@hackproduct9: Your LLM bill isn't a model problem, it's an architecture problem. Here are 9 ways to cut LLM costs, merged from 15 common tactics. 💸 Most teams send every request to the biggest model with the full chat history and get a surprise invoice. A better default: what's the cheapest path that can still do this job? 🧭 1. Route by difficulty: tools first, a small model next, the big model only when needed. ✂️ 2. Trim the context: summarize old turns and drop irrelevant history. Fewer input tokens on every call. 📚 3. Retrieve, don't stuff: RAG sends a few relevant chunks, not every document. ⚡ 4. Prompt caching: reuse a long, fixed prompt prefix and pay a fraction for it. 🔁 5. Cache answers: same or similar question? Semantic caching skips the LLM call entirely. 📦 6. Lean outputs: cap max_tokens and ask for JSON, not essays. 🕒 7. Batch it: non-urgent work through batch APIs runs at about half price. 🛑 8. Agent guardrails: cap iterations, tool calls and tokens, so one runaway loop can't burn your budget. 📊 9. Track every dollar: cost per request and per feature, every day. 👀 What most people miss: output tokens usually cost several times more than input tokens, and agents multiply every call. The cheapest token is the one you never generate. Measure first, then fix the biggest line on the bill. 📸 Save this before your next AI architecture review, or send it to whoever owns the AI spend. Follow @hackproduct for AI engineering concepts you can actually picture. ⚡ . . #LLM #AIengineering #GenAI #LLMops #costoptimization

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Friday 02 October 2026 02:14:08 GMT