Most teams ship their first LLM feature on a flagship model with zero optimization — then watch the bill scale linearly with usage. I find the waste and cut it, usually by 50–95%, without touching quality.
// Independent & model-agnostic — OpenAI, Anthropic, Google, open-weight
Identical task — the only variable is model choice & optimization.
Token price is one input among many. Architecture, model tier, caching, and batching swing the real cost far more than which logo is on the invoice — and almost nobody is measuring it.
Token costs hide inside cloud bills and feature teams. Without per-workload visibility, waste compounds silently as you scale.
Teams default the hardest model to every request — including the 70% of traffic a model 20× cheaper would answer just as well.
Caching, batch tiers, and routing routinely cut 50–95% — but only if they're designed in. Most stacks use none of them.
A repeatable sequence on a high-volume workload (1M docs/mo). Each step compounds on the last — the discounts multiply.
Move off the flagship to a fit-for-task tier and route by difficulty.
Reuse system prompts & context across calls — up to 90% off cached input.
Move latency-tolerant jobs to async batch tiers for a flat discount.
Lock in the savings with measurement and price-change alerts.
Cost optimization isn't one trick — it's a system. These are the seven levers I work through on every engagement, in priority order, against your actual workloads.
// The 7 levers — full framework
A fixed-scope cost audit maps your real per-workload token spend and hands you a prioritized savings roadmap — typically paying for itself many times over.
Book a cost audit →