Source page: https://www.o10.io/inference-spend

Plain-text reference for reading, copying, and citation. Examples are illustrative; use your own credentials for API requests.

Model data: https://www.o10.io/api/models.json
Content index: https://www.o10.io/llms.txt

# Inference spend: calculate cost per accepted outcome

Inference spend is the attributable cost of running model-powered workloads. Useful cost control connects that spend to accepted outcomes, including failed calls, retries, tools, and supporting infrastructure.

## Key takeaways

### What is inference spend?

Inference spend is the attributable cost of running model-powered workloads. Useful cost control connects that spend to accepted outcomes, including failed calls, retries, tools, and supporting infrastructure.

## Define a consistent cost boundary
Decide which costs belong to the comparison: model APIs, retrieval, tool services, serving infrastructure, monitoring, and human correction. Keep the same boundary for baseline and candidate results.
Track request or task identifiers so cost can be assigned to a workload. Shared capacity needs an explicit allocation method; unused commitments do not disappear simply because a new route has a lower marginal price.

## Use a reproducible calculation
For token-priced APIs, calculate each usage dimension at its relevant rate. Sum calls within a task and divide attributable cost by accepted outcomes. Publish the time window, traffic mix, price date, and acceptance criteria with the result.
Illustrative example: spending $500 on 10,000 attempted tasks gives a cost of $0.05 per attempt. If 8,000 are accepted, the cost per accepted task is $0.0625. These figures illustrate arithmetic, not a claimed customer result.

## Distinguish estimates from measured savings
A calculator projects costs under stated assumptions. A shadow experiment compares candidate behavior against observed traffic. A production result measures what happened after rollout. Those are different levels of evidence.
For savings, report baseline cost minus candidate cost, including overhead and any changes in acceptance rate. Do not label a list-price ratio as a demonstrated quality-equivalent saving.

## Methodology

Cost examples are illustrative. Measure quality and total costs on your own tasks, and check current endpoint terms before implementation.

## Related links

- [AI inference: from model output to production system](https://www.o10.io/ai-inference)
- [AI agents: useful actions, measurable outcomes](https://www.o10.io/ai-agents)
- [AI model selection: capabilities, constraints and evidence](https://www.o10.io/ai-models)

## Source URL

https://www.o10.io/inference-spend