Surge Cloud \Poland

Step 2 (Validation): Add edge caching layer using Cloudflare Workers + Fly.io POPs in Warsaw, Berlin, Amsterdam. Implement prompt caching (80% hit rate for repeated queries) to achieve <100ms P95 latency. Launch self-serve platform with Vercel-style deployment: customers push code, EdgeForge handles model hosting and scaling. Expand to 50 customers, $100K MRR. Validate that edge performance drives retention (target: <5% monthly churn).

Step 3 (Growth): Launch fine-tuning service: customers upload proprietary data (legal contracts, medical records), EdgeForge trains custom Llama/Mistral models on H100 clusters, deploys to edge. Charge $2-5K/month per fine-tuned model. Add observability dashboard (LangSmith integration) showing cost per query, latency P95/P99, and accuracy metrics. Expand to 200 customers, $500K MRR. Key growth loop: fine-tuned models create lock-in, customers increase usage as accuracy improves.

Step 4 (Moat): Build proprietary model quantization pipeline: compress Llama 70B to run on edge with <200ms latency using 4-bit quantization + speculative decoding. Launch 'EdgeForge Models'—pre-optimized versions of open-source LLMs that run 3-5x faster than standard deployments. Create marketplace: third-party developers publish specialized models (legal AI, medical coding, financial analysis), EdgeForge takes 20% revenue share. Reach $2M ARR, raise Series A to expand to 20 edge POPs and add voice/multimodal inference. Defensibility: proprietary quantization techniques, network effects from marketplace, compliance certifications (SOC2, ISO27001, GDPR) that take 12-18 months to replicate.