Documentation Index
Fetch the complete documentation index at: https://docs.openlit.io/llms.txt
Use this file to discover all available pages before exploring further.
1. Configure OpenTelemetry Collector
VictoriaMetrics stack requires an OpenTelemetry Collector to route metrics, logs and traces to VictoriaMetrics components. Install OpenTelemetry Collector (if not already running) For detailed installation instructions, refer to the OpenTelemetry Collector Documentation. Configure the Collector- Configure OTLP Receiver: Set up receiver on
0.0.0.0:4318for HTTP and0.0.0.0:4317for gRPC. - Define Exporters:
otlphttpfor metrics, logs and traces. - Assign to Pipelines: Route metrics, logs and traces to appropriate backends.
Complete Collector Configuration
Complete Collector Configuration
receivers:
otlp:
protocols:
grpc:
endpoint: 0.0.0.0:4317
http:
endpoint: 0.0.0.0:4318
exporters:
otlphttp/victoriametrics:
endpoint: "http://<VictoriaMetrics>/opentelemetry"
otlphttp/victorialogs:
endpoint: "http://<VictoriaLogs>/insert/opentelemetry"
otlphttp/victoriatraces:
endpoint: "http://<VictoriaTraces>/insert/opentelemetry"
service:
pipelines:
traces: { receivers: [ otlp ], exporters: [ otlphttp/victoriatraces ] }
metrics: { receivers: [ otlp ], exporters: [ otlphttp/victoriametrics ] }
logs: { receivers: [ otlp ], exporters: [ otlphttp/victorialogs ] }
2. Instrument your application
- SDK
- CLI
For direct integration into your Python applications:Replace:Set these environment variables:Replace:Refer to the OpenLIT Python SDK repository for more advanced configurations and use cases.
- Function Arguments
- Environment Variables
import openlit
openlit.init(
otlp_endpoint="YOUR_OTELCOL_URL:4318"
)
YOUR_OTELCOL_URL:4318with the HTTP endpoint of your OpenTelemetry Collector.- Example:
http://127.0.0.1:4318(for local collector) - Example:
http://otel-collector.monitoring.svc.cluster.local:4318(for Kubernetes)
- Example:
import openlit
openlit.init()
export OTEL_EXPORTER_OTLP_ENDPOINT="YOUR_OTELCOL_URL:4318"
YOUR_OTELCOL_URL:4318with the HTTP endpoint of your OpenTelemetry Collector.- Example:
http://127.0.0.1:4318(for local collector) - Example:
http://otel-collector.monitoring.svc.cluster.local:4318(for Kubernetes)
- Example:
For zero-code auto-instrumentation via command line:Replace:Replace:Refer to the OpenLIT Python SDK repository for more advanced configurations and use cases.
- CLI Arguments
- Environment Variables
# Using CLI arguments
openlit-instrument \
--otlp-endpoint "YOUR_OTELCOL_URL:4318" \
--service-name "my-ai-service" \
--deployment-environment "production" \
python app.py
YOUR_OTELCOL_URL:4318with the HTTP endpoint of your OpenTelemetry Collector.- Example:
http://127.0.0.1:4318(for local collector) - Example:
http://otel-collector.monitoring.svc.cluster.local:4318(for Kubernetes)
- Example:
# Set environment variables (takes precedence over CLI args)
export OTEL_EXPORTER_OTLP_ENDPOINT="YOUR_OTELCOL_URL:4318"
export OTEL_SERVICE_NAME="my-ai-service"
export OTEL_DEPLOYMENT_ENVIRONMENT="production"
# Run your application
openlit-instrument python app.py
YOUR_OTELCOL_URL:4318with the HTTP endpoint of your OpenTelemetry Collector.- Example:
http://127.0.0.1:4318(for local collector) - Example:
http://otel-collector.monitoring.svc.cluster.local:4318(for Kubernetes)
- Example:
3. Import the pre-built Dashboard
- Log into your Grafana Instance. To install Grafana, refer to the Official documentation.
- Add Data Sources: Make sure Prometheus (VictoriaMetrics), Jaeger (VictoriaTraces) and VictoriaLogs are added as data sources in Grafana. To add a new data source, follow the steps in the Official documentation.
- Import Dashboard: Once datasources are available in Grafana:
- Click Dashboards in the primary menu
- Click New and select Import in the drop-down menu
- Copy the dashboard JSON provided in the accordion below
- Paste the dashboard JSON text directly into the text area
- Click Import
- Save the dashboard
Dashboard
Dashboard
{
"annotations": {
"list": [
{
"builtIn": 1,
"datasource": {
"type": "grafana",
"uid": "-- Grafana --"
},
"enable": true,
"hide": true,
"iconColor": "rgba(0, 211, 255, 1)",
"name": "Annotations & Alerts",
"type": "dashboard"
}
]
},
"editable": true,
"fiscalYearStartMonth": 0,
"graphTooltip": 1,
"id": 0,
"links": [
{
"asDropdown": false,
"icon": "doc",
"includeVars": false,
"keepTime": false,
"tags": [
"GPU",
"AI"
],
"targetBlank": true,
"title": "Documentation",
"tooltip": "Documentation",
"type": "link",
"url": "https://grafana.com/docs/grafana-cloud/monitor-applications/ai-observability/"
},
{
"asDropdown": false,
"icon": "external link",
"includeVars": false,
"keepTime": false,
"tags": [],
"targetBlank": true,
"title": "OpenLIT Github",
"tooltip": "Github",
"type": "link",
"url": "https://github.com/openlit/openlit"
}
],
"panels": [
{
"fieldConfig": {
"defaults": {},
"overrides": []
},
"gridPos": {
"h": 4,
"w": 3,
"x": 0,
"y": 0
},
"id": 61,
"options": {
"code": {
"language": "plaintext",
"showLineNumbers": false,
"showMiniMap": false
},
"content": "<img\n src=\"https://grafana.com/media/docs/grafana-cloud/ai-observability/aio11y-logo.svg\"\n width=\"100%\"\n height=\"100%\">",
"mode": "html"
},
"pluginVersion": "12.2.0",
"title": "",
"transparent": true,
"type": "text"
},
{
"datasource": {
"type": "prometheus",
"uid": "${metrics_datasource}"
},
"description": "This panel displays the current LLM request rate showing the number of GenAI requests per second across all services. This metric helps monitor system load and demand patterns for capacity planning.",
"fieldConfig": {
"defaults": {
"color": {
"fixedColor": "blue",
"mode": "palette-classic-by-name"
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "red",
"value": 0
},
{
"color": "#EAB839",
"value": 10
},
{
"color": "#6ED0E0",
"value": 100
}
]
},
"unit": "none"
},
"overrides": []
},
"gridPos": {
"h": 4,
"w": 5,
"x": 4,
"y": 0
},
"id": 22,
"options": {
"colorMode": "background",
"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
"percentChangeColorMode": "standard",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"showPercentChange": false,
"textMode": "value_and_name",
"wideLayout": true
},
"pluginVersion": "12.2.0",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${metrics_datasource}"
},
"disableTextWrap": false,
"editorMode": "code",
"expr": "sum(increase(gen_ai_client_operation_duration_seconds_count{telemetry_sdk_name=\"openlit\", service_name=~\"$service_name\", deployment_environment=~\"$deployment_environment\"}[$__range]))",
"fullMetaSearch": false,
"includeNullMetadata": true,
"instant": false,
"legendFormat": "Total Requests",
"range": true,
"refId": "A",
"useBackend": false
}
],
"title": "",
"transparent": true,
"type": "stat"
},
{
"datasource": {
"type": "prometheus",
"uid": "${metrics_datasource}"
},
"description": "This panel displays the total number of tokens consumed by GenAI requests, providing a direct measure of usage volume. Monitoring this helps in assessing demand on GenAI services and guiding resource allocation strategies.",
"fieldConfig": {
"defaults": {
"color": {
"fixedColor": "purple",
"mode": "shades"
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": 0
},
{
"color": "red",
"value": 80
}
]
},
"unit": "short"
},
"overrides": []
},
"gridPos": {
"h": 4,
"w": 5,
"x": 9,
"y": 0
},
"id": 3,
"options": {
"colorMode": "background",
"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
"percentChangeColorMode": "standard",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"showPercentChange": false,
"textMode": "auto",
"wideLayout": true
},
"pluginVersion": "12.2.0",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${metrics_datasource}"
},
"disableTextWrap": false,
"editorMode": "code",
"expr": "sum(increase(gen_ai_client_token_usage_sum{telemetry_sdk_name=\"openlit\", service_name=~\"$service_name\", deployment_environment=~\"$deployment_environment\"}[$__range]))",
"fullMetaSearch": false,
"includeNullMetadata": true,
"instant": false,
"legendFormat": "Total Usage Tokens",
"range": true,
"refId": "A",
"useBackend": false
}
],
"title": "",
"transparent": true,
"type": "stat"
},
{
"datasource": {
"type": "prometheus",
"uid": "${metrics_datasource}"
},
"description": "This panel displays the average cost per use of the GenAI models and related services. It provides insights into the cost-effectiveness of interactions with GenAI, helping to identify trends in expense per operation.",
"fieldConfig": {
"defaults": {
"color": {
"fixedColor": "blue",
"mode": "shades"
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": 0
},
{
"color": "#EAB839",
"value": 0.5
},
{
"color": "red",
"value": 1
}
]
},
"unit": "currencyUSD"
},
"overrides": []
},
"gridPos": {
"h": 4,
"w": 5,
"x": 14,
"y": 0
},
"id": 5,
"options": {
"colorMode": "background",
"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
"percentChangeColorMode": "inverted",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"showPercentChange": false,
"textMode": "auto",
"wideLayout": true
},
"pluginVersion": "12.2.0",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${metrics_datasource}"
},
"disableTextWrap": false,
"editorMode": "code",
"expr": "sum(last_over_time(gen_ai_usage_cost_USD_sum{telemetry_sdk_name=\"openlit\", service_name=~\"$service_name\", deployment_environment=~\"$deployment_environment\"}[$__interval])) \n/ \n(sum(last_over_time(gen_ai_usage_cost_USD_count{telemetry_sdk_name=\"openlit\", service_name=~\"$service_name\", deployment_environment=~\"$deployment_environment\"}[$__interval])))",
"fullMetaSearch": false,
"includeNullMetadata": true,
"instant": false,
"legendFormat": "Avg Usage Cost",
"range": true,
"refId": "A",
"useBackend": false
}
],
"title": "",
"transparent": true,
"type": "stat"
},
{
"datasource": {
"type": "prometheus",
"uid": "${metrics_datasource}"
},
"description": "This panel displays the total cost incurred from using GenAI models. It reflects the financial impact of operational activities, offering insights into budgetary allocation and efficiency. Tracking this helps in effective cost management and financial planning for GenAI usage.",
"fieldConfig": {
"defaults": {
"color": {
"fixedColor": "blue",
"mode": "shades"
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": 0
},
{
"color": "red",
"value": 80
}
]
},
"unit": "currencyUSD"
},
"overrides": []
},
"gridPos": {
"h": 4,
"w": 5,
"x": 19,
"y": 0
},
"id": 2,
"options": {
"colorMode": "background",
"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
"percentChangeColorMode": "standard",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"showPercentChange": false,
"textMode": "auto",
"wideLayout": true
},
"pluginVersion": "12.2.0",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${metrics_datasource}"
},
"disableTextWrap": false,
"editorMode": "code",
"expr": "sum(last_over_time(gen_ai_usage_cost_USD_sum{telemetry_sdk_name=\"openlit\", service_name=~\"$service_name\", deployment_environment=~\"$deployment_environment\"}[$__interval]))",
"fullMetaSearch": false,
"includeNullMetadata": true,
"instant": false,
"legendFormat": "Total Usage Cost",
"range": true,
"refId": "A",
"useBackend": false
}
],
"title": "",
"transparent": true,
"type": "stat"
},
{
"datasource": {
"type": "prometheus",
"uid": "${metrics_datasource}"
},
"description": "Hourly cost trends to identify spending patterns",
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"axisBorderShow": false,
"axisCenteredZero": false,
"axisColorMode": "text",
"axisLabel": "",
"axisPlacement": "auto",
"barAlignment": 0,
"barWidthFactor": 0.6,
"drawStyle": "line",
"fillOpacity": 100,
"gradientMode": "opacity",
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
},
"insertNulls": false,
"lineInterpolation": "linear",
"lineWidth": 2,
"pointSize": 5,
"scaleDistribution": {
"type": "linear"
},
"showPoints": "auto",
"showValues": false,
"spanNulls": false,
"stacking": {
"group": "A",
"mode": "none"
},
"thresholdsStyle": {
"mode": "off"
}
},
"mappings": [],
"min": 0,
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": 0
},
{
"color": "red",
"value": 80
}
]
},
"unit": "currencyUSD"
},
"overrides": []
},
"gridPos": {
"h": 7,
"w": 12,
"x": 0,
"y": 4
},
"id": 55,
"options": {
"annotations": {
"multiLane": false
},
"legend": {
"calcs": [],
"displayMode": "list",
"placement": "bottom",
"showLegend": true
},
"tooltip": {
"hideZeros": true,
"mode": "multi",
"sort": "none"
}
},
"pluginVersion": "12.2.0",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${metrics_datasource}"
},
"editorMode": "code",
"expr": "increase(gen_ai_usage_cost_USD_sum{telemetry_sdk_name=\"openlit\", service_name=~\"$service_name\", deployment_environment=~\"$deployment_environment\"}[1h]) / 1000",
"legendFormat": "{{gen_ai_provider_name}} / {{gen_ai_request_model}}",
"range": true,
"refId": "A"
}
],
"title": "Cost Trend Analysis",
"type": "timeseries"
},
{
"datasource": {
"type": "prometheus",
"uid": "${metrics_datasource}"
},
"description": "Percentage of successful GenAI requests over time",
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"axisBorderShow": false,
"axisCenteredZero": false,
"axisColorMode": "text",
"axisLabel": "",
"axisPlacement": "auto",
"barAlignment": 0,
"barWidthFactor": 0.6,
"drawStyle": "line",
"fillOpacity": 100,
"gradientMode": "opacity",
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
},
"insertNulls": false,
"lineInterpolation": "linear",
"lineWidth": 2,
"pointSize": 5,
"scaleDistribution": {
"type": "linear"
},
"showPoints": "auto",
"showValues": false,
"spanNulls": false,
"stacking": {
"group": "A",
"mode": "none"
},
"thresholdsStyle": {
"mode": "off"
}
},
"mappings": [],
"min": 0,
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": 0
},
{
"color": "red",
"value": 80
}
]
},
"unit": "percent"
},
"overrides": []
},
"gridPos": {
"h": 4,
"w": 12,
"x": 12,
"y": 4
},
"id": 56,
"options": {
"annotations": {
"multiLane": false
},
"legend": {
"calcs": [],
"displayMode": "list",
"placement": "bottom",
"showLegend": true
},
"tooltip": {
"hideZeros": false,
"mode": "multi",
"sort": "none"
}
},
"pluginVersion": "12.2.0",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${metrics_datasource}"
},
"editorMode": "code",
"expr": "sum(rate(gen_ai_client_operation_duration_seconds_count{http_status_code!~\"4..|5..\", telemetry_sdk_name=\"openlit\", service_name=~\"$service_name\", deployment_environment=~\"$deployment_environment\"}[$__rate_interval])) / sum(rate(gen_ai_client_operation_duration_seconds_count{telemetry_sdk_name=\"openlit\", service_name=~\"$service_name\", deployment_environment=~\"$deployment_environment\"}[$__rate_interval])) * 100",
"hide": true,
"legendFormat": "Total Requests",
"range": true,
"refId": "A"
},
{
"datasource": {
"type": "prometheus",
"uid": "${metrics_datasource}"
},
"editorMode": "code",
"expr": "( \n sum(increase(gen_ai_client_operation_duration_seconds_count{telemetry_sdk_name=\"openlit\", service_name=~\"$service_name\", deployment_environment=~\"$deployment_environment\"}[$__rate_interval])) \n - \n (sum(increase(gen_ai_client_operation_duration_seconds_count{error_type=~\".+\", telemetry_sdk_name=\"openlit\", service_name=~\"$service_name\", deployment_environment=~\"$deployment_environment\"}[$__rate_interval])) or vector(0)) \n) \n/ \nsum(increase(gen_ai_client_operation_duration_seconds_count{telemetry_sdk_name=\"openlit\", service_name=~\"$service_name\", deployment_environment=~\"$deployment_environment\"}[$__rate_interval])) \n* 100 ",
"hide": true,
"instant": false,
"legendFormat": "__auto",
"range": true,
"refId": "B"
},
{
"datasource": {
"type": "prometheus",
"uid": "${metrics_datasource}"
},
"editorMode": "code",
"expr": "sum(increase(gen_ai_client_operation_duration_seconds_count{telemetry_sdk_name=\"openlit\", error_type=~\".+\", service_name=~\"$service_name\", deployment_environment=~\"$deployment_environment\"}[$__rate_interval])) \n/ \nsum(increase(gen_ai_client_operation_duration_seconds_count{telemetry_sdk_name=\"openlit\", service_name=~\"$service_name\", deployment_environment=~\"$deployment_environment\"}[$__rate_interval])) \n* 100 ",
"instant": false,
"legendFormat": "__auto",
"range": true,
"refId": "C"
},
{
"datasource": {
"type": "prometheus",
"uid": "${metrics_datasource}"
},
"editorMode": "code",
"expr": "histogram_quantile(0.95, sum by(le, gen_ai_provider_name) (rate(gen_ai_client_operation_duration_seconds_bucket{service_name=~\"$service_name\", deployment_environment=~\"$deployment_environment\"}[$__rate_interval]))) ",
"hide": true,
"instant": false,
"legendFormat": "{{gen_ai_provider_name}}",
"range": true,
"refId": "D"
}
],
"title": "Error Rate %",
"type": "timeseries"
},
{
"datasource": {
"type": "prometheus",
"uid": "${metrics_datasource}"
},
"description": "Top 5 GenAI models ranked by total request count",
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"align": "left",
"cellOptions": {
"mode": "basic",
"type": "gauge"
},
"footer": {
"reducers": []
},
"inspect": false
},
"decimals": 0,
"mappings": [],
"min": 0,
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": 0
},
{
"color": "red",
"value": 80
}
]
},
"unit": "short"
},
"overrides": [
{
"matcher": {
"id": "byName",
"options": "Metric"
},
"properties": [
{
"id": "displayName",
"value": "Model"
},
{
"id": "custom.width",
"value": 150
},
{
"id": "custom.cellOptions",
"value": {
"type": "auto"
}
}
]
}
]
},
"gridPos": {
"h": 7,
"w": 12,
"x": 12,
"y": 8
},
"id": 65,
"options": {
"cellHeight": "md",
"frameIndex": 0,
"showHeader": true,
"sortBy": [
{
"desc": true,
"displayName": "Model"
}
]
},
"pluginVersion": "12.2.0",
"targets": [
{
"app": "grafana-assistant-app",
"datasource": {
"type": "prometheus",
"uid": "${metrics_datasource}"
},
"editorMode": "code",
"expr": "topk(5, sum by(gen_ai_request_model) (last_over_time(gen_ai_client_operation_duration_seconds_count{telemetry_sdk_name=\"openlit\", service_name=~\"$service_name\", deployment_environment=~\"$deployment_environment\"}[$__range])))",
"instant": true,
"legendFormat": "{{gen_ai_request_model}}",
"queryType": "instant",
"range": false,
"refId": "A"
}
],
"title": "Top 5 Models by Requests",
"transformations": [
{
"id": "seriesToRows",
"options": {}
},
{
"id": "organize",
"options": {
"excludeByName": {
"Time": true
},
"renameByName": {
"Metric": "Model",
"Value": "Requests"
}
}
}
],
"type": "table"
},
{
"datasource": {
"type": "prometheus",
"uid": "${metrics_datasource}"
},
"description": "This panel displays the current 95th percentile time to first token across GenAI systems. Time to first token is critical for streaming applications and real-time user interactions, representing the delay before content generation begins.",
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": 0
},
{
"color": "yellow",
"value": 2
},
{
"color": "red",
"value": 5
}
]
},
"unit": "s"
},
"overrides": []
},
"gridPos": {
"h": 4,
"w": 12,
"x": 0,
"y": 11
},
"id": 58,
"options": {
"colorMode": "value",
"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
"percentChangeColorMode": "standard",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"showPercentChange": false,
"textMode": "auto",
"wideLayout": true
},
"pluginVersion": "12.2.0",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${metrics_datasource}"
},
"editorMode": "code",
"expr": "histogram_quantile(0.95, sum by (le, gen_ai_provider_name) (rate(gen_ai_server_time_to_first_token_seconds_bucket{telemetry_sdk_name=\"openlit\", service_name=~\"$service_name\", deployment_environment=~\"$deployment_environment\"}[$__rate_interval])))",
"legendFormat": "{{gen_ai_provider_name}} P95",
"range": true,
"refId": "A"
}
],
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},
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},
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