Cutting LLM Token Bills 60%: A Production Engineer's Field Notes
Dev.to / 6/17/2026
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Key Points
- The author describes how token economics became a critical operational focus after receiving an AWS bill that exposed rapidly growing LLM inference costs across multiple regions.
- They report rebuilding their inference layer three times over 14 months and achieving more than a 60% reduction in monthly spend while keeping p99 latency under a 1.8-second SLA.
- The article emphasizes that input and output tokens have fundamentally different cost and optimization characteristics: inputs are more cacheable, batchable, and compressible, while outputs are interactive, latency-sensitive, and harder to amortize.
- It highlights the large pricing dispersion across 184 available global API models and argues for routing decisions based on the true per-token economics of inputs versus outputs rather than simply choosing the cheapest model.
- Using a spreadsheet-backed view, the author details several “workhorse” model choices (e.g., DeepSeek and Qwen) and explains why expensive output pricing (notably for GPT-4o) requires strict, limited use plus aggressive caching.
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