Overview #
vLLM is an inference server that you run yourself. It serves models over an OpenAI-compatible API. It reports its own health in two ways: Prometheus metrics on an HTTP endpoint, and OpenTelemetry traces for each request. This guide sends both to SigNoz.
The metrics answer questions that only the server can answer. How many tokens per second is the GPU producing? How full is the key-value cache, the memory pool that holds attention state for active requests? How many requests wait in the queue?
Prerequisites #
- A vLLM server that you can restart
- An OpenTelemetry Collector that can reach the vLLM host. See Install the OpenTelemetry Collector
- An instance of SigNoz (Cloud orSelf-Hosted )
Send vLLM metrics to SigNoz #
Step 1: Confirm the metrics endpoint
vLLM serves Prometheus metrics at /metrics on the same port as the API, and it
needs no flag to turn them on. Confirm the endpoint responds:
curl -s http://localhost:8000/metrics | head
If you changed the port, use that port instead of 8000.
Step 2: Add a scrape job to the Collector
Append this scrape job to your existing otel-collector-config.yaml. Do not
replace the whole file.
otel-collector-config.yaml
receivers:
prometheus:
config:
scrape_configs:
- job_name: vllm
scrape_interval: 30s
static_configs:
- targets: ['<vllm-host>:8000']
metric_relabel_configs:
- source_labels: [__name__]
regex: '.*_created'
action: drop
Verify these values:
<vllm-host>: The hostname or IP address of the machine that runs vLLM.
Keep the metric_relabel_configs block. vLLM exposes a _created series next to
every counter and histogram, recording when the metric was first observed. On a
default server these are 111 of the 405 exposed series. Dropping them removes
about a quarter of the ingested data and loses no signal.
Then add the receiver to your metrics pipeline:
otel-collector-config.yaml
service:
pipelines:
metrics:
receivers: [prometheus] # append prometheus to your existing receivers list
processors: [batch]
exporters: [otlphttp]
Step 3: Restart the Collector
Restart the Collector so that it loads the new scrape job, then watch its logs for scrape errors.
sudo systemctl restart otelcol-contrib
sudo journalctl -u otelcol-contrib -f
docker compose up -d
docker compose logs -f
kubectl rollout restart deployment/<release>-k8s-infra-otel-deployment -n <collector-namespace>
kubectl logs -f deployment/<release>-k8s-infra-otel-deployment -n <collector-namespace>
The SigNoz K8s Infra chart installs two Collectors. Add the scrape job to the
Deployment, which handles cluster-wide scraping, rather than to the per-node
<release>-k8s-infra-otel-agent DaemonSet. Replace <release> with your Helm
release name and <collector-namespace> with the namespace that runs it. If you
run your own Collector under a different name, use that name instead.
The MSI installs the Collector as a Windows service named otelcol-contrib, with
OpenTelemetry Collector as its display name. Run this in PowerShell:
Restart-Service -Name otelcol-contrib
Get-EventLog -LogName Application -Source otelcol-contrib -Newest 20
Send a request to vLLM so that the server has something to report.
## Export Request Traces (Optional) #
Metrics tell you how the server behaves as a whole. Traces show where time went inside a single request. Traces go straight from vLLM to SigNoz and do not pass through the Collector, so the scrape job above is unaffected.
Set three environment variables, then add one flag to the launch command:
export OTEL_SERVICE_NAME=vllm
export OTEL_EXPORTER_OTLP_TRACES_PROTOCOL=http/protobuf
export OTEL_EXPORTER_OTLP_TRACES_HEADERS="signoz-ingestion-key=<your-ingestion-key>"
vllm serve <your-model> \
--host 0.0.0.0 \
--port 8000 \
--otlp-traces-endpoint https://ingest.<region>.signoz.cloud:443/v1/traces
Verify these values:
<your-model>: The model you serve, for exampleQwen/Qwen2.5-7B-Instruct.<region>: YourSigNoz Cloud region .<your-ingestion-key>: Your SigNozingestion key .
You must set the protocol and header variables. vLLM sends no authentication header of its own, and it defaults to gRPC.
Restart the server after you change the launch command.
Validate #
Open Metrics > Metrics Explorer and filter with service.name = 'vllm'.
The Prometheus receiver takes that value from the job_name in your scrape job.
Metrics appear within one scrape interval.
If you enabled traces, open Traces and filter on the value you set for
OTEL_SERVICE_NAME. Each request produces one span named llm_request.
## Metrics Reference #
| Metric | Type | What it tells you |
|---|---|---|
vllm:generation_tokens_total |
Counter | Output tokens produced. Apply a rate to get tokens per second. |
vllm:prompt_tokens_total |
Counter | Prefill tokens processed. |
vllm:prompt_tokens_cached_total |
Counter | Prompt tokens served from cache rather than recomputed. |
vllm:time_to_first_token_seconds |
Histogram | Delay before the first output token. Drives how fast a streaming response feels. |
vllm:inter_token_latency_seconds |
Histogram | Delay between output tokens after the first one. |
vllm:e2e_request_latency_seconds |
Histogram | Total request duration. |
vllm:request_queue_time_seconds |
Histogram | Time a request spent waiting before it ran. |
vllm:num_requests_running |
Gauge | Requests in the current execution batch. |
vllm:num_requests_waiting |
Gauge | Requests waiting to start. A number above zero means the server is saturated. |
vllm:kv_cache_usage_perc |
Gauge | Fraction of the key-value cache in use, from 0 to 1. |
vllm:prefix_cache_queries_total |
Counter | Prompt tokens looked up in the prefix cache. |
vllm:prefix_cache_hits_total |
Counter | Prompt tokens found in the prefix cache. |
vllm:num_preemptions_total |
Counter | Requests the scheduler preempted and re-ran. |
Every metric carries model_name and engine labels. Group by model_name when
one server hosts several models.
vLLM has no ready-made throughput gauge, so read tokens per second as a rate over
vllm:generation_tokens_total. It also has no prefix cache hit rate metric.
Divide vllm:prefix_cache_hits_total by vllm:prefix_cache_queries_total to get
one.
Metric names vary between vLLM releases. The names above come from v0.29.0. For the full list, see the vLLM metrics reference.
## Troubleshooting #
The metrics endpoint returns 404
Likely cause: the request went to the wrong port, or the server runs behind a
proxy that does not forward /metrics.
Fix: use the port vLLM serves the API on, which is 8000 by default.
Verify: curl -s http://localhost:8000/metrics | head prints lines that start
with # HELP.
The Collector logs a connection refused error
Likely cause: vLLM is bound to 127.0.0.1, so nothing outside the host can reach
it.
Fix: start the server with --host 0.0.0.0, and open port 8000 to the Collector
host in your firewall.
Verify: run curl -s http://<vllm-host>:8000/metrics | head from the machine that
runs the Collector.
Metrics arrive but every value is zero
Likely cause: the server has served no requests since it started. Counters and gauges stay at zero until traffic arrives.
Fix: send a request to the server.
Verify: vllm:prompt_tokens_total rises in Metrics Explorer.
Traces do not appear
Likely cause: the server is still exporting over gRPC, which cannot reach SigNoz Cloud.
Fix: set OTEL_EXPORTER_OTLP_TRACES_PROTOCOL=http/protobuf and
OTEL_EXPORTER_OTLP_TRACES_HEADERS in the environment of the vLLM process, then
restart it.
Verify: spans named llm_request appear in the Traces explorer.
Next steps #
- vLLM dashboard template
- Set up alerts on queue depth and time to first token
- LLM observability for tracing the application that calls this server
Get Help #
If you need help with the steps in this topic, please reach out to us on SigNoz Community Slack. If you are a SigNoz Cloud user, please use in product chat support located at the bottom right corner of your SigNoz instance or contact us at cloud-support@signoz.io.