PostgreSQL Monitoring Tools Compared (2026)

PostgreSQL monitoring tools range from built-in catalog views and CLI utilities to full SaaS platforms with alerting, query analysis, and health checks. The right choice depends on your team size, budget, PostgreSQL expertise, and whether you need deep Postgres-specific insights or general infrastructure visibility.

The Problem

PostgreSQL provides extensive monitoring capabilities through its system catalogs (pg_stat_* views, pg_settings, pg_locks) and extensions like pg_stat_statements. The raw data is all there. The problem is turning that data into actionable insight without building a custom monitoring stack.

A small team running a few PostgreSQL instances might get by with pg_stat_activity and manual EXPLAIN ANALYZE. But as databases grow in size, number, and criticality, you need historical trends, automated alerting, query performance tracking, and health checks that run without someone remembering to SSH in and run a query.

The monitoring tool landscape for PostgreSQL falls into distinct categories, each with tradeoffs. General-purpose infrastructure tools (Datadog, New Relic, Grafana) can monitor PostgreSQL alongside everything else, but their Postgres support is often shallow — they track connection counts and transaction rates without understanding query plans, index usage, or vacuum health. Postgres-specialized tools (pganalyze, myDBA.dev, PMM) go deeper but require evaluating a separate product.

Open-source tools (pgwatch2, pgBadger, pg_stat_monitor) are free but require self-hosting, configuration, and maintenance. Managed database providers (AWS RDS Performance Insights, Azure Monitor) offer integrated monitoring but lock you into their ecosystem and often lack the depth of standalone tools.

Choosing poorly means either paying for features you do not use, missing critical Postgres-specific insights, or spending engineering time maintaining monitoring infrastructure instead of building product.

How to Detect It

Before evaluating tools, understand what PostgreSQL itself provides. These built-in monitoring queries are the baseline that any tool should build upon:

-- Current activity: active queries, waiting connections, idle transactions
SELECT
    state,
    wait_event_type,
    wait_event,
    count(*) AS connection_count
FROM pg_stat_activity
WHERE backend_type = 'client backend'
GROUP BY state, wait_event_type, wait_event
ORDER BY connection_count DESC;

-- Query performance: top queries by total execution time
-- Requires pg_stat_statements extension
SELECT
    substring(query, 1, 80) AS query_preview,
    calls,
    round(total_exec_time::numeric, 1) AS total_ms,
    round(mean_exec_time::numeric, 1) AS avg_ms,
    rows
FROM pg_stat_statements
ORDER BY total_exec_time DESC
LIMIT 15;

-- Database health snapshot: cache hit ratio, transaction rates, dead tuples
SELECT
    datname AS database_name,
    round(
        100.0 * blks_hit / nullif(blks_hit + blks_read, 0), 1
    ) AS cache_hit_ratio,
    xact_commit AS commits,
    xact_rollback AS rollbacks,
    deadlocks
FROM pg_stat_database
WHERE datname = current_database();

If you find yourself running these queries manually more than once a week, you need a monitoring tool. If you need historical trends, alerting, or multi-instance visibility, the built-in views alone are not sufficient.

How myDBA.dev Shows It

myDBA.dev is purpose-built for PostgreSQL monitoring with a focus on actionable insights rather than raw metrics. The dashboard provides an immediate overview of database health across all monitored instances — connection activity, query performance trends, replication status, and health scores — without requiring you to write SQL.

The health check system runs automated assessments across 10 domains (Performance, Indexes, Vacuum, Schema, Security, WAL, Storage, Replication, Configuration, and Extensions) and produces a scored report with specific, actionable recommendations. Each finding includes severity, the SQL to verify it, and the fix — turning monitoring data into a clear remediation checklist.

Beyond the dashboard, myDBA.dev provides deep Postgres-specific features: automatic EXPLAIN plan capture, query plan regression detection, index advisor, lock chain visualization, replication topology mapping, and extension-specific monitoring for TimescaleDB, pgvector, and PostGIS.

PostgreSQL Monitoring Tools Compared

myDBA.dev

Type: Hosted SaaS, Postgres-specialized

myDBA.dev focuses exclusively on PostgreSQL, providing deep monitoring that generic tools cannot match. It combines a lightweight Go collector (15-second metric intervals) with PostgreSQL RPC functions for analysis, delivering real-time visibility into query performance, index efficiency, vacuum health, and replication status.

Strengths: Automated health checks with scored assessments, EXPLAIN plan capture and regression detection, index advisor, lock chain visualization, extension monitoring (TimescaleDB, pgvector, PostGIS), replication topology mapping. The collector connects directly to your PostgreSQL instance — no agent installation on the database server, no sidecar containers.

Gaps: Newer product with a smaller community than established tools. No infrastructure-level metrics (CPU, memory, disk) — it monitors PostgreSQL, not the host.

Pricing: Free tier (7-day retention, 1 instance), Pro tier with extended retention and multi-instance support.

pganalyze

Type: Hosted SaaS, Postgres-specialized

pganalyze is a mature PostgreSQL monitoring tool focused on query performance analysis. It collects data via a Ruby-based collector and provides detailed query statistics, index recommendations, EXPLAIN plan analysis, and schema change tracking.

Strengths: Deep query performance analysis, automated index recommendations, schema evolution tracking, log-based EXPLAIN plan collection, good documentation and established user base.

Gaps: The collector requires installation on the database host or a sidecar container. Limited real-time monitoring — data is processed in batch intervals. No built-in health check scoring system. No extension-specific monitoring for pgvector, PostGIS, or TimescaleDB.

Pricing: Starts at $249/month per server. No free tier for production use.

Datadog

Type: Hosted SaaS, general-purpose with Postgres integration

Datadog monitors PostgreSQL as part of its broader infrastructure monitoring platform. It tracks standard database metrics (connections, transactions, replication lag) alongside host metrics, APM traces, and log aggregation.

Strengths: Unified monitoring across your entire stack — correlate database slowness with application latency and infrastructure issues. Excellent alerting and dashboard customization. APM integration shows which application endpoints generate the most database load.

Gaps: PostgreSQL monitoring is shallow compared to specialized tools. No EXPLAIN plan capture, no index advisor, no vacuum-specific analysis, no health check scoring. Query analysis relies on pg_stat_statements without deeper plan-level insights. Postgres is one of hundreds of integrations, not the core focus.

Pricing: Database monitoring starts at $70/host/month on top of infrastructure monitoring ($15+/host/month). Costs scale quickly with host count.

Percona Monitoring and Management (PMM)

Type: Self-hosted, open-source, multi-database

PMM is Percona's open-source monitoring platform supporting PostgreSQL, MySQL, and MongoDB. It uses a client-server architecture with exporters collecting metrics and a central PMM Server (based on Grafana and VictoriaMetrics) for visualization and alerting.

Strengths: Free and open-source. Good query analytics (QAN) for identifying slow queries. Familiar Grafana-based dashboards. Supports multiple database engines. Active community and Percona support options.

Gaps: Requires self-hosting the PMM Server — operational overhead for installation, upgrades, backups, and scaling. PostgreSQL support is less mature than MySQL support (Percona's historical focus). No automated health checks or scoring. No EXPLAIN plan regression detection. Dashboard complexity can overwhelm smaller teams.

Pricing: Free (open-source). Percona offers commercial support contracts.

pgwatch2

Type: Self-hosted, open-source, Postgres-specialized

pgwatch2 is a Postgres-only monitoring tool that collects metrics via SQL queries and stores them in InfluxDB or TimescaleDB. It provides pre-built Grafana dashboards for visualization.

Strengths: Free and open-source. Postgres-specific from the ground up. Flexible metric collection — you can add custom SQL queries. Lightweight collector. Stores metrics in a time-series database for efficient historical queries.

Gaps: Requires self-hosting and maintaining three components (collector, metrics store, Grafana). No built-in alerting — relies on Grafana alerting or external tools. No EXPLAIN plan analysis. No automated recommendations. Setup and configuration take significantly more effort than hosted tools.

Pricing: Free (open-source).

pgBadger

Type: CLI tool, log analysis

pgBadger parses PostgreSQL log files and generates detailed HTML reports with query statistics, error analysis, lock information, and checkpoint details. It is a post-hoc analysis tool, not a real-time monitoring system.

Strengths: Free and open-source. Extremely detailed reports from log analysis — query normalization, hourly patterns, error categorization. No database load (analyzes log files, not live connections). Single binary with no dependencies.

Gaps: Not real-time — generates reports from historical logs. Requires PostgreSQL logging to be configured with specific parameters (log_min_duration_statement, log_checkpoints, log_lock_waits). No continuous monitoring, alerting, or dashboards. Each report is a static snapshot.

Pricing: Free (open-source).

pg_stat_statements + Custom Grafana

Type: DIY, self-hosted

Many teams build their own monitoring by querying pg_stat_statements and other system views, storing the results in a time-series database (Prometheus, InfluxDB, TimescaleDB), and building Grafana dashboards.

Strengths: Complete control over what is monitored and how it is displayed. No vendor lock-in. Can integrate with existing Grafana infrastructure.

Gaps: Significant engineering time to build and maintain. No automated analysis — you see metrics but must interpret them yourself. No EXPLAIN plan capture, health checks, or recommendations. Every Postgres version upgrade may break custom queries. The team that built it must also maintain it.

Pricing: Free (but engineering time is not free).

Decision Matrix

| Criteria | myDBA.dev | pganalyze | Datadog | PMM | pgwatch2 | pgBadger | |----------|-----------|-----------|---------|-----|----------|----------| | Setup time | Minutes | Hours | Hours | Hours-Days | Hours-Days | Minutes | | Self-hosting required | No | No | No | Yes | Yes | N/A | | Postgres depth | Deep | Deep | Shallow | Medium | Medium | Deep (logs) | | EXPLAIN plan analysis | Yes | Yes | No | No | No | No | | Health check scoring | Yes | No | No | No | No | No | | Index advisor | Yes | Yes | No | No | No | No | | Extension monitoring | Yes | No | No | No | No | No | | Real-time monitoring | Yes | Delayed | Yes | Yes | Yes | No | | Alerting | Yes | Yes | Yes | Yes | Via Grafana | No | | Multi-database support | PostgreSQL only | PostgreSQL only | Many | MySQL, PG, Mongo | PostgreSQL only | PostgreSQL only | | Free tier | Yes | No | No | Yes (self-host) | Yes (self-host) | Yes |

How to Prevent It

Choose your monitoring tool based on your actual needs, not feature count. A team of two running a single PostgreSQL instance does not need Datadog's APM correlation — they need something that catches missing indexes, vacuum problems, and slow queries before users complain. A platform team managing 50 instances across three cloud providers may need Datadog's breadth even if its Postgres depth is limited.

Start with built-in tools. Enable pg_stat_statements, configure log_min_duration_statement, and learn to read EXPLAIN ANALYZE output. These are foundational skills that no monitoring tool replaces. Then layer a tool on top for historical trends, alerting, and automated analysis.

Evaluate tools against your most common incidents. If your last three production issues were missing indexes, vacuum backlogs, and replication lag, choose a tool that monitors all three with specific recommendations — not one that shows you a CPU graph and leaves you to figure out the database-level root cause.

Review your monitoring setup quarterly. As your database grows, your monitoring needs change. A tool that was sufficient at 10 GB may be inadequate at 1 TB. New features like partitioning, logical replication, or pgvector may require monitoring capabilities your current tool does not have. Treat monitoring tool selection as an ongoing decision, not a one-time choice.

FAQ

What is the best free PostgreSQL monitoring tool?

For self-hosted environments, PMM (Percona Monitoring and Management) offers the most complete free monitoring with query analytics, dashboards, and alerting. For a lighter-weight option, pgwatch2 provides Postgres-specific metrics with Grafana dashboards. For log analysis without real-time monitoring, pgBadger generates detailed reports from PostgreSQL log files. myDBA.dev offers a free tier with 7-day retention that requires no self-hosting.

What should I monitor in a PostgreSQL database?

At minimum: query performance (via pg_stat_statements), connection counts, cache hit ratio, replication lag, vacuum health (dead tuples, last autovacuum time), lock waits, disk usage, and WAL generation rate. For production databases, add EXPLAIN plan tracking (to catch plan regressions), index usage analysis (to find missing or unused indexes), and transaction ID age monitoring (to prevent XID wraparound). Specialized extensions like TimescaleDB, pgvector, and PostGIS have their own metrics that need extension-aware monitoring.

How do PostgreSQL monitoring tools collect data?

Most tools use one of three approaches: (1) a collector agent that connects to PostgreSQL and queries system views (pg_stat_statements, pg_stat_activity, pg_stat_user_tables, etc.) at regular intervals; (2) log parsing, where the tool reads PostgreSQL log files and extracts query statistics, errors, and performance data; or (3) the PostgreSQL statistics collector, which aggregates runtime statistics that tools can query. Some tools combine multiple approaches — for example, using an agent for real-time metrics and log parsing for EXPLAIN plans captured via auto_explain.