---
# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
title: Observability
---

This guide covers metrics, tracing, and visualization for SGLang deployments running through Dynamo.

## Prometheus Metrics

When running SGLang through Dynamo, SGLang engine metrics are automatically passed through and exposed on Dynamo's `/metrics` endpoint (default port 8081). This allows you to access both SGLang engine metrics (prefixed with `sglang:`) and Dynamo runtime metrics (prefixed with `dynamo_*`) from a single worker backend endpoint.

**For the complete and authoritative list of all SGLang metrics**, always refer to the [official SGLang Production Metrics documentation](https://docs.sglang.io/references/production_metrics.html).

**For Dynamo runtime metrics**, see the [Dynamo Metrics Guide](../../observability/metrics.md).

**For visualization setup instructions**, see the [Prometheus and Grafana Setup Guide](../../observability/prometheus-grafana.md).

### Environment Variables

| Variable | Description | Default | Example |
|----------|-------------|---------|---------|
| `DYN_SYSTEM_PORT` | System metrics/health port | `-1` (disabled) | `8081` |

### Getting Started Quickly

This is a single machine example.

#### Start Observability Stack

For visualizing metrics with Prometheus and Grafana, start the observability stack. See [Observability Getting Started](../../observability/README.md#getting-started-quickly) for instructions.

#### Launch Dynamo Components

Launch a frontend and SGLang backend to test metrics:

```bash
# Start frontend (default port 8000, override with --http-port or DYN_HTTP_PORT env var)
$ python -m dynamo.frontend

# Enable system metrics server on port 8081
$ DYN_SYSTEM_PORT=8081 python -m dynamo.sglang --model <model_name> --enable-metrics
```

Wait for the SGLang worker to start, then send requests and check metrics:

```bash
# Send a request
curl -H 'Content-Type: application/json' \
-d '{
  "model": "<model_name>",
  "max_completion_tokens": 100,
  "messages": [{"role": "user", "content": "Explain why Roger Federer is considered one of the greatest tennis players of all time"}]
}' \
http://localhost:8000/v1/chat/completions

# Check metrics from the worker
curl -s localhost:8081/metrics | grep "^sglang:"
```

### Exposed Metrics

SGLang exposes metrics in Prometheus Exposition Format text at the `/metrics` HTTP endpoint. All SGLang engine metrics use the `sglang:` prefix and include labels (e.g., `model_name`, `engine_type`, `tp_rank`, `pp_rank`) to identify the source.

**Example Prometheus Exposition Format text:**

```
# HELP sglang:prompt_tokens_total Number of prefill tokens processed.
# TYPE sglang:prompt_tokens_total counter
sglang:prompt_tokens_total{model_name="meta-llama/Llama-3.1-8B-Instruct"} 8128902.0

# HELP sglang:generation_tokens_total Number of generation tokens processed.
# TYPE sglang:generation_tokens_total counter
sglang:generation_tokens_total{model_name="meta-llama/Llama-3.1-8B-Instruct"} 7557572.0

# HELP sglang:cache_hit_rate The cache hit rate
# TYPE sglang:cache_hit_rate gauge
sglang:cache_hit_rate{model_name="meta-llama/Llama-3.1-8B-Instruct"} 0.0075
```

**Note:** The specific metrics shown above are examples and may vary depending on your SGLang version. Always inspect your actual `/metrics` endpoint or refer to the [official documentation](https://docs.sglang.io/references/production_metrics.html) for the current list.

### Metric Categories

SGLang provides metrics in the following categories (all prefixed with `sglang:`):

- **Throughput metrics** - Token processing rates
- **Resource usage** - System resource consumption
- **Latency metrics** - Request and token latency measurements
- **Disaggregation metrics** - Metrics specific to disaggregated deployments (when enabled)

**Note:** Specific metrics are subject to change between SGLang versions. Always refer to the [official documentation](https://docs.sglang.io/references/production_metrics.html) or inspect the `/metrics` endpoint for your SGLang version.

### Available Metrics

The official SGLang documentation includes complete metric definitions with:
- HELP and TYPE descriptions
- Counter, Gauge, and Histogram metric types
- Metric labels (e.g., `model_name`, `engine_type`, `tp_rank`, `pp_rank`)
- Setup guide for Prometheus + Grafana monitoring
- Troubleshooting tips and configuration examples

For the complete and authoritative list of all SGLang metrics, see the [official SGLang Production Metrics documentation](https://docs.sglang.io/references/production_metrics.html).

### Implementation Details

- SGLang uses multiprocess metrics collection via `prometheus_client.multiprocess.MultiProcessCollector`
- Metrics are filtered by the `sglang:` prefix before being exposed
- The integration uses Dynamo's `register_engine_metrics_callback()` function
- Metrics appear after SGLang engine initialization completes

---

## Distributed Tracing

Dynamo propagates [W3C Trace Context](https://www.w3.org/TR/trace-context/) headers through the SGLang request pipeline, allowing you to correlate traces across the frontend, router, and individual SGLang workers in a disaggregated deployment.

### Prerequisites

SGLang's engine-internal tracing requires the `opentelemetry` packages. These are declared as SGLang's `[tracing]` extra. Install them into your Dynamo environment:

```bash
uv pip install opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp opentelemetry-exporter-otlp-proto-grpc
```

Without these packages, Dynamo-side spans (frontend, handler) will still work, but SGLang's internal engine spans will not be emitted and you will see a warning: `"Tracing is disabled because the packages cannot be imported."`

### How Trace Propagation Works

```
Frontend (Rust)
  creates span, embeds trace_id + span_id in Context
    |
    v
Dynamo RPC (NATS transport)
  Context serialized with trace_id, span_id
    |
    v
SGLang Handler (Python)
  handler_base.py:_get_trace_header(context)
  builds W3C traceparent: "00-{trace_id}-{span_id}-01"
    |
    v
sgl.Engine.async_generate(
    ...,
    rid=trace_id,                        # request ID = trace ID
    external_trace_header=traceparent    # W3C header for SGLang internal spans
)
    |
    v
SGLang Engine (internal spans attached to same trace)
```

Key implementation files:
- `components/src/dynamo/common/utils/otel_tracing.py` - W3C `traceparent` header builder
- `components/src/dynamo/sglang/request_handlers/handler_base.py:71-84` - Extracts trace context from Dynamo `Context` object
- `components/src/dynamo/sglang/request_handlers/llm/decode_handler.py` - Passes `external_trace_header` and `rid=trace_id` to `engine.async_generate()`

### Environment Variables

| Variable | Description | Default | Example |
|----------|-------------|---------|---------|
| `DYN_LOGGING_JSONL` | Enable JSONL logging (required for tracing) | `false` | `true` |
| `OTEL_EXPORT_ENABLED` | Enable OTLP trace export | `false` | `true` |
| `OTEL_EXPORTER_OTLP_TRACES_ENDPOINT` | OTLP gRPC endpoint for Tempo | `http://localhost:4317` | `http://tempo:4317` |
| `OTEL_SERVICE_NAME` | Service name shown in Grafana Tempo | `dynamo` | `dynamo-worker-decode` |

### SGLang-Specific Flags

| Flag | Description |
|------|-------------|
| `--enable-trace` | Enable W3C trace header propagation into SGLang engine |
| `--otlp-traces-endpoint` | OTLP gRPC endpoint for SGLang's internal trace export (bare `host:port` format, e.g. `localhost:4317`) |

Both flags are required for end-to-end tracing through the SGLang engine. Without `--enable-trace`, the Dynamo handler still creates spans, but SGLang's internal engine spans will not be linked.

### Launch with Tracing

The disaggregated launch script supports `--enable-otel` to enable tracing across all components:

```bash
# Start observability stack first
docker compose -f deploy/docker-compose.yml up -d
docker compose -f deploy/docker-observability.yml up -d

# Launch SGLang disaggregated with tracing
cd examples/backends/sglang/launch
./disagg.sh --enable-otel
```

Or manually for an aggregated deployment:

```bash
export DYN_LOGGING_JSONL=true
export OTEL_EXPORT_ENABLED=true
export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=http://localhost:4317

# Frontend
OTEL_SERVICE_NAME=dynamo-frontend python -m dynamo.frontend &

# SGLang worker with tracing
OTEL_SERVICE_NAME=dynamo-worker-sglang \
DYN_SYSTEM_PORT=8081 \
python -m dynamo.sglang \
  --model Qwen/Qwen3-0.6B \
  --enable-metrics \
  --enable-trace \
  --otlp-traces-endpoint localhost:4317
```

### What You'll See in Traces

With tracing enabled, each inference request produces a single end-to-end trace spanning the full request lifecycle:

- **Frontend `http-request` span** - Root span from the HTTP service, includes method/uri/trace_id
- **KV Router spans** - `kv_router.route_request`, `kv_router.select_worker`, `kv_router.compute_block_hashes`, `kv_router.find_matches`, `kv_router.compute_seq_hashes`, `kv_router.schedule`
- **Worker `handle_payload` span** - The Dynamo RPC handler on the worker side, with component/endpoint/namespace labels
- **SGLang engine spans** - `Req <id>`, `Scheduler`, `Tokenizer`, `request_process`, `prefill_forward`, `decode_loop`, `Bootstrap Room` (for disagg)
- **Semantic conventions** - `gen_ai.usage.prompt_tokens`, `gen_ai.usage.completion_tokens`, `gen_ai.latency.time_to_first_token`, etc.

Example trace tree for a KV-routed request:

```
dynamo-frontend: http-request (root)
  dynamo-frontend: kv_router.route_request
    dynamo-frontend: kv_router.select_worker
      kv_router.compute_block_hashes
      kv_router.find_matches
      kv_router.compute_seq_hashes
      kv_router.schedule
    dynamo-worker-1: handle_payload
      sglang: Bootstrap Room 0x0
        sglang: Req <trace-id-prefix>
          sglang: Scheduler [TP 0]
            request_process
            prefill_forward
            decode_loop (repeated per token)
          sglang: Tokenizer
            tokenize
            dispatch
```

![End-to-end trace in Grafana Tempo showing frontend, KV router, worker, and SGLang engine spans](../../assets/img/sglang-trace.png)

### Viewing Traces

1. Open Grafana at `http://localhost:3000` (username: `dynamo`, password: `dynamo`)
2. Navigate to **Explore** (compass icon)
3. Select **Tempo** as the data source
4. Use the **Search** tab:
   - Filter by **Service Name** (e.g., `dynamo-frontend`, `dynamo-worker-1`, `sglang`)
   - Filter by **Span Name** (e.g., `http-request`, `handle_payload`, `Req *`, `decode_loop`)
   - Filter by **Tags** (e.g., `rid=<trace-id>`, `gen_ai.response.model=Qwen/Qwen3-0.6B`)
5. Click a trace to view the flame graph spanning frontend -> router -> worker -> engine

Send a request with `x-request-id` for easy lookup:

```bash
curl -H 'Content-Type: application/json' \
  -H 'x-request-id: my-trace-001' \
  -d '{"model": "Qwen/Qwen3-0.6B", "max_completion_tokens": 50,
       "messages": [{"role": "user", "content": "Explain why Roger Federer is considered one of the greatest tennis players of all time"}]}' \
  http://localhost:8000/v1/chat/completions
```

For more details on the Tempo/Grafana tracing infrastructure, see the [Dynamo Tracing Guide](../../observability/tracing.md).

---

## SGLang Grafana Dashboard

Dynamo ships a pre-provisioned Grafana dashboard for SGLang at `deploy/observability/grafana_dashboards/sglang.json`. It is automatically loaded when the observability stack starts.

### Dashboard Panels

The dashboard is organized into five sections:

| Section | Panels | What to Watch |
|---------|--------|---------------|
| **Request Latency** | E2E Request Latency, Time-To-First-Token, Inter-Token Latency | Tail latency regressions, TTFT spikes during prefill pressure |
| **Throughput & Queue** | Token Generation Throughput (tok/s), Running & Queued Requests, Request Rate | Throughput saturation, queue depth growth |
| **Cache & PIN** | Cache Hit Rate, Active PIN Count, Retractions | KV cache reuse efficiency, PIN pressure from disagg routing |
| **Memory Pressure** | GPU KV Cache Usage %, Host (CPU) KV Cache Usage %, Eviction & Load-back Rate | OOM risk, HiCache offload activity |
| **HiCache Latency** | Eviction P99 Latency, Load-back P99 Latency | PCIe/NVLink bottlenecks in KV offload path |

### Accessing the Dashboard

1. Open Grafana at `http://localhost:3000`
2. Login with `dynamo` / `dynamo`
3. Click **Dashboards** in the left sidebar
4. Select **SGLang Engine**

Other available dashboards:
- **Dynamo Dashboard** (`dynamo.json`) - Frontend and component metrics
- **DCGM Metrics** (`dcgm-metrics.json`) - GPU utilization, memory, power
- **KVBM** (`kvbm.json`) - KV block manager metrics
- **Disagg Dashboard** (`disagg-dashboard.json`) - Disaggregated serving metrics

---

## Exposing on a Remote VM

When developing on a remote VM (cloud instance, bare metal, etc.), the observability ports are only bound to `localhost` inside the VM. You have two options to access them.

### Option 1: SSH Port Forwarding (Recommended)

Forward the relevant ports through your SSH connection. No firewall changes needed, traffic is encrypted.

```bash
# Forward Grafana (3000), Prometheus (9090), and Tempo (3200)
ssh -L 3000:localhost:3000 \
    -L 9090:localhost:9090 \
    -L 3200:localhost:3200 \
    user@your-vm-ip
```

Then open `http://localhost:3000` in your local browser.

For a long-running tunnel in the background:

```bash
ssh -fN \
    -L 3000:localhost:3000 \
    -L 9090:localhost:9090 \
    -L 3200:localhost:3200 \
    user@your-vm-ip
```

### Option 2: Firewall Rules

Open the ports directly. Only use this on trusted networks.

```bash
# Ubuntu/Debian
sudo ufw allow 3000/tcp   # Grafana
sudo ufw allow 9090/tcp   # Prometheus

# Or for cloud VMs, add inbound rules in your security group for ports 3000, 9090
```

Then access `http://<vm-ip>:3000` directly.

### Headless / Agent Access

For CI pipelines, AI coding agents, or headless workflows where no browser is available, you can query Grafana and Prometheus directly via their APIs:

```bash
# Query Prometheus for SGLang token throughput
curl -s 'http://localhost:9090/api/v1/query?query=rate(sglang:generation_tokens_total[1m])' | python3 -m json.tool

# Query Prometheus for GPU KV cache usage
curl -s 'http://localhost:9090/api/v1/query?query=dynamo_component_gpu_cache_usage_percent' | python3 -m json.tool

# List available Grafana dashboards
curl -s -u dynamo:dynamo http://localhost:3000/api/search | python3 -m json.tool

# Get the SGLang dashboard by title
curl -s -u dynamo:dynamo 'http://localhost:3000/api/search?query=SGLang' | python3 -m json.tool

# Fetch a specific dashboard by UID
curl -s -u dynamo:dynamo http://localhost:3000/api/dashboards/uid/<dashboard-uid> | python3 -m json.tool

# Snapshot current metrics via Prometheus range query (last hour)
START=$(date -u -d '1 hour ago' +%Y-%m-%dT%H:%M:%SZ)
END=$(date -u +%Y-%m-%dT%H:%M:%SZ)
curl -s "http://localhost:9090/api/v1/query_range?query=sglang:cache_hit_rate&start=${START}&end=${END}&step=15s"
```

This is useful for automated benchmarking pipelines where you want to capture metrics programmatically alongside performance results.

---

## Related Documentation

### SGLang Metrics
- [Official SGLang Production Metrics](https://docs.sglang.io/references/production_metrics.html)
- [SGLang GitHub - Metrics Collector](https://github.com/sgl-project/sglang/blob/v0.5.9/python/sglang/srt/metrics/collector.py)

### Dynamo Observability
- [Dynamo Metrics Guide](../../observability/metrics.md) - Complete documentation on Dynamo runtime metrics
- [Dynamo Tracing Guide](../../observability/tracing.md) - Distributed tracing with OpenTelemetry and Tempo
- [Prometheus and Grafana Setup](../../observability/prometheus-grafana.md) - Visualization setup instructions
- Dynamo runtime metrics (prefixed with `dynamo_*`) are available at the same `/metrics` endpoint alongside SGLang metrics
  - Implementation: `lib/runtime/src/metrics.rs` (Rust runtime metrics)
  - Metric names: `lib/runtime/src/metrics/prometheus_names.rs` (metric name constants)
  - Integration code: `components/src/dynamo/common/utils/prometheus.py` - Prometheus utilities and callback registration
