A single LLM call is easy to price.
One prompt goes in. One response comes back. You get one cost.
A multi-agent workflow works differently.
An orchestrator may call several agents. One might retry after a failed tool call. Another might receive a much larger context than the first. By the time the task finishes, one business outcome may represent dozens of model calls.
A monthly token total won't tell you what happened.
To control multi-agent AI costs, you need to know which workflow ran, which agents consumed the tokens, where retries happened and what a successful result actually cost.
That's the level this guide focuses on.
Three things make multi-agent costs harder to understand.
Looking only at total token consumption hides which step generated the cost.
Repeated failures can turn reliability problems into cost problems.
That's why aggregate token totals aren't enough.
Total tokens and total dollars still matter.
But four other measures tell you much more about whether an agent workflow is operating efficiently.
Larridin's Spend & Token Intelligence applies that approach to AI activity. Instead of stopping at the total bill, it connects costs with agents, workflows and teams.
You don't need to rebuild your agent stack to get useful cost visibility.
You do need consistent identifiers.
Create one identifier when the orchestrator starts the workflow.
Pass that ID through every downstream model and agent call.
Langfuse can use a trace for this. Helicone can group related requests into a session. You can also pass your own identifier through the orchestration layer.
Without a shared ID, you see individual calls but can't reliably tell which ones belong to the same piece of work.
Add an agent role and step identifier to each call.
For example:
Agent: retrieval
Step: 2
Now you can answer a much more useful question.
Which step costs the most?
Record input tokens, output tokens, cached tokens when relevant and the dollar cost of each model call.
Langfuse can record usage and cost for individual generations and roll that data into traces.
Helicone tracks request-level cost and can group related requests into sessions.
That gives you the raw data required to calculate workflow costs.
Don't let a retry look like another normal call.
Record whether the call was:
That allows you to calculate how much money went toward producing the result and how much went toward recovering from problems.
Once all calls share the same workflow ID, add up the cost of each successful run.
Now you have a useful unit of economics.
Cost per completed task.
A team handling customer support can measure cost per resolved ticket.
An engineering workflow can measure cost per completed review.
A finance agent can measure cost per processed invoice.
That makes the number easier for a budget owner to understand.
Add the business dimensions you care about.
That might include:
Now AI spend can be analyzed by the part of the company creating it instead of only by model provider.
Larridin's Spend & Token Intelligence is designed to roll this activity into workflow, agent and team views alongside broader AI usage data.
Cost attribution usually breaks when identifiers disappear between systems.
An orchestrator might create a workflow ID but fail to pass it to a sub-agent.
Or three departments might share the same API key.
When that happens, the provider's bill may show the model and token count but not who created the cost.
Fix that upstream.
A dashboard can't reconstruct metadata that never existed.
Department attribution uses the same approach as workflow attribution.
Attach the department to the run.
Then carry that tag through the agent calls.
Marketing's content workflow can now be separated from finance's reporting workflow even if they use the same model provider.
Shared API keys make this harder because provider billing views can't automatically determine which department created each request.
The solution is to identify the work before or during execution, not during next month's invoice review.
Several platforms address parts of this problem.
Pay-i focuses on GenAI economics and ROI. It connects AI costs with business KPIs and allows organizations to examine spending by use case, team, business unit and agent.
Its Provisioned Capacity product also tracks reserved AI capacity and helps attribute that capacity to agents and workloads.
Pay-i raised $4.9 million in seed funding in 2025.
Mavvrik approaches the problem from cost governance.
Its platform covers AI models, agents, GPU infrastructure, cloud services and other technology costs. It provides cost allocation, chargeback, forecasting, anomaly detection and budget controls.
It also supports allocation across teams, projects and customers.
Both are worth evaluating if detailed AI cost governance is your main requirement.
Larridin covers a broader measurement problem.
Spend & Token Intelligence sits alongside AI adoption, workflow and productivity measurement. That allows leaders to look at both what an AI workflow costs and what the organization gets from it.
That difference matters.
A dedicated cost platform may go deeper into infrastructure or capacity management.
A broader AI measurement platform becomes more useful when the question is whether the spend produced a measurable business outcome.
A monthly AI budget is useful for finance.
It's less useful for catching a runaway agent.
Set controls closer to the workflow.
A single unusually expensive run should be visible quickly instead of disappearing inside a monthly total.
A rising retry rate can be an early sign that costs are about to move.
It also tells you why they're moving.
Compare this week with last week.
A slow increase may point to longer prompts, additional agents, larger context or a model change.
Helicone recommends alerts around 50%, 80% and 95% of a budget.
That gives teams time to investigate before the entire budget is consumed.
Alerts become much more useful when they're attached to workflow and ownership metadata.
“AI spend increased” doesn't tell anyone what to fix.
“The invoice-processing agent's retry cost doubled this week” does.
These operational signals connect with the broader questions CFOs ask about AI ROI and the visibility CIOs need across enterprise AI.
Several LLM observability platforms can provide the underlying cost data.
Multi-agent workflows didn't change the price of tokens.
They changed the unit of work.
One useful outcome may now require several agents, model calls, tool calls and retries.
Your measurement needs to follow the same chain.
Whether you use Langfuse or Helicone for agent-level observability, Pay-i or Mavvrik for AI cost management, or Larridin for spend measurement alongside broader AI usage and outcomes, the same rule applies.
Know what generated the cost before the invoice arrives.
Want to connect token spend with the agents, workflows and teams creating it? See Larridin's AI measurement platform.