Build KQL queries for Azure Monitor Logs with templates and syntax guidance.
Last verified: April 2026
Output will appear here...The Azure Monitor Query Builder helps you construct KQL (Kusto Query Language) queries for Azure Monitor with pre-built templates and guided field selection. KQL is powerful but has a learning curve, and this tool bridges the gap by letting you pick common scenarios like VM performance, App Service errors, or network metrics and customize them into production-ready queries. The generated queries can be pasted directly into Azure Monitor Logs, Log Analytics workspaces, or Application Insights.
KQL (Kusto Query Language) is the query language used across Azure Monitor Logs, Log Analytics, Application Insights, Azure Data Explorer, and Microsoft Sentinel. Queries generated by this tool work in any of these services.
No. This tool generates KQL query strings in your browser. You copy the generated query and run it in your Azure environment. The tool itself does not connect to Azure.
The pre-built templates target standard Azure Monitor tables like Heartbeat, Perf, AzureActivity, and AppRequests. You can modify the generated queries to reference custom log tables in your workspace.
Your App Service starts returning intermittent 500 errors. You open the tool, select the App Service error analysis template, and paste the generated KQL into Log Analytics. The query groups AppServiceHTTPLogs by resultCode and time bin, revealing that errors correlate with deployment slots swapping at 3 PM. You add a 'where' clause filtering to the staging slot and confirm the new deployment has a dependency failure.
The builder presents categorized query templates organized by Azure service and monitoring scenario. When you select a template, it populates a KQL query skeleton with appropriate table references, column selections, and time filters. User inputs like resource names, time ranges, and threshold values are injected into parameterized placeholders to produce a complete, executable KQL query.
KQL's 'summarize' operator is your most powerful tool for incident investigation. Combine 'summarize count() by bin(TimeGenerated, 5m), ResultType' to instantly spot error rate spikes. This is significantly faster than scrolling through raw logs in Log Analytics.
Log Analytics workspace ingestion charges $2.76/GB. Before you enable diagnostics on every Azure resource, estimate the log volume first. A busy App Service can generate 10+ GB/day of logs. Use resource-specific tables and diagnostic settings to ingest only what you need.
KQL queries in Azure Monitor have a 10-minute timeout and 64 MB result limit. If you are hitting these limits, narrow your time range, add 'where' filters early in the query pipeline, or use 'project' to select only the columns you need before expensive operations like 'join'.
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Disclaimer: This tool runs entirely in your browser. No data is sent to our servers. Always verify outputs before using them in production. AWS, Azure, and GCP are trademarks of their respective owners.