Token Costs, TTFT/TPOT & ROI

LLM Token Economics, TTFT/TPOT Latency Breakdown & ROI Optimization defines the quantitative systems and financial engineering required to scale GenAI applications economically; the latency taxonomy comprises: 1) Time to First Token (TTFT: prefill phase computation), 2) Time Per Output Token (TPOT: autoregressive decoding speed), and 3) End-to-End Latency ($TTFT + N_{\text{out}} \times TPOT$); core cost-optimization levers encompass: Multi-tier Semantic Caching (reducing API calls by 30%-50%), Prompt Caching (saving 75% on repeated prefix tokens), Model Cascades/Routers (dispatching 80% of low-complexity queries to small cheap models, reserving flagship LLMs for complex tasks), and Speculative Decoding.

High-volume LLM gateway design, SaaS gross margin optimization, and interactive streaming user experience tuning.

Naively routing every query to flagship models blows up operating expenses and delivers sluggish 3-5 second TTFTs; cascading optimization slashes operating costs by 80% while driving TTFT below 300ms.

Contrast Compute-Bound Prefill vs Memory-Bound Decode phases and analyze their direct impacts on TTFT and TPOT?

Design a dual-tier semantic cache combining exact hash matching with vector cosine thresholding (>0.95) to serve instantaneous cached responses?

Explain the underlying KV cache reuse mechanism of Prompt Caching and its financial cost advantages on long system prompts?

How does FrugalGPT dynamic model routing dynamically triage queries between 7B and 70B models to maximize accuracy per dollar?

How to build real-time LLMOps cost dashboards with per-tenant token quotas and automated circuit breakers?