Key takeaways
- Model costs are often only part of what an AI agent costs. OCR, paid APIs, retrieval, and storage can make up a larger share of a workflow’s cost than the model calls themselves.
- Langfuse tracks model usage and costs well, including custom pricing and analysis by user and tag. The question is whether your team also needs paid non-model services and business attribution in the same view.
- Business context (customer, user, application, use case, session) lets finance answer questions like “what did this customer’s activity cost, and why did it go up?”
- Mavvrik integrates with Langfuse, so teams can keep their observability setup and build cost reporting on telemetry they already collect.
Best for
- FinOps leaders
- Finance teams
- AI platform and engineering teams running agents
“Why can’t we just use Langfuse?”
You can, if its cost reporting answers the questions your team needs to answer. Langfuse supports model usage and cost tracking, custom pricing, and cost analysis by users and tags. Those are useful capabilities.
The next question is whether that view includes the paid services surrounding your models, carries the business context your organization needs, and works across the different environments where your agents run.
Mavvrik provides a common cost and attribution layer across supported agent environments. Its Agent SDK captures supported model usage, lets developers meter additional paid operations, and connects that activity to customers, sessions, applications, and use cases.
For teams already using Langfuse, Mavvrik also offers an integration. You can build on the telemetry you already collect. Learn how Mavvrik tracks agent costs across the enterprise stack.
Why model cost does not explain the whole interaction
An AI agent may consume several paid resources to complete one task:
- Model calls to interpret a request, choose an action, and generate a response.
- OCR or document extraction charged by the page.
- Search or business APIs charged by the request.
- Retrieval services and storage with their own billing models.
A model-cost report answers how much the recorded model calls cost. Understanding the interaction also requires accounting for the other billable resources it consumed.
Consider a document-processing agent:
| Recorded activity | Illustrative cost |
|---|---|
| Model calls | $0.04 |
| OCR processing | $0.06 |
| Paid verification API | $0.10 |
| Total recorded workflow cost | $0.20 |
In this example, model usage accounts for 20% of the recorded workflow cost. Optimizing tokens alone would leave the largest cost driver untouched.
Mavvrik’s custom metering lets developers record paid non-model consumption alongside supported model usage. Quantity and a supplied rate make the calculation explicit: pages processed multiplied by price per page, or API requests multiplied by price per request. A named operation can also be added to show that activity as a distinct execution step.
The coverage depends on what you capture and meter. A recorded workflow total should not be confused with fully allocated cost-to-serve, which may also include shared infrastructure and other operating expenses.
Why use Mavvrik if Langfuse already tracks costs?
The distinction becomes clearer when you look at the work required to answer a business question.
Suppose finance asks:
“What did this customer’s document-processing activity cost last month, and why did it increase?”
Answering that requires more than a model total. The team needs to:
- Identify the relevant customer and workflows.
- Include the paid resources those workflows consumed.
- Apply the appropriate rates.
- Aggregate the recorded costs.
- Investigate the operations behind the increase.
You could build and maintain that reporting around your existing telemetry. Mavvrik provides capabilities designed for this cost-management workflow.
| Requirement | How Mavvrik supports it |
|---|---|
| Include paid services beyond models | Custom metering for non-LLM consumption |
| Connect consumption to business activity | Context for customers, users, sessions, applications, use cases, and tags |
| Use contracted model rates | Configurable LLM pricing |
| Investigate an expensive interaction | Cost reporting connected to supported execution activity |
| Bring multiple agent environments into one view | SDKs and supported integrations, including Langfuse |
Langfuse’s cost features are real. The decision is whether they meet your reporting needs or whether you want a dedicated platform to bring these additional inputs into a common cost-management model.
Mavvrik’s configurable model pricing also allows reporting to reflect contracted rates when they differ from default prices.
Give the cost a customer, team, and purpose
A session ID helps locate an interaction. Business context tells you where its cost belongs.
With Mavvrik Agent SDK, developers can set context around a request, workflow, session, or job. Supported model calls and metered operations created within that boundary inherit the context.
That context can identify:
- The customer receiving the work.
- The user initiating it.
- The application and use case.
- The session or request.
- Additional reporting dimensions, such as team or product.
This supports questions such as, “What did this customer consume?” and “Which use case is driving spend?” The application must supply the relevant context; the SDK carries it through supported activity.
This is also why cost allocation matters for financial intelligence: knowing the total is only the beginning. Teams need to connect spend to the business activity responsible for it.
Keep the evidence behind an expensive session
A cost increase gives finance a reason to investigate. Execution detail gives engineering somewhere to look.
For the document-processing example, the useful question is whether the increase came from more pages, additional verification requests, or greater model consumption.
Mavvrik connects cost reporting with supported session activity so teams can examine the operations behind recorded spend. That helps finance and engineering investigate the same interaction using its cost and execution context.
Agent-level reporting is one part of Mavvrik’s broader AI cost governance approach, which addresses costs across the technology stack.
What does the SDK capture automatically?
Mavvrik Agent SDK observes supported AI libraries without replacing existing provider or framework calls. Depending on the integration and the telemetry exposed, it records model usage, timing, status, trace relationships, and pricing inputs.
Paid resources that cannot be priced automatically require explicit metering. The SDK is not a general application-monitoring agent that automatically discovers every database, storage, or internal application cost.
Prompt and response text is not required or sent by default for standard SDK cost reporting.
See what your agent workflows actually cost
Explore how Mavvrik brings supported model usage, metered paid services, and business attribution together. Bring one production workflow to a demo to discuss the resources it consumes and the cost questions your team needs to answer.
Frequently Asked Questions
Can we just use Langfuse for AI agent cost tracking?
Yes, if its reporting covers your requirements. Langfuse supports model costs, custom pricing, and analysis by users and tags. Evaluate whether you also need metered non-model services and cost reporting across additional agent environments.
Does Mavvrik replace Langfuse?
Teams can use them together. Mavvrik’s Langfuse integration brings supported observations into its agent cost reporting while Langfuse continues serving as the observability environment.
Does Mavvrik automatically capture every agent cost?
No. It captures supported model activity automatically. Developers must explicitly meter additional paid resources and supply the business context needed for attribution.
Can we track costs without collecting prompts and responses?
Yes. Standard Mavvrik SDK cost reporting does not require prompt or response text.
When should we consider Mavvrik?
Consider Mavvrik when you need to combine model usage with paid workflow services, attribute that consumption to business activity, and investigate costs across supported agent environments.
Start with one production workflow: identify its paid resources, add the context needed for reporting, and verify one known session using the Mavvrik SDK setup guide.
