# Open WebUI Dashboard

> Source: <https://signoz.io/docs/dashboards/dashboard-templates/open-webui-dashboard>
> Published: 2026-07-23 00:00:00+00:00

This dashboard provides a comprehensive view of your `Open WebUI`

deployment using trace and log data. It combines two telemetry sources: Open WebUI's native OpenTelemetry auto-instrumentation of FastAPI, SQLAlchemy, and HTTP clients (server spans carrying `http.route`

, database spans carrying `db.system`

and `db.operation`

), and OpenLIT GenAI instrumentation for model calls (chat spans carrying the OpenTelemetry `gen_ai.*`

semantic conventions such as `gen_ai.request.model`

, `gen_ai.usage.input_tokens`

, and `gen_ai.usage.output_tokens`

, plus OpenLIT's own `gen_ai.usage.cost`

extension). Every panel keys off these attributes and filters through the `service_name`

picker at the top, so you can scope the view to one or more services (for example the application service and the OpenLIT LLM service).

Dashboard Preview

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What This Dashboard Monitors

This dashboard tracks the performance, cost, and reliability of your Open WebUI deployment using OpenTelemetry trace and log data to help you:

**Track Application Health**: See total request volume, error rate, and p95/p99 latency for the Open WebUI backend at a glance.** Analyze Endpoints**: Break down traffic and latency by`http.route`

to find the busiest and slowest API endpoints.**Monitor the Database**: Watch p95/p99 query latency and per-operation counts to catch database-driven slowness.** Measure LLM Usage**: Track model request volume, input and output tokens, and total cost across every model in use.** Control LLM Cost**: Follow spend over time and compare cost per model to keep the bill predictable.** Watch LLM Latency**: See p50, p95, and p99 latency for model calls to surface slow responses and regressions.** Catch Errors Early**: Watch the error rate by service and drill into recent failed traces and logs the moment incidents begin.

Panels Included

Application Health

| Panel | Type | What It Shows |
|---|---|---|
Total Requests | Value | Count of inbound HTTP requests (FastAPI server spans) handled by the selected services |
Error Rate | Value | Fraction of server requests that ended in an error status, shown as a percentage |
Latency p95 | Value | 95th-percentile request duration across all endpoints |
Latency p99 | Value | 99th-percentile request duration across all endpoints |

**Request Rate by Endpoint**: Time-series graph of request volume grouped by`http.route`

, showing the traffic mix and which endpoints dominate.**Latency Percentiles (p50 / p95 / p99)**: Time-series graph of request duration percentiles, showing both typical performance and tail latency over time.** Error Rate Over Time**: Time-series graph of the error fraction of server requests, revealing incident windows and regressions.** Request Distribution by Endpoint**: Pie chart of each endpoint's share of requests (`http.route`

), a clear view of where traffic concentrates.**Endpoint Performance**: Table of endpoints with request count and p95/p99 latency, for a side-by-side comparison to find the slow routes.

Database

**DB Query Latency (p95 / p99)**: Time-series graph of p95 and p99 duration for database (SQLAlchemy) query spans, surfacing database-driven slowness.** Database Operations**: Table of database operations grouped by`db.operation`

, with query count and average latency, showing which queries run most and which are slowest.

LLM / GenAI

| Panel | Type | What It Shows |
|---|---|---|
LLM Requests | Value | Count of LLM chat requests captured by the OpenLIT `gen_ai` spans |
Input Tokens | Value | Total prompt tokens consumed across LLM calls (`gen_ai.usage.input_tokens` ) |
Output Tokens | Value | Total completion tokens generated across LLM calls (`gen_ai.usage.output_tokens` ) |
Total Cost (USD) | Value | Summed OpenLIT-computed cost of LLM calls (`gen_ai.usage.cost` ) |

**LLM Latency Percentiles (p50 / p95 / p99)**: Time-series graph of duration percentiles for LLM chat spans, showing model-call latency both typical and tail.** Token Usage Over Time**: Time-series graph of input vs output token volume, showing demand and how prompt/response sizes trend.** LLM Cost Over Time (USD)**: Time-series graph of LLM spend, to spot cost spikes and trends before they surprise the bill.** Requests by Model**: Pie chart of each model's share of LLM requests (`gen_ai.request.model`

), showing which models carry the load.**Model Breakdown**: Table of models with request count, input/output tokens, cost, and average latency, for the cost and usage comparison across models.

Errors & Logs

**Errors Over Time by Service**: Time-series graph of error span count grouped by`service.name`

, pinpointing which service is failing and when.**Recent Error Traces**: List of the latest errored spans across the selected services, for jumping straight to failures when the error rate climbs.**Recent Logs**: List of the most recent logs from the selected services, for quick log context to read alongside the traces.