Inference Economics

Make AI spend explainable.

Provider bills show usage and cost. They do not show which products, customers, workflows, agents, or jobs are creating the spend, whether the cost is justified, or what is worth improving.

Book a 20-minute fit call See where it applies

Where AI cost hides

AI spend rarely comes from one obvious line item. It builds up through product choices, workflow design, model selection, retries, context size, and automation patterns that become expensive at scale.

Usage pattern

Long context by default

Full conversation history, retrieved documents, or large files get sent even when the task only needs a small slice.

conversation history retrieved context large documents

Usage pattern

Verbose or unnecessary output

Responses are longer, more detailed, or more frequent than the workflow actually needs.

output length summaries generated reports

Usage pattern

Retries and agent loops

Agents, tools, or automations repeat calls when they fail, branch, or search for an answer.

tool calls retry logic agent loops

Usage pattern

Scheduled or broad automation

Jobs run across too many accounts, records, users, or documents instead of only where needed.

scheduled jobs bulk processing always-on workflows

Usage pattern

Model overuse

Expensive models or service tiers are used for tasks that may not need them.

model mix routing service tier

When AI spend becomes material, the bill is not enough.

Product teams need cost visibility by feature, customer, task, or account. Internal teams need visibility by workflow, agent, automation, job, or team.

AI product companies

Which features, customers, tasks, or accounts are driving the cost?

Spend often maps to

features customers tasks accounts

Internal AI operations

Which workflows, agents, automations, or jobs are creating the spend?

Spend often maps to

workflows agents automations jobs teams

Start with a cost diagnostic.

Use provider exports and usage data to find first-pass cost pockets, check the patterns inside them, and decide whether to stop, map deeper, or investigate specific fixes.

Find the first-pass cost pockets Start with the views available from provider data before asking engineering teams to instrument anything new.

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provider model project API key service tier time period

Check the patterns inside them Look for usage patterns that may explain why a cost pocket is growing, noisy, or worth investigating further.

→

input/output token profile long-context signals cached token signal model mix batch vs standard time-based spikes

What the diagnostic delivers

A focused readout that turns provider exports into a decision your product, finance, and engineering teams can act on.

What you receive	What it answers	Why it matters
Cost pocket readout	Where is spend concentrated by provider, model, project, API key, service tier, or time period?	Focuses attention on material areas before asking engineering teams to instrument anything new.
Usage pattern review	Do token profiles, long-context signals, caching, model mix, batch usage, retries, or spikes explain the spend?	Separates normal growth from suspicious waste, margin risk, or fixable system behavior.
Priority list	Which cost pockets deserve deeper mapping, further investigation, or no action?	Turns the first pass into a ranked set of decisions instead of an open-ended analysis project.
Decision memo	Should you stop here, map deeper, or investigate specific fixes?	Keeps follow-on work disciplined, commercial, and evidence-based.

The first pass starts from provider exports and usage data. If there is no meaningful signal, the recommendation may be to stop.

The Inference Economics method

AI cost work gets useful by adding context in layers. The diagnostic covers the first two layers; deeper mapping and fixes only happen where the evidence supports it.

Included in the diagnostic

Layer 01

Provider view

Start with the raw views available from usage and cost exports.

provider model project API key time period

Layer 02

Usage patterns

Identify whether a cost pocket looks like normal growth, suspicious waste, or worth deeper investigation.

token profile long context caching batch usage spikes

Follow-on work if warranted

Layer 03

Business mapping

Connect the cost to the product or operations context that explains it.

Product context

features customers tasks accounts

Operations context

workflows agents automations jobs teams

Layer 04

Targeted action

Decide what is worth changing, then apply targeted fixes where the evidence supports it.

Prioritize by

savings potential margin impact unit economics quality risk engineering effort

Implement with

model routing context reduction caching batch migration retry fixes

Find out whether your AI spend is worth investigating.

In 20 minutes, we can talk through your usage, current visibility, and whether a cost diagnostic makes sense.

Book a fit call