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

# Logs

This page describes the logging-based telemetry emitted by the platform. These are log events, not Prometheus metrics, and are intended for detailed debugging, performance analysis, and forensics.

Log events are typically ingested into a log backend such as OpenSearch or Loki and queried via Grafana or a similar tool.

## RDU manifest events

RDU manifest events are structured logs emitted per request by the model runtime. They contain token counts, high-level latencies, and a set of detailed timing fields.

These events are typically indexed into a log index (for example, an OpenSearch index) and can be filtered by fields such as model, tenant, pod, and time range.

### RDU manifest fields

| Field                        | Category        | Key                                     | Description                                                                  |
| ---------------------------- | --------------- | --------------------------------------- | ---------------------------------------------------------------------------- |
| Prompt tokens                | Tokens          | `prompt_tokens_count`                   | Number of input tokens in the prompt.                                        |
| Completion tokens            | Tokens          | `completion_tokens_count`               | Number of output tokens generated.                                           |
| Total latency                | Latency         | `total_latency`                         | End-to-end time from request start to last token (includes queue + compute). |
| Time to first token (TTFT)   | Latency         | `time_to_first_token`                   | Time from request submission to first token.                                 |
| Completion tokens per second | Throughput      | `completion_tokens_per_sec`             | Effective throughput over the entire completion.                             |
| Tokens/sec after first token | Throughput      | `completion_tokens_after_first_per_sec` | Decode throughput after first token (steady-state).                          |
| Acceptance rate              | Spec decoding   | `acceptance_rate`                       | Acceptance rate for speculative decoding.                                    |
| Decode queue time            | Internal timing | `decode_queue_time`                     | Time spent in decode-related queues (e.g. continuous batching queues).       |
| Tensor transfer time         | Internal timing | `tensor_transfer_time`                  | Time spent transferring tensors between components.                          |
| Cache transfer time          | Internal timing | `cache_transfer_time`                   | Time spent transferring cache (e.g. KV cache).                               |

<Note>
  These fields are logging events and may be subject to schema evolution.
</Note>

## Example queries

Examples assume a log backend that supports a query language (e.g., OpenSearch or Loki) and timestamps on each event.

**p95 total latency per model (last 15 minutes)**

* Filter: `model:"<model_name>" AND @timestamp:[now-15m TO now]`
* Aggregate: percentile 95 on `total_latency` grouped by `model`.

**TTFT vs total latency comparison**

* Group by `model`, compute p50/p90 for both `time_to_first_token` and `total_latency`.

**Decode queue time hotspots**

* Filter: `decode_queue_time > <threshold>`
* Group by `model` or `tenant` to identify where queueing is highest.

**Speculative decoding acceptance rate**

* Filter: `model:"<model_name>"`
* Aggregate: average `acceptance_rate` over time.

## Related topics

* [Monitoring and Observability](/en/v1.2.0/sambastack/reference-architecture/observability/overview) – High-level telemetry breakdown and hierarchy.
* [Metrics](/en/v1.2.0/sambastack/reference-architecture/observability/metrics) – Router-level metrics reference.