About
pgpulse is an Intelligent PostgreSQL observability and health monitoring platform that gives teams deep visibility into database internals, proactively detects incidents before they escalate, and reduces MTTD and MTTR from hours to minutes with evidence-backed runbooks, an MCP server, and rich performance and query insights.
Core Features
Intelligent Pulse Score: A single health score for every PostgreSQL instance, built from critical database signals across replication, vacuum, locks, WAL, storage, memory, query behavior, and operational risk.
Proactive Incident Detection: Detects issues early before they turn into outages, helping teams reduce MTTD with faster visibility into what is degrading and why.
Deep Postgres Internals Monitoring: Monitors the internals that matter most, including XID wraparound risk, replication lag, connection pressure, lock contention, bloat, autovacuum health, WAL pipeline behavior, disk vitals, and object integrity.
Performance and Query Insights: Surfaces slow, unstable, and inefficient query patterns with actionable performance signals, helping teams understand where time, I/O, and contention are being lost.
Evidence-Backed Runbooks: Converts incidents into guided investigations with structured runbooks, context, and recommended next steps so teams can bring MTTR down from hours to minutes.
MCP Server for AI-Native Workflows: Exposes pgpulse investigation data through an MCP server so AI agents and engineering workflows can securely consume health, alert, and evidence context.
Smart Alerts and Incident Workflows: Sends meaningful alerts based on real database health conditions, with grouped notifications and context that help teams focus on incidents that actually matter.
Maintenance and Optimization Insights: Highlights maintenance gaps and optimization opportunities such as vacuum/analyze issues, unused indexes, and index improvement candidates.
Historical Health and Recovery Context: Tracks how an instance is trending over time so teams can see whether a system is stabilizing, degrading, or recovering after an incident.
Lightweight Postgres-Native Monitoring: Built specifically for PostgreSQL with a lightweight collection model that gives deep visibility without becoming operationally heavy.