Connections
Prometheus + Tempo
Connections
Prometheus + Tempo
LLM Observability with Prometheus and Grafana Tempo using OpenLIT
To send OpenTelemetry metrics and traces collected by OpenLIT from your LLM Application to Prometheus and Grafana tempo, Follow the below step.
1
Install OpenTelemetry Collector (Optional)
This step is optional if you have the OpenTelemetry Collector already running.
For detailed installation instructions for the OpenTelemetry Collector , please refer to the OpenTelemetry Collector Documentation. This guide provides comprehensive steps to get you up and running with the Collector on various platforms.
2
Configure the OpenTelemetry Collector
-
Configure HTTP Receiver: In the
receiverssection of your OpenTelemetry Collector config, ensure thehttpreceiver is set withendpoint: 0.0.0.0:4318.receivers: otlp: protocols: http: endpoint: 0.0.0.0:4318 -
Define Exporters: Add
prometheusremotewriteandotlpexporters.exporters: prometheusremotewrite: endpoint: YOUR_PROMETHEUS_REMOTE_WRITE_URL add_metric_suffixes: false otlp: endpoint: YOUR_TEMPO_URLReplace:
YOUR_PROMETHEUS_REMOTE_WRITE_URLwith the Remote Write URL of Prometheus.- Example -
https://prometheus.grafana.net/api/prom/push
- Example -
YOUR_TEMPO_URLwith the URL of Grafana Tempo- Example -
tempo.grafana.net:443
- Example -
-
Assign Exporters to Pipelines: Link
prometheusremotewritetoservice.pipelines.metricsandotlptoservice.pipelines.tracesfor data export.service: pipelines: traces: receivers: [ otlp ] exporters: [ otlp ] metrics: receivers: [ otlp ] exporters: [ prometheusremotewrite ]
Complete Configuration Example
receivers:
otlp:
protocols:
grpc:
endpoint: 0.0.0.0:4317
http:
endpoint: 0.0.0.0:4318
processors:
batch:
memory_limiter:
# 80% of maximum memory up to 2G
limit_mib: 1500
# 25% of limit up to 2G
spike_limit_mib: 512
check_interval: 5s
exporters:
prometheusremotewrite:
endpoint: "YOUR_PROMETHEUS_REMOTE_WRITE_URL"
add_metric_suffixes: false
otlp:
endpoint: "YOUR_TEMPO_URL"
service:
pipelines:
traces:
receivers: [ otlp ]
processors: [ memory_limiter, batch ]
exporters: [ otlp ]
metrics:
receivers: [ otlp ]
processors: [ memory_limiter, batch ]
exporters: [ prometheusremotewrite ]
3
Add the following two lines to your application code:
import openlit
openlit.init(
otlp_endpoint="YOUR_OTELCOL_URL:4318",
)
Replace:
YOUR_OTELCOL_URL:4318with the URL HTTP endpoint of your OpenTelemetry Collector.- Example -
http://127.0.0.1:4318
- Example -
Refer to the OpenLIT Python SDK repository for more advanced configurations and use cases.
4
Start monitoring using a pre-built Grafana dashboard
- Log into your Grafana Instance. To install Grafana, Refer to the Official documentation
- Make sure Prometheus and Tempo are added as a datasource in Grafana. To add a new datasource, You can follow the steps in the Official documentation
- Once Prometheus and Tempo are available as data source 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 named
Dashboardbelow. - Paste the dashboard JSON text directly into the text area.
- Click Import.
- Save the 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": 15,
"links": [
{
"asDropdown": false,
"icon": "bolt",
"includeVars": false,
"keepTime": false,
"tags": [],
"targetBlank": true,
"title": "OpenLIT Github",
"tooltip": "Github",
"type": "link",
"url": "https://github.com/openlit/openlit"
},
{
"asDropdown": false,
"icon": "doc",
"includeVars": false,
"keepTime": false,
"tags": [],
"targetBlank": true,
"title": "OpenLIT Docs",
"tooltip": "Documentation",
"type": "link",
"url": "https://docs.openlit.io/"
}
],
"panels": [
{
"datasource": {
"type": "prometheus",
"uid": "${metricsDatasource}"
},
"gridPos": {
"h": 5,
"w": 12,
"x": 0,
"y": 0
},
"id": 21,
"options": {
"code": {
"language": "plaintext",
"showLineNumbers": false,
"showMiniMap": false
},
"content": "---\n# GenAI Observability\n\nThis dashboard displays the usage of Large Language Models (LLM) and Vector Databases, tracking OpenTelemetry Traces and Metrics sent using [OpenLIT](https://github.com/openlit/openlit).\n\n---",
"mode": "markdown"
},
"pluginVersion": "11.1.0-69950",
"transparent": true,
"type": "text"
},
{
"datasource": {
"type": "prometheus",
"uid": "${metricsDatasource}"
},
"description": "This panel displays the LLM Request Rate ",
"fieldConfig": {
"defaults": {
"color": {
"fixedColor": "blue",
"mode": "palette-classic-by-name"
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "red",
"value": null
},
{
"color": "#EAB839",
"value": 10
},
{
"color": "#6ED0E0",
"value": 100
}
]
},
"unit": "reqps"
},
"overrides": []
},
"gridPos": {
"h": 2,
"w": 12,
"x": 12,
"y": 0
},
"id": 22,
"options": {
"colorMode": "background",
"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"showPercentChange": true,
"textMode": "value_and_name",
"wideLayout": true
},
"pluginVersion": "11.1.0-69950",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${metricsDatasource}"
},
"disableTextWrap": false,
"editorMode": "code",
"expr": "sum(rate(gen_ai_total_requests{telemetry_sdk_name=\"openlit\"}[$__rate_interval]))",
"fullMetaSearch": false,
"includeNullMetadata": true,
"instant": false,
"legendFormat": "LLM Request Rate",
"range": true,
"refId": "A",
"useBackend": false
}
],
"transparent": true,
"type": "stat"
},
{
"datasource": {
"type": "prometheus",
"uid": "${metricsDatasource}"
},
"description": "This panel displays the VectorDB Request Rate",
"fieldConfig": {
"defaults": {
"color": {
"fixedColor": "blue",
"mode": "palette-classic-by-name"
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "red",
"value": null
},
{
"color": "#EAB839",
"value": 10
},
{
"color": "#6ED0E0",
"value": 100
}
]
},
"unit": "reqps"
},
"overrides": []
},
"gridPos": {
"h": 2,
"w": 12,
"x": 12,
"y": 2
},
"id": 23,
"options": {
"colorMode": "background",
"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"showPercentChange": true,
"textMode": "value_and_name",
"wideLayout": true
},
"pluginVersion": "11.1.0-69950",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${metricsDatasource}"
},
"disableTextWrap": false,
"editorMode": "code",
"expr": "sum(rate(db_total_requests{telemetry_sdk_name=\"openlit\"}[$__rate_interval]))",
"fullMetaSearch": false,
"includeNullMetadata": true,
"instant": false,
"legendFormat": "VectorDB Request Rate",
"range": true,
"refId": "A",
"useBackend": false
}
],
"transparent": true,
"type": "stat"
},
{
"datasource": {
"type": "prometheus",
"uid": "${metricsDatasource}"
},
"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": null
},
{
"color": "red",
"value": 80
}
]
},
"unit": "currencyUSD"
},
"overrides": []
},
"gridPos": {
"h": 3,
"w": 12,
"x": 12,
"y": 4
},
"id": 2,
"options": {
"colorMode": "background",
"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"showPercentChange": true,
"textMode": "auto",
"wideLayout": true
},
"pluginVersion": "11.1.0-69950",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${metricsDatasource}"
},
"disableTextWrap": false,
"editorMode": "code",
"expr": "sum(gen_ai_usage_cost_sum{telemetry_sdk_name=\"openlit\", gen_ai_application_name=~\"$application\", gen_ai_environment=~\"$environment\"})",
"fullMetaSearch": false,
"includeNullMetadata": true,
"instant": false,
"legendFormat": "Total Usage Cost",
"range": true,
"refId": "A",
"useBackend": false
}
],
"transparent": true,
"type": "stat"
},
{
"datasource": {
"type": "prometheus",
"uid": "${metricsDatasource}"
},
"description": "This panel displays the total number of successful requests made to the GenAI system. A successful request is one that is completed without errors, indicating seamless operation and effective utilization of the GenAI service. Tracking this helps in understanding the reliability and performance of the GenAI system.",
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic-by-name"
},
"mappings": [],
"min": 0,
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
},
"unit": "none"
},
"overrides": []
},
"gridPos": {
"h": 5,
"w": 6,
"x": 0,
"y": 5
},
"id": 1,
"options": {
"colorMode": "background",
"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"showPercentChange": true,
"textMode": "auto",
"wideLayout": true
},
"pluginVersion": "11.1.0-69950",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${metricsDatasource}"
},
"disableTextWrap": false,
"editorMode": "code",
"expr": "sum by() (gen_ai_total_requests{telemetry_sdk_name=\"openlit\", gen_ai_application_name=~\"$application\", gen_ai_environment=~\"$environment\"})",
"fullMetaSearch": false,
"includeNullMetadata": true,
"instant": false,
"legendFormat": "Total Successful GenAI Requests",
"range": true,
"refId": "A",
"useBackend": false
}
],
"transparent": true,
"type": "stat"
},
{
"datasource": {
"type": "prometheus",
"uid": "${metricsDatasource}"
},
"description": "This panel displays the total count of successful VectorDB requests. It indicates requests that have been processed completely without any errors, showcasing VectorDB's effectiveness and reliability. Watching this helps gauge how well VectorDB is performing and its success in handling operations.",
"fieldConfig": {
"defaults": {
"color": {
"mode": "thresholds"
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
},
"unit": "none"
},
"overrides": []
},
"gridPos": {
"h": 5,
"w": 6,
"x": 6,
"y": 5
},
"id": 7,
"options": {
"colorMode": "background",
"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"showPercentChange": true,
"textMode": "auto",
"wideLayout": true
},
"pluginVersion": "11.1.0-69950",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${metricsDatasource}"
},
"disableTextWrap": false,
"editorMode": "code",
"expr": "sum(db_total_requests{telemetry_sdk_name=\"openlit\", gen_ai_application_name=~\"$application\", gen_ai_environment=~\"$environment\"})",
"fullMetaSearch": false,
"includeNullMetadata": true,
"instant": false,
"legendFormat": "Total Successful VectorDB Requests",
"range": true,
"refId": "A",
"useBackend": false
}
],
"transparent": true,
"type": "stat"
},
{
"datasource": {
"type": "prometheus",
"uid": "${metricsDatasource}"
},
"description": "This panel displays the total number of tokens consumed by GenAI requests, providing a direct measure of usage. Monitoring this helps in assessing the demand on GenAI services and guiding resource allocation or optimization strategies.",
"fieldConfig": {
"defaults": {
"color": {
"fixedColor": "purple",
"mode": "shades"
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
},
"unit": "none"
},
"overrides": []
},
"gridPos": {
"h": 3,
"w": 6,
"x": 12,
"y": 7
},
"id": 3,
"options": {
"colorMode": "background",
"graphMode": "area",
"justifyMode": "auto",
"orientation": "auto",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"showPercentChange": true,
"textMode": "auto",
"wideLayout": true
},
"pluginVersion": "11.1.0-69950",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${metricsDatasource}"
},
"disableTextWrap": false,
"editorMode": "code",
"expr": "sum(gen_ai_usage_total_tokens{telemetry_sdk_name=\"openlit\", gen_ai_application_name=~\"$application\", gen_ai_environment=~\"$environment\"})",
"fullMetaSearch": false,
"includeNullMetadata": true,
"instant": false,
"legendFormat": "Total Usage Tokens",
"range": true,
"refId": "A",
"useBackend": false
}
],
"transparent": true,
"type": "stat"
},
{
"datasource": {
"type": "prometheus",
"uid": "${metricsDatasource}"
},
"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. Monitoring this assists in optimizing budget allocation and improving cost efficiency in GenAI utilization.",
"fieldConfig": {
"defaults": {
"color": {
"fixedColor": "blue",
"mode": "shades"
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "#EAB839",
"value": 0.5
},
{
"color": "red",
"value": 1
}
]
},
"unit": "currencyUSD"
},
"overrides": []
},
"gridPos": {
"h": 3,
"w": 6,
"x": 18,
"y": 7
},
"id": 5,
"options": {
"colorMode": "background",
"graphMode": "none",
"justifyMode": "auto",
"orientation": "auto",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"showPercentChange": true,
"textMode": "auto",
"wideLayout": true
},
"pluginVersion": "11.1.0-69950",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${metricsDatasource}"
},
"disableTextWrap": false,
"editorMode": "code",
"expr": "avg by() (gen_ai_usage_cost_sum{telemetry_sdk_name=\"openlit\", gen_ai_application_name=~\"$application\", gen_ai_environment=~\"$environment\"})",
"fullMetaSearch": false,
"includeNullMetadata": true,
"instant": false,
"legendFormat": "Avg Usage Cost",
"range": true,
"refId": "A",
"useBackend": false
}
],
"transparent": true,
"type": "stat"
},
{
"datasource": {
"type": "tempo",
"uid": "${traceDatasource}"
},
"description": "This panel displays the distribution of request durations for both GenAI and VectorDB services. It highlights how long requests take to complete, from the shortest to the longest durations, offering insights into system performance and efficiency. Understanding this distribution helps in identifying bottlenecks and optimizing response times for both services.",
"fieldConfig": {
"defaults": {
"color": {
"fixedColor": "blue",
"mode": "palette-classic"
},
"custom": {
"fillOpacity": 81,
"gradientMode": "opacity",
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
},
"lineWidth": 3,
"stacking": {
"group": "A",
"mode": "none"
}
},
"mappings": [],
"min": 0,
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 10
}
]
},
"unit": "s"
},
"overrides": []
},
"gridPos": {
"h": 7,
"w": 24,
"x": 0,
"y": 10
},
"id": 4,
"options": {
"legend": {
"calcs": [],
"displayMode": "list",
"placement": "bottom",
"showLegend": false
}
},
"pluginVersion": "11.1.0-69372",
"targets": [
{
"datasource": {
"type": "tempo",
"uid": "${traceDatasource}"
},
"filters": [
{
"id": "status",
"operator": "=",
"scope": "intrinsic",
"tag": "status",
"value": "ok",
"valueType": "keyword"
},
{
"id": "f755ab99",
"operator": "=",
"scope": "span",
"tag": "telemetry.sdk.name",
"value": [
"openlit"
],
"valueType": "string"
}
],
"limit": 20,
"query": "{status=ok && span.telemetry.sdk.name=\"openlit\" && span.gen_ai.application_name=~\"$application\" && span.gen_ai.environment=~\"$environment\"}",
"queryType": "traceql",
"refId": "A",
"tableType": "traces"
}
],
"title": "Request Duration Distribution",
"type": "histogram"
},
{
"datasource": {
"type": "prometheus",
"uid": "${metricsDatasource}"
},
"description": "This panel displays the ranking of GenAI models based on their usage frequency. It identifies which models are most popular or in-demand, providing insights into user preferences and operational trends. Analyzing this helps in resource allocation, optimizing model availability, and understanding which GenAI features are driving usage.",
"fieldConfig": {
"defaults": {
"color": {
"mode": "continuous-BlYlRd"
},
"mappings": [],
"thresholds": {
"mode": "absolute",
"steps": [
{
"color": "green",
"value": null
},
{
"color": "red",
"value": 80
}
]
},
"unit": "none"
},
"overrides": []
},
"gridPos": {
"h": 10,
"w": 14,
"x": 0,
"y": 17
},
"id": 17,
"options": {
"displayMode": "gradient",
"maxVizHeight": 300,
"minVizHeight": 16,
"minVizWidth": 8,
"namePlacement": "auto",
"orientation": "horizontal",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"showUnfilled": true,
"sizing": "auto",
"valueMode": "text"
},
"pluginVersion": "11.1.0-69950",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${metricsDatasource}"
},
"disableTextWrap": false,
"editorMode": "code",
"expr": "topk(5, sum by(gen_ai_request_model) (gen_ai_total_requests{telemetry_sdk_name=\"openlit\", gen_ai_application_name=~\"$application\", gen_ai_environment=~\"$environment\"}))",
"fullMetaSearch": false,
"includeNullMetadata": true,
"instant": false,
"legendFormat": "__auto",
"range": true,
"refId": "A",
"useBackend": false
}
],
"title": "Top GenAI Models by Usage",
"transparent": true,
"type": "bargauge"
},
{
"datasource": {
"type": "prometheus",
"uid": "${metricsDatasource}"
},
"description": "This panel displays the distribution of GenAI requests across different platforms such as OpenAI, Cohere, Anthropic, etc. It shows where requests are being sent, giving insights into platform popularity and usage patterns. Monitoring this helps in understanding platform preferences, and it can guide strategic decisions for integration or diversification of GenAI services.",
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
}
},
"mappings": [],
"min": 0,
"unit": "none"
},
"overrides": []
},
"gridPos": {
"h": 5,
"w": 5,
"x": 14,
"y": 17
},
"id": 16,
"options": {
"legend": {
"displayMode": "list",
"placement": "bottom",
"showLegend": false
},
"pieType": "pie",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"tooltip": {
"mode": "single",
"sort": "none"
}
},
"pluginVersion": "11.1.0-69372",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${metricsDatasource}"
},
"disableTextWrap": false,
"editorMode": "code",
"expr": "sum by(gen_ai_system) (gen_ai_total_requests{telemetry_sdk_name=\"openlit\", gen_ai_application_name=~\"$application\", gen_ai_environment=~\"$environment\"})",
"fullMetaSearch": false,
"includeNullMetadata": true,
"instant": false,
"legendFormat": "__auto",
"range": true,
"refId": "A",
"useBackend": false
}
],
"title": "GenAI Requests by Platform",
"type": "piechart"
},
{
"datasource": {
"type": "prometheus",
"uid": "${metricsDatasource}"
},
"description": "This panel displays the breakdown of GenAI requests by type, such as Chat, Embedding, Image, Audio, and Fine Tuning. It reveals the diversity in usage, highlighting which types of requests are most common. Understanding this distribution assists in optimizing resources for the most demanded services and planning for future needs based on usage trends.",
"fieldConfig": {
"defaults": {
"color": {
"mode": "palette-classic"
},
"custom": {
"hideFrom": {
"legend": false,
"tooltip": false,
"viz": false
}
},
"mappings": [],
"min": 0,
"unit": "none"
},
"overrides": []
},
"gridPos": {
"h": 5,
"w": 5,
"x": 19,
"y": 17
},
"id": 13,
"options": {
"legend": {
"displayMode": "list",
"placement": "bottom",
"showLegend": false
},
"pieType": "pie",
"reduceOptions": {
"calcs": [
"lastNotNull"
],
"fields": "",
"values": false
},
"tooltip": {
"mode": "single",
"sort": "none"
}
},
"pluginVersion": "11.1.0-69372",
"targets": [
{
"datasource": {
"type": "prometheus",
"uid": "${metricsDatasource}"
},
"disableTextWrap": false,
"editorMode": "code",
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"description": "This panel displays a comparative graph showing the average number of tokens consumed for completions and prompts against the average usage cost. It provides a visual representation of the relationship between the volume of data processed (in tokens) and the financial implications of using GenAI services. Analyzing this comparison helps in assessing cost-effectiveness and guiding strategic decisions for efficient resource utilization.",
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