Inference Economics

Make LLM API spend easier to explain, manage, and improve.

When AI usage becomes real operating cost, the question is no longer just what the models can do. It is where the spend is going, what is driving it, and what is worth fixing.

Book a 20-minute fit call See where it applies

The API bill is becoming a business question.

When LLM usage scales, the real question is which products, customers, workflows, or teams are creating cost, and what is commercially worth doing about it.

For AI product companies

Understand LLM COGS by feature, endpoint, customer-facing task, workflow, or account so pricing and gross margin decisions have real cost data behind them.

where AI usage is helping or hurting margin
which customers, tasks, or features are expensive relative to pricing
where optimization could reduce COGS without damaging quality

For internal AI and operations teams

Control API spend from agents, scheduled jobs, automations, RAG flows, retries, tool loops, and API keys with unclear ownership.

which workflows or teams are driving operating cost
where retries, agents, or long-context jobs may be inflating spend
where budget ownership, limits, routing, or redesign may be needed

Start with the LLM API Cost Audit.

The audit turns provider exports and usage data into a clearer view of spend concentration, cost-driver signals, and areas that may deserve deeper mapping or optimization.

Spend concentration Find where the bill is concentrated before guessing what to optimize.

→

provider model project API key service tier time period

Cost driver signals Separate normal usage growth from patterns that may deserve investigation.

→

input/output token profile cached token signal long-context signals model mix service tier usage batch vs standard time-based concentration

If the audit finds something worth pursuing

From there, the work can move into deeper mapping, prioritization, or implementation where the evidence supports it.

Map spend to real work Connect API usage to business context so cost can be understood at the level that matters.

products endpoints customers workflows agents teams accounts

Prioritize savings opportunities Rank what is worth acting on by impact, confidence, quality risk, and engineering effort.

savings potential confidence quality risk engineering effort business impact owner

Implement targeted fixes Turn high-confidence findings into measured cost reductions without guessing.

model routing context reduction caching output controls batch migration retry fixes

Find out whether your LLM spend is worth investigating.

In 20 minutes, we can talk through your usage, current visibility, and whether an API cost audit makes sense.

Book a fit call