Marco Rossi·
Weighted scoring matrix told me Fastify over Go for my team and actually explained why
Evaluates and recommends the optimal technology stack across frontend, backend, database, DevOps, and testing using weighted scoring and risk analysis.
Tech Stack Selection Framework
You are a CTO-level technical strategist who has evaluated technology stacks for Series A through IPO-stage companies. Help my team select the optimal tech stack.
**Project Type**: {{project_type}} (e.g., B2B SaaS, consumer mobile app, data-intensive platform)
**Team Composition**: {{team_composition}} (size, seniority levels, existing expertise, hiring market)
**Non-Functional Requirements**: {{non_functional_requirements}} (latency, throughput, availability, compliance)
**Constraints**: {{constraints}} (budget, existing integrations, vendor lock-in tolerance, open-source preference)
Provide a structured evaluation:
1. **Frontend Layer Options** - Compare 3-4 frameworks (React, Vue, Svelte, Angular) scored on: ecosystem, performance, hiring, learning curve, mobile support
2. **Backend Layer Options** - Compare 3-4 platforms (Node/Express, Python/FastAPI, Go, Java/Spring, Rust) with benchmarks
3. **Database Layer** - SQL (PostgreSQL, MySQL, CockroachDB) vs NoSQL (MongoDB, DynamoDB, Cassandra) decision matrix
4. **Cache Layer** - Redis, Memcached, KeyDB, Dragonfly comparison with use case fit
5. **Message Queue** - Kafka, RabbitMQ, NATS, AWS SQS/SNS comparison for your event patterns
6. **Cloud Platform** - AWS vs GCP vs Azure decision framework with service mapping
7. **Container Orchestration** - ECS vs EKS vs GKE vs self-managed K8s cost and complexity analysis
8. **CI/CD Pipeline** - GitHub Actions, GitLab CI, CircleCI, Jenkins comparison with pipeline architecture
9. **Observability Stack** - Metrics (Prometheus/Grafana), Logs (ELK/Loki), Traces (Jaeger/Tempo/Zipkin), APM (Datadog/NewRelic)
10. **Testing Strategy** - Unit, integration, e2e tool selection with testing pyramid distribution
11. **Security Stack** - Auth (Auth0/Cognito/Keycloak), secrets management, WAF, SAST/DAST tools
12. **Risk Analysis** - For each choice: bus factor, vendor lock-in risk, community health, 5-year viability prediction
Score each option 1-10 across at least 6 dimensions and provide a weighted recommendation matrix.
Ergebnisse
Tech-stack evaluation for a **B2B SaaS** (team of 8, mostly mid-level TS, 99.9% availability target, GDPR, open-source preferred). Scored 1-10 on the dimensions that move the needle for *this* team.
**Backend candidates**
| Option | Ecosystem | Perf | Hiring | Learning curve | Weighted |
|--------|-----------|------|--------|----------------|----------|
| Node/Fastify | 9 | 7 | 9 | 9 | **8.5** |
| Go | 8 | 9 | 6 | 6 | 7.4 |
| Python/FastAPI | 9 | 6 | 8 | 8 | 7.8 |
**Recommendation: Node + Fastify.** With a TS-fluent team, sharing types across the wire (frontend ↔ backend ↔ DB) removes a whole class of bugs; Go's raw throughput isn't the binding constraint at your scale and would slow hiring.
**The rest of the stack**
- **Frontend:** React + Vite — largest hiring pool, mature a11y tooling.
- **DB:** PostgreSQL — JSONB covers your semi-structured needs, no premature NoSQL.
- **Cache/queue:** Redis (cache + BullMQ jobs) — one dependency, two jobs.
- **Cloud:** AWS on ECS Fargate — no K8s ops burden for an 8-person team.
- **Observability:** Prometheus + Grafana + Loki (self-hosted) to dodge Datadog's per-host bill.
**Risk flags:** Fastify's plugin ecosystem is thinner than Express (mitigated, plugins are easy to author); managed Fargate is mild AWS lock-in (mitigated by keeping everything in plain Docker images).
Modell: Claude Opus 4
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