The total cost is distributed
An agent workflow can consume tokens, paid APIs, databases, search, vector services, and compute across multiple environments. Provider billing shows pieces, not the connected operational story.
Use case
Build toward AI-agent cost attribution.
Product direction: connect model, MCP, API, and infrastructure spend to the edge workflow, user, device, team, and outcome responsible for it.
Talk to AxLoopWhat to attribute
Cost decisions need more than a monthly total.
- Input and output tokens by model and agent
- MCP tool invocation count and duration
- Paid API and data-service calls
- Retries, timeouts, and duplicate work
- Device, user, team, workflow, and outcome context
Fleet context changes the decision
A high-cost tool may produce a critical outcome, while a low-cost call repeated across hundreds of devices may create more waste. Rollups and trace detail reveal both patterns.
Measure without collecting content
Token counts, model identifiers, tool names, timing, and safe workflow labels can support attribution while prompt and parameter content remains excluded or redacted.
Common questions
Answers, briefly.
- Why is AI agent cost hard to attribute?
- One workflow can consume tokens from several model providers, paid APIs, search, databases, and compute. Each bill shows a piece. Attribution connects those pieces to the user, team, workflow, and outcome.
- What signals are used for cost attribution?
- Token counts by model, tool invocation counts and durations, paid API calls, retries and duplicate work, and safe workflow labels, joined with device, user, and team context.
- How could cost attribution identify waste?
- With future trace and cost signals, repeated retries, duplicate tool calls, and low-value calls across many devices could become visible when fleet rollups are combined with trace detail.
- Would cost attribution require prompt content?
- Not necessarily. Token counts, model identifiers, tool names, and timing can support attribution. AxLoop's product direction remains metadata-first, with content collection requiring explicit authorization.
AxLoop AI
Start with evidence at the edge.
Discover what exists. Build toward understanding the interaction.