llm-cost-latency-budget
GitHub用于在上线前估算LLM功能的成本与延迟，通过Token数学计算、模型分层、缓存策略及流式处理优化性能。输出包含月度成本预测、p95延迟分析及防超支护栏计划，避免生产环境账单惊喜。
Trigger Scenarios
Install
npx skills add mohitagw15856/pm-claude-skills --skill llm-cost-latency-budget -g -y
SKILL.md
Frontmatter
{
"name": "llm-cost-latency-budget",
"description": "Model the cost and latency of an LLM feature before it ships and surprises the bill. Use when asked to estimate LLM API costs, set a latency\/token budget, decide which model tier to use, or bring down the cost of an AI feature. Produces a cost & latency budget — token math per request, monthly cost projection, model tiering, caching\/streaming levers, p95 latency targets, and a guardrail\/alert plan."
}
LLM Cost & Latency Budget Skill
LLM features have a unit cost and a tail latency that demos hide and production exposes. This skill does the token math up front — what one request costs, what a million cost, where the p95 latency comes from — and lays out the levers (model tiering, caching, prompt trimming) so cost and speed are designed, not discovered.
Required Inputs
Ask for these only if they aren't already provided:
- The request shape — typical system prompt, user input, retrieved context, and output sizes (in rough tokens).
- Volume — requests/day now and at target scale; peak concurrency.
- Models in play — candidate model(s) and their per-token input/output prices.
- Targets — acceptable cost per request (or per user/month) and the latency users will tolerate (p50 / p95).
Output Format
Cost & Latency Budget: [feature]
1. Per-request token math — a table estimating tokens in/out per call, and the resulting cost at each candidate model's price.
| Component | Tokens | $ in | $ out |
|---|---|---|---|
| System prompt | |||
| Retrieved context | |||
| User input | |||
| Output | |||
| Per request | $x |
2. Monthly projection — per-request cost × volume, at current and target scale; the headline number leadership will ask for.
3. Model tiering — route easy requests to a cheaper/faster model and only escalate hard ones (cascade); show the blended cost. Often the single biggest saving.
4. Latency — where the p95 comes from (model TTFT + output length + retrieval + network), the target, and how streaming changes perceived latency even when total time is unchanged.
5. Cost levers — ranked by impact: prompt/context trimming, caching (prompt cache + response cache for repeats), shorter outputs (max_tokens), batching, tiering, and "do you need the model at all for this path."
6. Guardrails — per-user / per-day rate limits, a max-tokens cap, a spend alert threshold, and a kill switch — so a bug or abuse can't produce a surprise invoice.
Quality Checks
- Token estimates are itemised (system + context + input + output), not a single guessed number
- The monthly cost is projected at target scale, not just today's volume
- Model tiering / cascade is considered before accepting the flagship-model cost everywhere
- p95 (not just average) latency is targeted, and streaming is considered for perceived speed
- Caching is evaluated for repeated prompts/contexts
- A spend alert + rate limit + kill switch are specified to cap the downside
Anti-Patterns
- Do not budget on average latency — users feel the p95, and the tail is where AI features feel broken
- Do not default every call to the most capable model — most requests don't need it; tiering often cuts cost by more than half
- Do not forget output tokens cost more than input — verbose responses are often the hidden cost driver
- Do not ship without a spend cap and alert — an unbounded LLM feature is an unbounded bill
- Do not optimise cost before measuring it — itemise the real token usage first, then pull the biggest lever
Based On
LLM production cost/latency practice — token accounting, model cascades/tiering, prompt & response caching, and tail-latency budgeting.
Version History
- a38bc30 Current 2026-07-05 11:10
