cto-advisor

name	cto-advisor
description	Technical leadership for startup CTOs. Use when the user mentions architecture decisions, tech stack selection, build vs buy, tech debt management, team scaling, system design, infrastructure, security architecture, engineering process, code review standards, or any CTO-level technical decision. Also triggers on: CTO, technical architecture, microservices, monolith, database selection, API design, scaling, DevOps, CI/CD, engineering hiring, tech debt, code quality, system reliability, SRE, incident response.
user-invocable	false

CTO Advisor

Technical leadership frameworks for startup CTOs. Balance engineering excellence with business reality. Simple > clever. Boring technology wins.

Keywords

CTO, chief technology officer, architecture, tech stack, build vs buy, tech debt, team scaling, system design, microservices, monolith, database, API design, scaling, DevOps, CI/CD, engineering hiring, infrastructure, security, code quality, SRE, incident response, performance, reliability

Core Responsibilities

1. Architecture & System Design

Architecture decision process:

1. Define constraints (team size, budget, timeline, scale requirements)
2. List realistic options (what can this team actually build and maintain?)
3. Evaluate tradeoffs (performance, cost, complexity, hiring, vendor lock-in)
4. Document the decision (ADR — Architecture Decision Record)
5. Plan migration path (if changing existing system)

Monolith vs Microservices decision tree:

• Team < 10 engineers? → Monolith
• Single product? → Monolith
• Need independent deployment of components? → Consider microservices
• Can you afford the operational overhead? → If no, monolith
• Clear bounded contexts with different scaling needs? → Microservices make sense

Database selection:

Need	Recommendation
General purpose, relational data	PostgreSQL
Document store, flexible schema	MongoDB
High-speed caching	Redis
Full-text search	Elasticsearch / Meilisearch
Time-series data	TimescaleDB / InfluxDB
Graph relationships	Neo4j
Simple key-value at scale	DynamoDB
Analytics/OLAP	ClickHouse / BigQuery

The default stack for startups:

• Backend: Node.js/TypeScript or Python/FastAPI
• Frontend: React/Next.js or Vue/Nuxt
• Database: PostgreSQL + Redis
• Infrastructure: Railway, Render, or Vercel (avoid AWS complexity early)
• CI/CD: GitHub Actions
• Monitoring: Sentry + basic metrics

2. Tech Stack Selection

Evaluation criteria:

Factor	Weight	Questions
Team capability	30%	Can the team build and maintain this?
Hiring pool	20%	Can we hire for this technology?
Maturity	15%	Is this battle-tested or bleeding edge?
Performance	15%	Does it meet our scale requirements?
Cost	10%	Total cost of ownership?
Lock-in risk	10%	How hard is it to switch later?

The "boring technology" rule: Choose technology that's proven, well-documented, and has a large community. Save your innovation tokens for your product, not your infrastructure.

3. Build vs Buy Analysis

Decision framework:

Factor	Build	Buy
Core to your product?	Build if core differentiator	Buy if commodity
Team expertise?	Build if you have the skills	Buy if it's outside your domain
Time to market?	Build if no time pressure	Buy if speed matters
Customization needs?	Build if highly custom	Buy if standard works
Long-term cost?	Build if high volume makes it cheaper	Buy if low volume
Maintenance burden?	Build only if you can maintain it	Buy to offload maintenance

Rule of thumb: If it's not your core product and someone else does it well, buy it. Your engineering time is your most expensive resource.

4. Tech Debt Management

Tech debt categories:

• Deliberate-prudent: "We know this is a shortcut, and we know how to fix it later"
• Deliberate-reckless: "We don't have time for good design" (danger zone)
• Inadvertent-prudent: "Now we know how we should have built it"
• Inadvertent-reckless: "What's architecture?" (fire the dev lead)

Tech debt prioritization matrix:

Impact	High Frequency	Low Frequency
High Impact	Fix NOW	Fix this quarter
Low Impact	Fix when touching that code	Probably never

The 20% rule: Allocate 20% of engineering capacity to tech debt reduction. Non-negotiable. It compounds.

5. Team Scaling

Engineering team structure (by stage):

Stage	Team Size	Structure
Pre-seed	1-3	Everyone does everything
Seed	3-8	Full-stack generalists + 1 specialist
Series A	8-20	2-3 squads, tech lead per squad
Series B	20-50	Engineering manager layer, platform team
Growth	50+	Multiple orgs, principal engineers, architects

Hiring priorities by stage:

• First 5: Generalists who can ship fast
• 5-15: First specialists (DevOps, security, data)
• 15-30: First engineering managers, senior ICs
• 30+: Platform team, architecture review board

Engineering culture essentials:

• PR review within 24 hours
• Deploy to production daily (or faster)
• Blameless post-mortems
• Document decisions (ADRs)
• Knowledge sharing (tech talks, pairing)

6. Security Architecture

Security by stage:

Stage	Must Have	Nice to Have
Pre-seed	HTTPS, auth, input validation	Pen test
Seed	+ secrets management, RBAC, backups	SOC 2 prep
Series A	+ SOC 2, dependency scanning, incident response	Bug bounty
Series B+	+ SIEM, WAF, security team	ISO 27001

CTO Metrics Dashboard

Category	Metric	Target	Frequency
Velocity	Deployment frequency	Daily+	Weekly
Velocity	Lead time for changes	<1 day	Weekly
Quality	Change failure rate	<5%	Weekly
Quality	Mean time to recovery	<1 hour	Per incident
Health	Tech debt ratio	<20% of sprint	Monthly
Health	Test coverage	>80% critical paths	Monthly
Team	Engineering satisfaction	>7/10	Quarterly
Cost	Infrastructure cost / ARR	<15%	Monthly

Red Flags

• Deployment takes more than 30 minutes
• No one understands a critical system (bus factor = 1)
• "We'll fix it later" is the most common phrase
• Engineers spend >30% of time on toil (manual processes)
• No monitoring/alerting on critical paths
• Production incidents happening weekly
• Tech stack chosen by resume-driven development

Reference Files

• references/architecture.md — System design patterns, ADRs, scaling strategies
• references/tech-debt.md — Tech debt categorization, prioritization, reduction strategies
• references/team-scaling.md — Hiring, team structure, engineering culture
• references/stack-selection.md — Technology evaluation frameworks, default stacks