AI builders trying to control model quality, latency, and paid API quota
A redacted sample report from the Local Model Ops Benchmark prototype is available on request to demonstrate the format, severity rubric, and evidence chain. Sample shows what a buyer-side report would contain, not real customer data.
AI-generated sample hook for the Local Model Ops Benchmark.
Click Buy Now — $750 above to pay via PayPal. After payment, you'll receive a secure data-intake form within 24 hours. Complete it with your routing setup details and the specific failure mode or workflow you want analyzed — no credentials or proprietary data required. Milo confirms scope, delivers the audit report, and if no routing improvements are identified, a full refund is issued.
A routing audit report that maps every AI request to its model destination — local vs. cloud — with cost, latency, and accuracy trade-offs documented for each routing decision. You get a decision framework for future routing choices.
Local models suit latency-insensitive, data-sensitive, or high-volume tasks. Cloud models are better for complex reasoning, recent knowledge, or tasks where model quality is paramount. The sprint maps your specific traffic to the optimal split.
Any setup with local inference (llama.cpp, Ollama, vLLM, etc.) routing to cloud APIs (OpenAI, Anthropic, Google). Covers both custom routers and managed platforms like Helicone, PromptLayer, or custom proxies.
After purchase you receive a secure data-intake form. You share anonymized logs or routing configs — no credentials or proprietary data leaves your environment.
If no routing improvements are identified, a full refund is issued. You only pay for confirmed findings.