tech-stack-eval

Tech Stack Evaluation

When to Use

• Comparing frontend/backend frameworks or libraries for new projects
• Evaluating cloud providers (AWS vs Azure vs GCP) for specific workloads
• Planning technology migrations with risk and effort assessment
• Calculating TCO including hidden costs; making build vs. buy decisions
• Assessing open-source library viability and ecosystem health

Do NOT use when: the decision is trivial (use team preference), the technology is already mandated, or this is an emergency production issue.

Context Required

From startup-context: product type, team skills, tech stack, stage, scale, budget. Also ask:

• What problem are you solving? (push back on solution-first thinking)
• Non-negotiable requirements (performance, compliance, team familiarity)
• Team experience with each option and timeline pressure (tight deadlines favor familiar tools)
• Growth expectations that affect scalability requirements

Workflow

1. Clarify the decision — What exactly is being decided and what are the real requirements? Push back if the user picks tech before defining the problem.
2. Identify candidates — List 2-4 realistic options. Exclude clearly wrong choices early.
3. Define weighted evaluation criteria — Select 6-8 criteria from the master list below. Assign weights based on the user's priorities (total = 100%).
4. Score each candidate — Rate 1-5 on each criterion with one-line justification per score.
5. Assess ecosystem health — Evaluate GitHub activity, npm/PyPI adoption, community strength, corporate backing, and trajectory (growing, stable, declining).
6. Calculate TCO — Project 5-year total cost including compute, storage, bandwidth, licensing, engineering time (setup + ongoing), and operational overhead. Engineering time is usually the largest cost for startups.
7. Analyze migration path — If migrating, estimate effort, risks, timeline, and recommend phased approach (strangler fig pattern).
8. Deliver recommendation — Clear winner with rationale and confidence level. No "it depends" without a follow-up question to resolve the ambiguity.

Output Format

# Tech Stack Evaluation: [Decision Title]
 
## Decision Context — what we are choosing and why it matters
## Candidates — table: technology, version, license, one-liner
## Evaluation Criteria — table: criterion, weight, why it matters
## Scoring Matrix — table: criterion (weight), scores per option, weighted total

Frameworks & Best Practices

Master Evaluation Criteria

Select 6-8 and assign weights (total = 100%):

• Performance — throughput, latency, resource efficiency for the specific workload
• Developer Experience — tooling, debugging, documentation quality, error messages
• Learning Curve / Team Familiarity — time to productivity for the current team
• Ecosystem & Libraries — packages, integrations, third-party support
• Maintenance & Longevity — release cadence, corporate backing, bus factor
• Hiring Pool — developer availability in your market and salary band
• Scalability — handle 10-100x growth without a rewrite
• Cost / Vendor Lock-in — TCO and switching cost if you need to move later
• Security & Compliance — vulnerability track record, compliance tooling readiness

Ecosystem Health Scoring

TCO Calculation Framework

Project over 12 months minimum (5 years for infrastructure decisions): compute, storage, bandwidth, licensing, engineering time (setup + ongoing maintenance x loaded cost), operational overhead (monitoring, on-call), and hidden costs (training, migration tooling, dual-running).

Engineering time is usually the largest cost for startups. A technology saving $200/month on hosting but costing 40 extra engineering hours to operate is a net loss.

Migration Risk Assessment

Use the strangler fig pattern: route new traffic to the new system, migrate old incrementally. Always maintain rollback capability. Set a concrete cut-off date -- half-migrated systems are the worst outcome.

Confidence Levels

Common Decision Anti-Patterns

• Resume-Driven Development — choosing tech for resumes, not fit
• Hype Cycle Trap — adopting at peak hype before stability is proven
• Premature Optimization — distributed systems when a single Postgres handles the load
• Sunk Cost Fallacy — refusing to migrate because of prior investment
• Ignoring Team Skills / Solution-First Thinking — picking tech nobody knows, or selecting technology before defining the problem

Related Skills

• architecture-design — chain when the tech stack decision feeds into a broader system design
• cicd-setup — chain to configure CI/CD for the chosen technology

Examples

Example prompt: "Compare React vs Vue for a SaaS dashboard. Priorities: developer productivity (40%), ecosystem (30%), performance (30%)."

Good output snippet:

Example prompt: "We're on Heroku at $2,400/mo. Should we migrate to AWS?"

Good output snippet: