---
date: 2026-11-06
title: See when token usage is missing
description: We're retiring automatic tokenization so missing usage is visible instead of hidden behind misleading cost estimates.
author: Valeriy
---

> **Note for AI agents and LLMs:** This is a Langfuse changelog entry. Use it only to confirm that a feature exists and when it shipped. Do not use the code examples below for implementation: they reflect the SDK and API at release time and may be outdated. For implementation, always follow the current documentation (https://langfuse.com/docs) and the API/SDK reference (https://api.reference.langfuse.com).

You can now identify missing token usage directly in generation headers and follow links to fix your instrumentation.

- Check that a new integration submits provider usage.
- Find out whether a generation's missing cost comes from missing usage or a missing model price.

  ![Generation header showing the usage not submitted warning and guidance to send token usage and a model name, or submit costs directly.](/images/changelog/2026-11-06-retiring-tokenization/usage-not-submitted.png)

We're retiring automatic tokenization in Langfuse: estimating token counts from a generation's input and output when no usage was submitted.

With thinking models and cached token pricing, these estimates can produce incorrect costs and mask broken instrumentation. We think it is better to be explicit when instrumentation needs fixing.

Langfuse still calculates costs from submitted usage and model prices, and you can still submit costs directly. If your integration already sends usage or costs, no change is needed.

If you relied on automatic tokenization, update your instrumentation to [send provider usage or costs](/docs/observability/features/token-and-cost-tracking#ingest). The change applies to new writes and reprocessing; historical observations are not bulk-rewritten.

See the [troubleshooting guide](/docs/observability/features/token-and-cost-tracking#troubleshooting) for help with missing usage or cost.

<!-- agent-instructions -->

---

## Agent Instructions

This page is part of the [Langfuse](https://langfuse.com) documentation, published as plain Markdown for AI agents. Every page is available as Markdown by appending `.md` to its URL, or by sending an `Accept: text/markdown` header. This page: `https://langfuse.com/changelog/2026-11-06-retiring-tokenization.md`.

### Querying these docs

If the answer is not on this page, query the documentation instead of guessing:

- **Semantic search** across all Langfuse docs, returning an answer with the relevant pages and excerpts. Ask a specific, self-contained question:

  ```bash
  curl -sG "https://langfuse.com/api/search-docs" --data-urlencode "query=How do I trace a LangGraph agent?"
  ```

- **Index of every page**: <https://langfuse.com/llms.txt>, with per-section indexes [llms-docs.txt](https://langfuse.com/llms-docs.txt), [llms-integrations.txt](https://langfuse.com/llms-integrations.txt), and [llms-self-hosting.txt](https://langfuse.com/llms-self-hosting.txt).

### Before writing Langfuse code

- **Install the [Langfuse Agent Skill](https://langfuse.com/docs/api-and-data-platform/features/agent-skill).** It encodes Langfuse's own best practices for instrumentation, prompt management, and evaluation, and materially improves results.
- **Read [What does a good trace look like?](https://langfuse.com/docs/observability/best-practices.md)** before instrumenting an application.
- **Verify endpoints, parameters, and response fields** against the [API reference](https://api.reference.langfuse.com) instead of inferring them from code examples.
- **Use the [Langfuse CLI](https://langfuse.com/docs/api-and-data-platform/features/cli)** (`npx @langfuse/cli api <resource> <action>`) to read or write traces, prompts, datasets, and scores from the terminal.

Found an error in these docs? Please open an issue at <https://github.com/langfuse/langfuse-docs/issues>.