High performance |
| Traffic Type | Cost |
|---|---|
| Data IN | Free |
| Data OUT (first 10TB) | $0.09/GB |
| Data OUT (next 40TB) | $0.085/GB |
| Inter-AZ transfer | $0.01/GB each way |
| Inter-region transfer | $0.02/GB |
| CloudFront to internet | $0.085/GB |
Development Cost = (Hours × Hourly Rate) × Complexity Factor × Risk Buffer
Hourly Rate (Fully Loaded):
- Junior Engineer: $75-100/hr
- Mid-level Engineer: $100-150/hr
- Senior Engineer: $150-200/hr
- Staff/Principal: $200-300/hr
Complexity Factors:
- Greenfield, known tech: 1.0x
- Existing codebase, known tech: 1.2x
- New technology for team: 1.5x
- Complex integrations: 1.3x
- Regulatory/compliance: 1.4x
Risk Buffer:
- Well-defined requirements: 1.2x
- Ambiguous requirements: 1.5x
- Experimental/R&D: 2.0x
| Size | Story Points | Hours | Cost (Mid-level) |
|---|---|---|---|
| XS | 1 | 2-4 | $200-400 |
| S | 2 | 4-8 | $400-800 |
| M | 3 | 8-16 | $800-1,600 |
| L | 5 | 16-32 | $1,600-3,200 |
| XL | 8 | 32-64 | $3,200-6,400 |
| XXL | 13+ | 64+ | $6,400+ |
## Monthly Team Cost
Engineering Team:
- 2 Senior Engineers × $15,000 = $30,000
- 3 Mid-level Engineers × $10,000 = $30,000
- 1 Engineering Manager × $18,000 = $18,000
Overhead (benefits, tools, etc.): 30%
Monthly Burn: ($78,000) × 1.3 = $101,400
Annual Team Cost: ~$1.2M
Build vs Buy Decision Matrix:
LOW Differentiation HIGH Differentiation
┌────────────────────┬────────────────────┐
HIGH Volume/ │ │ │
Usage │ Consider │ BUILD │
│ Build │ (competitive │
│ (cost savings) │ advantage) │
├────────────────────┼────────────────────┤
LOW Volume/ │ │ │
Usage │ BUY │ BUY │
│ (no question) │ (then consider │
│ │ build if scales) │
└────────────────────┴────────────────────┘
## Option A: Build Custom Solution
### Initial Development
- Engineering time: X months × $Y/month = $Z
- Infrastructure setup: $A
### Ongoing Costs (Annual)
- Infrastructure: $B
- Maintenance (20% of dev time): $C
- On-call/support: $D
### 3-Year TCO
Year 1: $Z + $A + $B + $C + $D
Year 2: $B + $C + $D
Year 3: $B + $C + $D
Total: $XXX
---
## Option B: Buy SaaS Solution
### Initial Costs
- Implementation/integration: $X
- Training: $Y
### Ongoing Costs (Annual)
- License fees: $Z/year
- Per-user costs: $A × users
- API costs: $B
### 3-Year TCO
Year 1: $X + $Y + $Z + $A + $B
Year 2: $Z + $A + $B
Year 3: $Z + $A + $B
Total: $XXX
| Capability | Build When | Buy When |
|---|---|---|
| Authentication | Unique security requirements | Standard OAuth/OIDC works |
| Payments | Core business differentiator | Standard e-commerce |
| Search | Domain-specific relevance | Generic search needs |
| Analytics | Proprietary insights needed | Standard dashboards work |
| High volume, custom delivery | Standard transactional | |
| ML/AI | Proprietary models needed | Pre-trained models work |
| Scale | Users | Monthly Infra | Notes |
|---|---|---|---|
| Startup | 0-1K | $200-500 | Single server, managed DB |
| Growth | 1K-10K | $500-2,000 | Load balancer, caching |
| Scale | 10K-100K | $2,000-10,000 | Horizontal scaling |
| Enterprise | 100K-1M | $10,000-50,000 | Multi-region, HA |
| Large | 1M+ | $50,000+ | Global, custom CDN |
| Application Type | Cost/User/Month | Notes |
|---|---|---|
| Simple web app | $0.05-0.20 | Static + API |
| Data-intensive | $0.20-0.50 | Analytics, storage |
| Real-time | $0.50-2.00 | WebSockets, streaming |
| ML-powered | $1.00-5.00 | Inference costs |
| Video/media | $2.00-10.00 | Transcoding, CDN |
## Monthly Infrastructure Cost by GMV
$0-100K GMV/month:
- Basic infrastructure: $500
- Payment processing (2.9%): ~$2,000
- Total: ~$2,500
$100K-1M GMV/month:
- Scaled infrastructure: $2,000
- Payment processing: ~$20,000
- Fraud protection: $500
- Total: ~$22,500
$1M-10M GMV/month:
- HA infrastructure: $10,000
- Payment processing: ~$200,000
- Fraud/security: $5,000
- CDN/performance: $3,000
- Total: ~$218,000
Infrastructure:
Development:
Operations:
Third-Party Services:
| Strategy | Savings | Effort |
|---|---|---|
| Reserved instances | 30-60% | Low |
| Right-sizing instances | 20-40% | Medium |
| Spot instances (non-critical) | 60-90% | Medium |
| Storage tiering | 50-80% | Low |
| CDN caching | 30-50% bandwidth | Low |
| Optimization | Impact | Complexity |
|---|---|---|
| Caching (Redis) | 50-80% DB load reduction | Medium |
| Queue-based processing | Smooth traffic spikes | Medium |
| Auto-scaling | Pay for what you use | Medium |
| Serverless (appropriate use) | Variable → zero when idle | High |
| Multi-region read replicas | Reduce cross-region costs | High |
# Project: [Name]
# Duration: [X months]
## Development Costs
| Phase | Duration | Team Size | Cost |
|-------|----------|-----------|------|
| Discovery/Design | 2 weeks | 2 | $X |
| MVP Development | 8 weeks | 4 | $X |
| Testing/QA | 2 weeks | 3 | $X |
| Deployment | 1 week | 2 | $X |
| **Total Development** | | | **$X** |
## Infrastructure Costs (First Year)
| Component | Monthly | Annual |
|-----------|---------|--------|
| Compute | $X | $X |
| Database | $X | $X |
| Storage | $X | $X |
| Network | $X | $X |
| Third-party APIs | $X | $X |
| Monitoring/Tools | $X | $X |
| **Total Infrastructure** | **$X** | **$X** |
## Ongoing Costs (Annual)
| Category | Cost |
|----------|------|
| Infrastructure | $X |
| Maintenance (20% of dev) | $X |
| Support/On-call | $X |
| Tool licenses | $X |
| **Total Annual** | **$X** |
## Summary
| Metric | Value |
|--------|-------|
| Total First Year | $X |
| Annual Run Rate | $X |
| 3-Year TCO | $X |
| Cost per User (at scale) | $X |
## Quick Infrastructure Estimate
Inputs:
- Expected users: [X]
- Requests per user/day: [Y]
- Data storage per user: [Z GB]
- Growth rate: [W%/month]
Calculations:
- Daily requests: X × Y
- Monthly requests: Daily × 30
- Required compute: (Monthly requests / 100K) × $50
- Storage: X × Z × $0.10
- Database: (X / 10K) × $200
- Estimated monthly: Compute + Storage + Database × 1.3
12-month projection with growth:
Sum of (Monthly × (1 + W%)^month) for months 1-12
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概要と使いどころ
Detect common technical and organizational anti-patterns in proposals, architectures, and plans. Use when strategic-cto-mentor needs to identify red flags before they become problems.
日本語の概要は準備中です。原文の説明を表示しています。
Recommend architecture patterns (monolith, microservices, serverless, modular monolith) based on scale, team size, and constraints. Use when cto-architect needs to select the right architectural approach for a new system or migration.
日本語の概要は準備中です。原文の説明を表示しています。
Identify and challenge implicit assumptions in plans, proposals, and technical decisions. Use when strategic-cto-mentor needs to surface hidden assumptions and wishful thinking before they become costly mistakes.
日本語の概要は準備中です。原文の説明を表示しています。
Generate targeted clarifying questions (2-3 max) that challenge vague requirements and extract missing context. Use after request-analyzer identifies clarification needs, before routing to specialist agents. Helps cto-orchestrator avoid delegating unclear requirements.
日本語の概要は準備中です。原文の説明を表示しています。
Transform clarified user requests into structured delegation prompts optimized for specialist agents (cto-architect, strategic-cto-mentor, cv-ml-architect). Use after clarification is complete, before routing to specialist agents. Ensures agents receive complete context for effective work.
日本語の概要は準備中です。原文の説明を表示しています。
Deep expertise in ML/CV model selection, training pipelines, and inference architecture. Use when designing machine learning systems, computer vision pipelines, or AI-powered features.
日本語の概要は準備中です。原文の説明を表示しています。