Production Metrics#

SGLang exposes the following metrics via Prometheus. The metrics are namespaced by $name (the model name).

An example of the monitoring dashboard is available in examples/monitoring/grafana.json.

Here is an example of the metrics:

$ curl http://localhost:30000/metrics
# 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"} 8.128902e+06
# 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"} 7.557572e+06
# HELP sglang:token_usage The token usage
# TYPE sglang:token_usage gauge
sglang:token_usage{model_name="meta-llama/Llama-3.1-8B-Instruct"} 0.28
# 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"} 0.007507552643049313
# HELP sglang:time_to_first_token_seconds Histogram of time to first token in seconds.
# TYPE sglang:time_to_first_token_seconds histogram
sglang:time_to_first_token_seconds_sum{model_name="meta-llama/Llama-3.1-8B-Instruct"} 2.3518979474117756e+06
sglang:time_to_first_token_seconds_bucket{le="0.001",model_name="meta-llama/Llama-3.1-8B-Instruct"} 0.0
sglang:time_to_first_token_seconds_bucket{le="0.005",model_name="meta-llama/Llama-3.1-8B-Instruct"} 0.0
sglang:time_to_first_token_seconds_bucket{le="0.01",model_name="meta-llama/Llama-3.1-8B-Instruct"} 0.0
sglang:time_to_first_token_seconds_bucket{le="0.02",model_name="meta-llama/Llama-3.1-8B-Instruct"} 0.0
sglang:time_to_first_token_seconds_bucket{le="0.04",model_name="meta-llama/Llama-3.1-8B-Instruct"} 1.0
sglang:time_to_first_token_seconds_bucket{le="0.06",model_name="meta-llama/Llama-3.1-8B-Instruct"} 3.0
sglang:time_to_first_token_seconds_bucket{le="0.08",model_name="meta-llama/Llama-3.1-8B-Instruct"} 6.0
sglang:time_to_first_token_seconds_bucket{le="0.1",model_name="meta-llama/Llama-3.1-8B-Instruct"} 6.0
sglang:time_to_first_token_seconds_bucket{le="0.25",model_name="meta-llama/Llama-3.1-8B-Instruct"} 6.0
sglang:time_to_first_token_seconds_bucket{le="0.5",model_name="meta-llama/Llama-3.1-8B-Instruct"} 6.0
sglang:time_to_first_token_seconds_bucket{le="0.75",model_name="meta-llama/Llama-3.1-8B-Instruct"} 6.0
sglang:time_to_first_token_seconds_bucket{le="1.0",model_name="meta-llama/Llama-3.1-8B-Instruct"} 27.0
sglang:time_to_first_token_seconds_bucket{le="2.5",model_name="meta-llama/Llama-3.1-8B-Instruct"} 140.0
sglang:time_to_first_token_seconds_bucket{le="5.0",model_name="meta-llama/Llama-3.1-8B-Instruct"} 314.0
sglang:time_to_first_token_seconds_bucket{le="7.5",model_name="meta-llama/Llama-3.1-8B-Instruct"} 941.0
sglang:time_to_first_token_seconds_bucket{le="10.0",model_name="meta-llama/Llama-3.1-8B-Instruct"} 1330.0
sglang:time_to_first_token_seconds_bucket{le="15.0",model_name="meta-llama/Llama-3.1-8B-Instruct"} 1970.0
sglang:time_to_first_token_seconds_bucket{le="20.0",model_name="meta-llama/Llama-3.1-8B-Instruct"} 2326.0
sglang:time_to_first_token_seconds_bucket{le="25.0",model_name="meta-llama/Llama-3.1-8B-Instruct"} 2417.0
sglang:time_to_first_token_seconds_bucket{le="30.0",model_name="meta-llama/Llama-3.1-8B-Instruct"} 2513.0
sglang:time_to_first_token_seconds_bucket{le="+Inf",model_name="meta-llama/Llama-3.1-8B-Instruct"} 11008.0
sglang:time_to_first_token_seconds_count{model_name="meta-llama/Llama-3.1-8B-Instruct"} 11008.0
# HELP sglang:e2e_request_latency_seconds Histogram of End-to-end request latency in seconds
# TYPE sglang:e2e_request_latency_seconds histogram
sglang:e2e_request_latency_seconds_sum{model_name="meta-llama/Llama-3.1-8B-Instruct"} 3.116093850019932e+06
sglang:e2e_request_latency_seconds_bucket{le="0.3",model_name="meta-llama/Llama-3.1-8B-Instruct"} 0.0
sglang:e2e_request_latency_seconds_bucket{le="0.5",model_name="meta-llama/Llama-3.1-8B-Instruct"} 6.0
sglang:e2e_request_latency_seconds_bucket{le="0.8",model_name="meta-llama/Llama-3.1-8B-Instruct"} 6.0
sglang:e2e_request_latency_seconds_bucket{le="1.0",model_name="meta-llama/Llama-3.1-8B-Instruct"} 6.0
sglang:e2e_request_latency_seconds_bucket{le="1.5",model_name="meta-llama/Llama-3.1-8B-Instruct"} 6.0
sglang:e2e_request_latency_seconds_bucket{le="2.0",model_name="meta-llama/Llama-3.1-8B-Instruct"} 6.0
sglang:e2e_request_latency_seconds_bucket{le="2.5",model_name="meta-llama/Llama-3.1-8B-Instruct"} 6.0
sglang:e2e_request_latency_seconds_bucket{le="5.0",model_name="meta-llama/Llama-3.1-8B-Instruct"} 7.0
sglang:e2e_request_latency_seconds_bucket{le="10.0",model_name="meta-llama/Llama-3.1-8B-Instruct"} 10.0
sglang:e2e_request_latency_seconds_bucket{le="15.0",model_name="meta-llama/Llama-3.1-8B-Instruct"} 11.0
sglang:e2e_request_latency_seconds_bucket{le="20.0",model_name="meta-llama/Llama-3.1-8B-Instruct"} 14.0
sglang:e2e_request_latency_seconds_bucket{le="30.0",model_name="meta-llama/Llama-3.1-8B-Instruct"} 247.0
sglang:e2e_request_latency_seconds_bucket{le="40.0",model_name="meta-llama/Llama-3.1-8B-Instruct"} 486.0
sglang:e2e_request_latency_seconds_bucket{le="50.0",model_name="meta-llama/Llama-3.1-8B-Instruct"} 845.0
sglang:e2e_request_latency_seconds_bucket{le="60.0",model_name="meta-llama/Llama-3.1-8B-Instruct"} 1513.0
sglang:e2e_request_latency_seconds_bucket{le="+Inf",model_name="meta-llama/Llama-3.1-8B-Instruct"} 11228.0
sglang:e2e_request_latency_seconds_count{model_name="meta-llama/Llama-3.1-8B-Instruct"} 11228.0
# HELP sglang:time_per_output_token_seconds Histogram of time per output token in seconds.
# TYPE sglang:time_per_output_token_seconds histogram
sglang:time_per_output_token_seconds_sum{model_name="meta-llama/Llama-3.1-8B-Instruct"} 866964.5791549598
sglang:time_per_output_token_seconds_bucket{le="0.005",model_name="meta-llama/Llama-3.1-8B-Instruct"} 1.0
sglang:time_per_output_token_seconds_bucket{le="0.01",model_name="meta-llama/Llama-3.1-8B-Instruct"} 73.0
sglang:time_per_output_token_seconds_bucket{le="0.015",model_name="meta-llama/Llama-3.1-8B-Instruct"} 382.0
sglang:time_per_output_token_seconds_bucket{le="0.02",model_name="meta-llama/Llama-3.1-8B-Instruct"} 593.0
sglang:time_per_output_token_seconds_bucket{le="0.025",model_name="meta-llama/Llama-3.1-8B-Instruct"} 855.0
sglang:time_per_output_token_seconds_bucket{le="0.03",model_name="meta-llama/Llama-3.1-8B-Instruct"} 1035.0
sglang:time_per_output_token_seconds_bucket{le="0.04",model_name="meta-llama/Llama-3.1-8B-Instruct"} 1815.0
sglang:time_per_output_token_seconds_bucket{le="0.05",model_name="meta-llama/Llama-3.1-8B-Instruct"} 11685.0
sglang:time_per_output_token_seconds_bucket{le="0.075",model_name="meta-llama/Llama-3.1-8B-Instruct"} 433413.0
sglang:time_per_output_token_seconds_bucket{le="0.1",model_name="meta-llama/Llama-3.1-8B-Instruct"} 4.950195e+06
sglang:time_per_output_token_seconds_bucket{le="0.15",model_name="meta-llama/Llama-3.1-8B-Instruct"} 7.039435e+06
sglang:time_per_output_token_seconds_bucket{le="0.2",model_name="meta-llama/Llama-3.1-8B-Instruct"} 7.171662e+06
sglang:time_per_output_token_seconds_bucket{le="0.3",model_name="meta-llama/Llama-3.1-8B-Instruct"} 7.266055e+06
sglang:time_per_output_token_seconds_bucket{le="0.4",model_name="meta-llama/Llama-3.1-8B-Instruct"} 7.296752e+06
sglang:time_per_output_token_seconds_bucket{le="0.5",model_name="meta-llama/Llama-3.1-8B-Instruct"} 7.312226e+06
sglang:time_per_output_token_seconds_bucket{le="0.75",model_name="meta-llama/Llama-3.1-8B-Instruct"} 7.339675e+06
sglang:time_per_output_token_seconds_bucket{le="1.0",model_name="meta-llama/Llama-3.1-8B-Instruct"} 7.357747e+06
sglang:time_per_output_token_seconds_bucket{le="2.5",model_name="meta-llama/Llama-3.1-8B-Instruct"} 7.389414e+06
sglang:time_per_output_token_seconds_bucket{le="+Inf",model_name="meta-llama/Llama-3.1-8B-Instruct"} 7.400757e+06
sglang:time_per_output_token_seconds_count{model_name="meta-llama/Llama-3.1-8B-Instruct"} 7.400757e+06
# HELP sglang:func_latency_seconds Function latency in seconds
# TYPE sglang:func_latency_seconds histogram
sglang:func_latency_seconds_sum{name="generate_request"} 4.514771912145079
sglang:func_latency_seconds_bucket{le="0.05",name="generate_request"} 14006.0
sglang:func_latency_seconds_bucket{le="0.07500000000000001",name="generate_request"} 14006.0
sglang:func_latency_seconds_bucket{le="0.1125",name="generate_request"} 14006.0
sglang:func_latency_seconds_bucket{le="0.16875",name="generate_request"} 14006.0
sglang:func_latency_seconds_bucket{le="0.253125",name="generate_request"} 14006.0
sglang:func_latency_seconds_bucket{le="0.3796875",name="generate_request"} 14006.0
sglang:func_latency_seconds_bucket{le="0.56953125",name="generate_request"} 14006.0
sglang:func_latency_seconds_bucket{le="0.8542968750000001",name="generate_request"} 14006.0
sglang:func_latency_seconds_bucket{le="1.2814453125",name="generate_request"} 14006.0
sglang:func_latency_seconds_bucket{le="1.9221679687500002",name="generate_request"} 14006.0
sglang:func_latency_seconds_bucket{le="2.8832519531250003",name="generate_request"} 14006.0
sglang:func_latency_seconds_bucket{le="4.3248779296875",name="generate_request"} 14007.0
sglang:func_latency_seconds_bucket{le="6.487316894531251",name="generate_request"} 14007.0
sglang:func_latency_seconds_bucket{le="9.730975341796876",name="generate_request"} 14007.0
sglang:func_latency_seconds_bucket{le="14.596463012695313",name="generate_request"} 14007.0
sglang:func_latency_seconds_bucket{le="21.89469451904297",name="generate_request"} 14007.0
sglang:func_latency_seconds_bucket{le="32.84204177856446",name="generate_request"} 14007.0
sglang:func_latency_seconds_bucket{le="49.26306266784668",name="generate_request"} 14007.0
sglang:func_latency_seconds_bucket{le="+Inf",name="generate_request"} 14007.0
sglang:func_latency_seconds_count{name="generate_request"} 14007.0
# HELP sglang:num_running_reqs The number of running requests
# TYPE sglang:num_running_reqs gauge
sglang:num_running_reqs{model_name="meta-llama/Llama-3.1-8B-Instruct"} 162.0
# HELP sglang:num_used_tokens The number of used tokens
# TYPE sglang:num_used_tokens gauge
sglang:num_used_tokens{model_name="meta-llama/Llama-3.1-8B-Instruct"} 123859.0
# HELP sglang:gen_throughput The generate throughput (token/s)
# TYPE sglang:gen_throughput gauge
sglang:gen_throughput{model_name="meta-llama/Llama-3.1-8B-Instruct"} 86.50814177726902
# HELP sglang:num_queue_reqs The number of requests in the waiting queue
# TYPE sglang:num_queue_reqs gauge
sglang:num_queue_reqs{model_name="meta-llama/Llama-3.1-8B-Instruct"} 2826.0

Setup Guide#

To setup a monitoring dashboard, you can use the following docker compose file: examples/monitoring/docker-compose.yaml.

Assume you have sglang server running at localhost:30000, to start the server, ensure you have --enable-metrics flag enabled:

python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct \
--port 30000 --host 0.0.0.0 --enable-metrics

To start the monitoring dashboard (prometheus + grafana), cd to examples/monitoring and run:

docker compose -f compose.yaml -p monitoring up

Then you can access the Grafana dashboard at http://localhost:3000.

Grafana Dashboard#

In a new Grafana setup, ensure that you have the Prometheus data source enabled. To check that, go to http://localhost:3000/connections/datasources and ensure that Prometheus is enabled.

If not, click Add data source -> Prometheus, set Prometheus URL to http://localhost:9090, and click Save & Test.

To import the Grafana dashboard, click + -> Import -> Upload JSON file -> Upload and select grafana.json.

Troubleshooting#

Check if the variables are created#

The example dashboard assume you have the following variables avaliable:

model_name (name: model_name, label: model name, Data source: Prometheus, Type: Label values)
instance (name: instance, label: instance, Data source: Prometheus, Type: Label values)

If you don’t have these variables, you can create them manually.

To create a variable, go to dashboard settings, Variables -> New variable.

You should be able to see the preview the values (e.g. meta-llama/Llama-3.1-8B-Instruct for model_name).

Check if the metrics are being collected#

Run python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompts 3000 --random-input 1024 --random-output 1024 --random-range-ratio 0.5 to generate some requests.

Then you should be able to see the metrics in the Grafana dashboard.

Production Metrics

Contents