Tag archive
Most teams know their total LLM bill and nothing else. Here's how to attribute every token to a feature, team or customer — and turn that into unit economics.
Orchestrator-subagent pipelines multiply token spend fast. A practical guide to measuring per-agent cost and cutting it with caching, routing and fan-out limits
Even in an era of AI and automation, the most valuable asset we have is judgment. We talk often about...
Provider prompt caching discounts the tokens you re-send; semantic caching skips the model call altogether when a new question is close enough to one you have a
The stable-prefix-first pattern that reuses cached input at up to 90% off across Anthropic, OpenAI and Google — with a provider table, Python before/after and a
A 2026 playbook for cutting LLM costs 50% on async jobs with the Anthropic and OpenAI Batch APIs — with Python code, a worked savings model and the pitfalls to
The serving-layer optimisation stack for self-hosted inference: quantisation, continuous batching, speculative decoding and prefix caching — with worked cost-pe
A durable method for cutting LLM API spend by around 60% — caching, routing and cascades, and batching — with worked cost tables and code, framed for India and
Three advanced levers cut LLM inference costs 50–90%: model distillation, semantic caching, and 2026-tier model routing. Code, decision trees, and a full cost m
Prompt caching is the highest-leverage cost-reduction technique available in 2026. Implementation patterns and real cost comparisons across Claude, GPT-4o, and
The complete 2026 guide to LLM knowledge distillation: teacher-student training, QLoRA with Axolotl, student model selection, eval frameworks and break-even ROI
A 2026 playbook for cutting LLM API costs 70–90% in production with three stacking levers — caching, model routing and prompt compression — with code, cost mode