LLM inference cost at scale

The uncomfortable property of LLM inference cost is that it grows faster than your traffic does. Users who like a feature use it more, conversations get longer, context accumulates, and agent workloads multiply calls per task. Doubling users rarely doubles the bill — it usually more than doubles it.

Where the money actually goes

• Context length. Every turn of a conversation re-sends history. Long-context features are billed on the whole window, every call.
• Model mix. Most teams route 100% of traffic to one premium model because it was the easiest thing to ship. This is normally the single largest overspend.
• Agent trajectories. One user action can become 20 model calls. Cost per task, not per call, is the unit that matters.
• Retries and validation failures. Invisible in dashboards that only count successful responses.
• Cache misses. Repeated or near-identical prompts paying full price every time.

The five levers that work

1. Route by difficulty. Classify the request and send easy work to a cheaper model behind a quality floor. This is the biggest single lever for most teams — commonly a 40–70% reduction with no measurable quality change.
2. Cache aggressively. Provider prompt caching for shared prefixes, plus semantic caching for near-duplicate requests. See semantic caching.
3. Trim context. Summarize history instead of replaying it. Most conversations do not need the full transcript.
4. Enforce structured output. Schema-valid responses on the first attempt remove the retry tax. See structured outputs.
5. Set budgets per workload. A per-key budget converts a surprise invoice into a policy decision.

Measure cost per successful task

Per-token cost tells you nothing about whether spend is productive. Per-task cost — total tokens across retries and agent steps, divided by tasks actually completed — is the number that tells you whether an optimization worked. Neural Router returns exact usage and cost on every routing receipt so this is directly computable rather than estimated.