> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tensormesh.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Serverless Usage

> Monitor token consumption, cache hit rates, and per-model costs for your serverless inference calls.

Navigate to **Operations → Serverless Usage** to monitor your token consumption across serverless inference calls.

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## What You'll See

| Metric                | What it shows                                                                                            |
| --------------------- | -------------------------------------------------------------------------------------------------------- |
| **Input Tokens**      | Total tokens sent in requests (system prompt + messages)                                                 |
| **Output Tokens**     | Total tokens generated by models                                                                         |
| **Cached Tokens**     | Input tokens served from the KV cache at \$0.00                                                          |
| **GPU Cache Hit**     | Subset of cached tokens served from the vLLM prefix cache (L0 — GPU memory). Fastest possible cache hit. |
| **CPU & Storage Hit** | Subset of cached tokens served from LMCache (L1 CPU memory or L2 External Storage).                      |
| **Cache Hit Rate**    | Percentage of input tokens served from cache                                                             |

A chart tracks your cache hit performance over time so you can see whether prompt changes are improving cache efficiency.

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## Cache Hit Rate

* Shows what percentage of input tokens were served from cache at **\$0.00**
* Higher = your prompts share consistent prefixes, no repeated compute costs
* **If it's low:** the most common cause is a variable or inconsistent system prompt, or dynamic content appearing too early in the prompt

See [Pricing Overview](/pricing-overview) for strategies to improve cache hit rate.

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## Per-Model Cost

The usage table breaks down by model:

* Token counts (input, output, cached) per model
* Cost per model
* Useful for spotting which models drive most of your spend
* A low cache hit rate on a specific model often signals inconsistent prompts in that flow

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## Token Pricing

| Token Type       | Cost                                         |
| ---------------- | -------------------------------------------- |
| **Input Token**  | Per-model rate (see **Deploy → Serverless**) |
| **Output Token** | Per-model rate                               |
| **Cached Token** | **\$0.00**                                   |

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## Related

* [Cache Savings](/cache-savings)
* [Pricing Overview](/pricing-overview)
* [External Storage](/external-storage)