Production Roadmap: Live Pages
Philosophy
First make it work, then optimize. The concept is validated with free, slow models. Once the system works, each piece improves independently.
Phase 0 — Concept Validation (now)
Goal: Demonstrate the idea works, even if slow.
Stack:
- Free model: Llama 3.1 8B on Groq (free tier)
- skillnet-docs page with content separated as React state
- User input for target language
- Direct API call from frontend or minimal backend
Result: Page translates. Takes 800ms-7s depending on cold/warm cache. But it WORKS. Showable as “this is what it does, optimization comes next.”
Cost: $0 (Groq free tier)
Time: 1-2 days
Phase 1 — Functional Iteration
Goal: Work in a real scenario without being optimal.
Stack:
- Minimal backend (FastAPI or Node) receiving requests and calling Groq
- SSE streaming so the page writes in real-time
- Cache for frequent results
Improvement over Phase 0: Streaming makes it feel instant. Repeated translations come from cache.
Cost: ~$0 (Groq free) + backend time
Time: ~1 week
Phase 2 — Fine-tuned Model (Quality)
Goal: Correct translations (accents, grammar, domain terminology).
Steps:
- Generate 500-1,000 pairs of translated markdown
- Train LoRA on local GPU (QLoRA 8B, ~1-2h) or cloud GPU (A100, ~20 min)
- Deploy LoRA on Together AI or Fireworks AI
Result: Idiomatically correct translations. The model knows “français” takes an accent and “employé” is spelled correctly.
Cost: ~$10 (RunPod) + ~$50/mo (Together/Fireworks)
Time: ~1 week
Phase 3 — Speed Optimization
Goal: Response in <500ms always.
| Option | Latency | Cost | Effort |
|---|---|---|---|
| Smart caching | Instant on repeat | $0 | Low |
| Prompt caching (Groq) | -50% input cost | Included | Low |
| Groq Performance Tier | Higher priority | ~$100-200/mo | Medium |
| Cerebras Developer | From $10 | From $10 | Low |
| Groq Enterprise LoRA | ~840 tok/s | Enterprise pricing | High |
Recommended path: Start with cache + prompt caching (covers 80% of cases at $0). If insufficient, upgrade to Groq Performance Tier (~$100-200/mo).
Phase 4 — Multi-Agent Expansion
Goal: Not just translation — multiple content adaptation modes.
Agents:
- Translator: Translate to any language preserving structure
- Simplifier: Reduce technical difficulty
- Expander: Generate new sections on demand
- Accessibility: Rewrite for dyslexia, low vision, etc.
Router: A small classifier (Needle or keyword-based) decides which agent to activate based on user intent.
Cost: Same as Phase 3, multiplied by N agents. Each agent is just a different prompt on the same model.
Cost Summary
| Phase | Initial cost | Monthly cost | Token/s |
|---|---|---|---|
| Phase 0: Validation | $0 | $0 | ~200-800 |
| Phase 1: Functional | $0 | $0 | ~200-800 |
| Phase 2: Quality (LoRA) | ~$10 | ~$50-100 | ~200-400 |
| Phase 3: Speed | ~$0-100 | ~$100-300 | ~500-1,000 |
| Phase 4: Multi-agent | ~$0 | ~$100-300 | ~500-1,000 |
Total for real production: ~$50-100/mo (Phase 2).
Future Decisions
- Base model for LoRA (Llama 3.1 8B, Qwen3 8B, Gemma 4)
- Hosting for fine-tuned model — see LoRA Serving Options
- Dataset size (500, 1,000, 2,000 pairs)
- Priority languages (French, German, Portuguese, Chinese)
- Cache: duration, invalidation strategy
- Router: rules-based or small model (Needle)
- Security: rate limiting, auth, output sanitization