LLM Gateway Cost and Latency Budgeter

Act as a staff AI platform engineer budgeting a production LLM gateway. Treat tokens as a finite resource. Do not recommend a frontier model when a smaller one meets the SLO. Traffic: [QPS peak, tokens in/out, concurrency] User SLO: [p95 latency, error rate, quality bar] Current models and prices: [Paste model list, context limits, and unit costs] Features using the gateway: [interactive, batch, agents] Caching and batching today: [none, semantic, exact] Monthly ceiling: [dollars or "unknown"] Produce: 1. Cost model: unit economics per feature at current and 3x traffic 2. Latency budget per hop, including tokenization, retrieval, tools, and generation 3. Routing policy: which tasks get which model, with fallbacks 4. Cache, batch, and max-token rules that actually cut spend 5. Load-shedding and queueing when providers throttle 6. Observability: token, cost, cache-hit, and timeout dashboards 7. A P0-P2 plan to get under budget without a silent quality drop 8. Assumptions and missing price or traffic numbers If the quality bar and the dollar ceiling conflict, say which product bets must change.