What it is
Cost per conversation divides total AI operating costs (LLM tokens, infrastructure, tool calls) by the number of customer or employee conversations handled by an agent. It helps teams compare AI agent efficiency against human agent costs and across different model choices. Tracking it over time reveals whether optimization efforts like routing or prompt trimming are working.
Why it matters
Mid-market leaders need a simple, board-friendly number to justify AI investment and catch runaway costs before they scale out of control.
Key components
- Token usage tracking
- Compute/infrastructure cost
- Tool call cost
- Resolution outcome data
How it connects
Data Cloud and agent telemetry feed the usage data needed to calculate cost per conversation across Agentforce deployments.
Good to know
Track cost per conversation alongside resolution rate—cheap conversations that don't resolve issues aren't actually a win.
Related terms
Agent Telemetry
The runtime data emitted by an AI agent — every decision, tool call, input, output, latency, and cost — used to monitor reliability, quality, and spend in production.
Flex Credits
Salesforce's consumption-based pricing unit for Agentforce — you pay per conversation, not per user license.
LLM Cost Attribution
The practice of tying every LLM call back to the task, agent, process, or skill that triggered it — across every vendor — so AI spend can be measured against outcomes, not just tokens.
Token-Maxxing
The deliberate strategy of spending generous amounts of inference tokens — through extended thinking, deep research loops, or multi-shot agentic chains — to maximize output quality. The "more tokens equals better answers" doctrine that emerged with reasoning models. Also spelled "token-maxing."
