erikdarlingdata/PerformanceMonitor

Free, open-source SQL Server and Postgres performance monitoring. Collectors, real-time alerts, graphical plan viewer, MCP server for AI analysis. Supports SQL 2016-2025, Azure SQL, AWS RDS, Postgres, Aurora Postgres.

What it solves

This project solves the problem of paying thousands of dollars per server per year for commercial SQL Server monitoring tools. It offers the same kind of monitoring for free, without sending your data anywhere. It also covers PostgreSQL.

How it works

The project comes in two main editions. Lite is a desktop app you run on your own machine; it connects to remote servers on demand and stores data locally in DuckDB and Parquet files. Darling is a service that runs on Windows or Linux, collects data 24/7 into a central PostgreSQL store, and offers a viewer and web dashboard. Both can send alerts to your own AI agent via a built-in MCP server, and Darling can even let the agent change alert settings or force query plans, with toggles that are all off by default.

Who it's for

Database administrators and teams that run SQL Server (or PostgreSQL) and are tired of paying high licensing fees for monitoring. It also suits people who want full control of their monitoring data, since nothing leaves your infrastructure unless you set it up to do so.

Highlights

  • Free and open source (MIT), with no features withheld
  • Two editions: Lite for simple on-demand remote monitoring, Darling for 24/7 collection into PostgreSQL
  • Real-time alerts and customizable notification channels
  • Built-in MCP server lets your own AI agent analyze data or take actions (with opt-in permissions)
  • Supports SQL Server 2016 SP2 through 2025, Azure SQL, AWS RDS, and PostgreSQL
  • No telemetry: data stays on your servers and machines

Related

  • Project
    dbt-labs/dbt-mcp

    dbt‑MCP Server is an open‑source service that exposes a large catalog of dbt‑related tools (SQL execution, semantic‑layer queries, model discovery, dbt CLI commands, job administration, code generation, and documentation lookup) via the Model Context Protocol. It lets LLM‑powered agents understand and act on a dbt project—generating SQL from natural language, running dbt jobs, fetching lineage, and even creating new model files—making it a concrete bridge between data‑engineering metadata and AI assistants.

  • Project
    caioricciuti/duck-ui

    A fully browser-based, serverless workbench for DuckDB that allows for SQL analysis, Python notebooks, and data visualization without requiring a backend or data leaving the user's machine.

  • Project
    explorerhq/sql-explorer

    SQL Explorer is a Django‑based web app for writing, sharing, and visualising SQL queries. It supports any Django‑compatible database plus CSV/JSON uploads, offers schema‑aware autocomplete, in‑browser stats/pivot charts, and an optional LLM‑powered assistant that can draft or debug queries. Install via Docker, log in with admin/admin, and start exploring data instantly.

  • Project
    dorylab/dory

    Dory is an AI-native SQL client that provides a persistent, editable workspace for both humans and AI agents to query, visualize, and manage database operations via the Model Context Protocol.

  • Project
    Higangssh/homebutler

    A lightweight operations toolkit for homelabs that tracks server state changes, verifies backups, and provides structured diagnostics for humans or AI agents.