An AI-powered PostgreSQL monitoring and management platform that combines real-time metrics, proactive anomaly detection, and an agentic AI assistant to help teams diagnose and resolve issues faster.
The pgEdge AI DBA Workbench is a self-hosted, AI-driven platform designed to monitor, manage, and optimize PostgreSQL environments at scale. It integrates continuous metrics collection across 34 built-in probes, advanced alerting with three-tier anomaly detection (statistical, vector-based, and LLM-powered), and an intelligent AI assistant ("Ellie") that can actively troubleshoot issues.
Unlike traditional monitoring tools, the Workbench provides an agentic AI experience, enabling users to run queries, analyze execution plans, inspect schemas, and perform multi-step diagnostics directly through natural language. Its MCP-native architecture allows seamless integration with modern AI development tools like Claude Code, Cursor, and VS Code with Copilot.
The platform offers multi-level dashboards (estate to object level), native support for distributed PostgreSQL (including Spock multi-master replication), and flexible deployment options. It can run fully air-gapped with local LLMs or connect to cloud AI providers like OpenAI, Anthropic, or Google.
Built for enterprises running mission-critical, distributed PostgreSQL workloads, the Workbench acts as a force multiplier for DBA and DevOps teams, reducing manual effort, improving visibility, and accelerating issue resolution while keeping all data within the user's infrastructure.
Highlights
Agentic AI assistant (Ellie): Performs real diagnostics, runs queries, analyzes execution plans, and guides multi-step troubleshooting, not just recommendations
Three-tier anomaly detection: Combines statistical baselines, vector similarity, and LLM classification to catch issues before they become outages
Built for distributed PostgreSQL: Native support for multi-master (Spock) replication, as well as native PostgreSQL logical and binary replication, with full estate visibility across clusters and environments
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
This listing offers a single pricing option: the AI DBA Workbench, provided free. There are no tiers, instance sizes, or usage-based add-ons to choose from. You install and run the self-hosted platform on your own infrastructure at no software cost. Because it is free, your only expenses come from the infrastructure you run it on and any AI model you connect. A local model runner adds no per-use charge beyond hardware, while a cloud model charges per token. Commercial support is available separately and is not part of this free listing.
Top-of-mind questions for buyers
What does one "unit" cover, and does it limit how many databases I can monitor?
The listing has one free unit covering the whole self-hosted platform. It scales from a single instance to hundreds of clusters. You add each database through the admin panel or REST API, and the Collector begins probing it. There is no per-instance or per-node software charge.
Since the software is free, what real costs should I plan for when running it?
Your costs come from the infrastructure the four components run on, plus any cloud AI model you connect. A local model runner adds no per-use charge beyond hardware. Cloud models charge per token. Commercial support is available separately, included at no extra cost for pgEdge Enterprise Postgres subscribers.
Does my cost change if I turn off AI features or my cloud model provider goes down?
The software stays free regardless. If you disable AI or run a local model, you avoid cloud per-token charges entirely. When a cloud provider is down, monitoring and alerting keep running; tier-three LLM detection falls back to tiers one and two until service returns.
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