Technology Stack Consulting
Stack choice determines development cost, hiring speed, system performance, and maintenance complexity for 3–5 years ahead. Wrong framework or database choice is technical debt paid daily.
What Influences Stack Selection
Load and scaling. Starting load of 100 RPS and peak 10,000 RPS require different architectural decisions. Node.js with event loop excels at I/O-heavy tasks; Go wins at CPU-intensive computation; Python suits ML pipelines but lags in throughput without async.
Team and hiring. React developers on the market outnumber Svelte or Solid by an order of magnitude. Vue.js dominates in CIS markets. Angular is typical for corporate projects with Java-background teams.
Data type. Relational data with transactions → PostgreSQL. Documents with flexible schema → MongoDB. High-load caching → Redis. Graph connections → Neo4j. Time series metrics → TimescaleDB or InfluxDB.
Hosting budget. Serverless (Vercel, AWS Lambda) is cheap at low load, expensive at constant load. Kubernetes justifies when team is 5+ DevOps and load is high.
Frontend: Comparison
| Framework | Best Scenario | Weaknesses |
|---|---|---|
| Next.js | SaaS, e-commerce, corporate sites | Vercel vendor lock-in, complex App Router |
| Nuxt 3 | Vue-experienced teams, localization | Smaller ecosystem than Next |
| Remix | Form-heavy apps, nested layouts | New, fewer resources |
| Astro | Content sites, blogs, landing pages | Not for SPA with rich UI |
| SvelteKit | Performance over ecosystem | Few ready components |
Backend: Comparison
| Stack | Best Scenario | Weaknesses |
|---|---|---|
| Node.js + Fastify | High I/O APIs, real-time | Callback hell with complex logic |
| Laravel (PHP) | CRUD apps, fast startup | Scales horizontally worse |
| Django (Python) | Data-heavy, ML integrations | GIL limits concurrency |
| Go (Gin/Echo) | High load, microservices | Verbose code, no generics pre-1.18 |
| Rust (Axum) | Maximum performance | Steep learning curve |
| .NET (ASP.NET Core) | Enterprise, Windows environments | Harder for small teams |
Example: SaaS B2B Application
Typical requirements: 1000 client companies, 50,000 users, REST + WebSocket API, dashboards with reports, external service integrations.
Recommended stack:
Frontend: Next.js 14 (App Router) + TypeScript
Backend: Node.js + Fastify + Prisma ORM
Database: PostgreSQL 16 (primary) + Redis 7 (cache, sessions)
Queue: BullMQ (Redis-based)
Auth: Auth0 or Clerk
File storage: S3-compatible (AWS S3 / MinIO)
Search: Meilisearch or Elasticsearch
Infra: Docker + Kubernetes (EKS/GKE) or Render
CI/CD: GitHub Actions + ArgoCD
Monitoring: Grafana + Prometheus + Sentry
Arguments:
- Next.js + Fastify — one language (TypeScript) across stack, shared types via tRPC or OpenAPI-codegen
- Prisma — type-safe ORM, migrations out of the box
- BullMQ — task queues without Redis Streams complexity
- Clerk over Auth0 — cheaper at startup, better DX
Example: High-Load Platform
Requirements: 100K+ RPS, real-time events, global audience.
API Gateway: Kong or Nginx
Services: Go (Gin) for critical paths, Node.js for others
Database: CockroachDB (distributed SQL) or Aurora PostgreSQL
Cache: Redis Cluster + CDN (Cloudflare)
Queue: Apache Kafka
Search: Elasticsearch
Infra: Kubernetes multi-region (AWS + GCP)
Consultation Process
- Discovery call (1–2 hours) — business requirements, team, budget, timeline
- Analysis (1–3 days) — research similar projects, benchmarks
- Recommendations document — comparison table of options, ADR (Architecture Decision Record) with justification
- Q&A session — address team questions
Result — specific choice with arguments, not "depends on the task".
Technology stack consultation — 1–3 working days including document preparation.