{"slug": "open-webui-dashboard", "title": "Open WebUI Dashboard", "summary": "Open WebUI released a new dashboard that monitors the performance, cost, and reliability of its deployment using OpenTelemetry trace and log data, tracking application health, database queries, and LLM usage across endpoints and models.", "body_md": "This dashboard provides a comprehensive view of your `Open WebUI`\n\ndeployment 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`\n\n, database spans carrying `db.system`\n\nand `db.operation`\n\n), and OpenLIT GenAI instrumentation for model calls (chat spans carrying the OpenTelemetry `gen_ai.*`\n\nsemantic conventions such as `gen_ai.request.model`\n\n, `gen_ai.usage.input_tokens`\n\n, and `gen_ai.usage.output_tokens`\n\n, plus OpenLIT's own `gen_ai.usage.cost`\n\nextension). Every panel keys off these attributes and filters through the `service_name`\n\npicker at the top, so you can scope the view to one or more services (for example the application service and the OpenLIT LLM service).\n\nDashboard Preview\n\nDashboards → + New dashboard → Import JSON\n\nWhat This Dashboard Monitors\n\nThis dashboard tracks the performance, cost, and reliability of your Open WebUI deployment using OpenTelemetry trace and log data to help you:\n\n**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`\n\nto 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.\n\nPanels Included\n\nApplication Health\n\n| Panel | Type | What It Shows |\n|---|---|---|\nTotal Requests | Value | Count of inbound HTTP requests (FastAPI server spans) handled by the selected services |\nError Rate | Value | Fraction of server requests that ended in an error status, shown as a percentage |\nLatency p95 | Value | 95th-percentile request duration across all endpoints |\nLatency p99 | Value | 99th-percentile request duration across all endpoints |\n\n**Request Rate by Endpoint**: Time-series graph of request volume grouped by`http.route`\n\n, 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`\n\n), 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.\n\nDatabase\n\n**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`\n\n, with query count and average latency, showing which queries run most and which are slowest.\n\nLLM / GenAI\n\n| Panel | Type | What It Shows |\n|---|---|---|\nLLM Requests | Value | Count of LLM chat requests captured by the OpenLIT `gen_ai` spans |\nInput Tokens | Value | Total prompt tokens consumed across LLM calls (`gen_ai.usage.input_tokens` ) |\nOutput Tokens | Value | Total completion tokens generated across LLM calls (`gen_ai.usage.output_tokens` ) |\nTotal Cost (USD) | Value | Summed OpenLIT-computed cost of LLM calls (`gen_ai.usage.cost` ) |\n\n**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`\n\n), 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.\n\nErrors & Logs\n\n**Errors Over Time by Service**: Time-series graph of error span count grouped by`service.name`\n\n, 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.", "url": "https://wpnews.pro/news/open-webui-dashboard", "canonical_source": "https://signoz.io/docs/dashboards/dashboard-templates/open-webui-dashboard", "published_at": "2026-07-23 00:00:00+00:00", "updated_at": "2026-07-27 06:24:42.539987+00:00", "lang": "en", "topics": ["developer-tools", "artificial-intelligence", "large-language-models"], "entities": ["Open WebUI", "OpenTelemetry", "FastAPI", "SQLAlchemy", "OpenLIT"], "alternates": {"html": "https://wpnews.pro/news/open-webui-dashboard", "markdown": 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