> For the complete documentation index, see [llms.txt](https://docs.uptiq.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.uptiq.ai/ai-workbench-qore/control-center/observe/llm-logs.md).
# LLM Logs
Trace model calls, token use, cost, and latency.
LLM Logs records every LLM completion and embedding call the platform made. Open **Control Center → Observe → LLM Logs**, on either the **Gateway Logs** or **Embedding Logs** tab.
## Gateway Logs
Gateway Logs records every completion call, with its model, target, tokens, and cost.
Gateway Logs, tracing every completed model call.
You can narrow by project and model. You can also search by model or agent. Each row is one completed model call:
| Field | What it shows |
| ----------- | ------------------------------------------- |
| **Model** | The model that ran, such as `gpt-4.1-mini`. |
| **For** | The target the call was made for. |
| **Tokens** | Tokens consumed by the call. |
| **Cost** | The credit cost of the call. |
| **Latency** | How long the call took, in milliseconds. |
| **When** | The completion time. |
| **Status** | The call outcome, such as **Success**. |
## Embedding Logs
Every query sent to an embedding model gets an entry in Embedding Logs, from any provider and any part of the platform, including calls that fail. It records embedding calls, not completions.
Embedding Logs, with one entry expanded.
You can narrow by project, model, and date range. You can also search by query or model. Each row is one embedding call:
| Field | What it shows |
| ---------- | --------------------------------------------------------------------------------- |
| **Query** | The text that was embedded — a document chunk, a search query, or an instruction. |
| **Model** | The embedding model that ran, such as `OpenAI Text Embedding Ada 002`. |
| **When** | The completion time. |
| **Status** | **Success** or **Failed**. |
Select a row on either tab to see the call's full detail. On **Embedding Logs**, an expanded row shows the full query text, the model, and how long the call took, in milliseconds.
***
{% columns %}
{% column width="83.33333333333334%" %}
Maintained by Abhishek Paul AI-assisted, human-approved
{% endcolumn %}
{% endcolumns %}
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.
Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:
```
GET https://docs.uptiq.ai/ai-workbench-qore/control-center/observe/llm-logs.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
