Problem Statement
The workflow aims to forecast the operating cost of an LLM-powered feature before launch. It uses expected traffic, prompt lengths, model choices, tool call patterns, and caching assumptions to produce cost ranges, cost drivers, mitigation tactics, and budget guardrails. The goal is to provide a practical, assumption-transparent estimate suitable for pre‑launch planning.
Steps
Gather Usage and Cost Assumptions
INGESTCollect projected request volume, prompt/response lengths, chosen models, tool-call frequency, and expected cache hit rates. Normalize all inputs into structured values that can be used for estimation.
Retrieve Model Pricing
EXECUTEPull current per‑token or per‑call pricing for the selected LLM models and any tool‑calling services. These pricing details will be used to compute ranges based on token assumptions.
Generate Cost Range Estimate
EXECUTEUse the inputs and pricing to compute low, mid, and high cost estimates. Adjust for cache hit rates, tool-call multipliers, and potential prompt-length variance.
Identify Cost Drivers and Reduction Levers
EXECUTEAnalyze which components contribute most to cost and generate tactical recommendations such as caching policies, truncation strategies, or reranking pipelines.
Validate Guardrails and Monitoring Plan
EXECUTECheck that assumptions, ranges, and guardrails are coherent and produce a monitoring plan covering spend, traffic, token usage, and cache performance.
Prompt Configurations
LLM Cost Forecasting Prompt
You are an analytical, practical cost‑forecasting assistant. Using the inputs provided (traffic, prompt/response lengths, model choices, tool‑call expectations, and caching assumptions), produce: (1) cost estimate ranges, (2) biggest cost drivers, (3) cost‑reduction tactics including caching, truncation, and reranking, (4) a monitoring plan, and (5) budget guardrails. Be explicit about assumptions, reference only provided data, and do not invent numbers not grounded in the inputs. Use a clear, analytical tone suitable for product planning.
Example Inputs/Outputs
Daily traffic: 50,000 requests. Prompt length: 2,000 input tokens, 800 output tokens. Model: GPT‑4o. Tool calls: average 1.2 per request. Cache hit rate: 25%.
Estimated daily cost range: $820–$1,050. Major cost drivers: long prompts, output tokens, and low cache hit rate. Cost‑reduction tactics: aggressive prompt truncation, moving static content to embeddings, and caching deterministic tool results. Monitoring plan: track token usage per request, traffic volatility, cache performance, and effective cost per user. Budget guardrails: daily spend cap of $1,200 with alerts at 80% utilization.