pganalyze vs Percona Monitoring and Management

Quick verdict

pganalyze monitors and tunes PostgreSQL only. The open source pganalyze collector (BSD 3-Clause) runs on the database server or on a VM or container that can reach it, and sends query statistics, schema statistics, logs and, on self-managed servers, operating system metrics to pganalyze's cloud service or, on the Enterprise plan, to the self-hosted pganalyze Enterprise Server. Its documentation lists Amazon RDS and Aurora, Azure Flexible Server, Google Cloud SQL and AlloyDB, CloudNativePG on Kubernetes, Heroku Postgres, Crunchy Bridge, Aiven, Supabase, Neon and self-managed servers. See our pganalyze profile.

Percona Monitoring and Management (PMM) is Percona's open source observability and management platform for MySQL, PostgreSQL, MongoDB and Valkey or Redis. PMM Server (GNU AGPLv3) bundles Grafana dashboards, VictoriaMetrics, ClickHouse for Query Analytics and an internal PostgreSQL; PMM Client (Apache 2.0) runs pmm-agent and exporters next to each database or monitors cloud databases remotely. Version 3.10.0 was released on 22 October 2026. See our PMM profile.

Side by side

Key differences

Specialist service versus general open source stack

pganalyze is built around PostgreSQL alone, and its features reflect that. The Index Advisor uses what pganalyze calls its Indexing Engine to extract table scans from the workload and run what-if analysis for missing indexes, and reports unused indexes too. The VACUUM Advisor covers bloat, freezing and autovacuum tuning. Query Advisor flags query plan problems, and Workbooks give a place to work through a slow query. Log Insights watches the PostgreSQL log for unexpected events.

PMM is a general monitoring stack that covers PostgreSQL as one of several engines. It provides PostgreSQL dashboards, Query Analytics (QAN) with per-query metrics, and Percona Advisors that run configuration, performance, security and query checks, including PostgreSQL checks, with custom advisors possible. In our view, PMM tells you what is happening across your databases, while pganalyze goes further in suggesting the specific PostgreSQL change to make.

Query statistics: pg_stat_statements and pg_stat_monitor

Both depend on PostgreSQL's statistics extensions. pganalyze reads pg_stat_statements and collects plans for slow queries through auto_explain (listed from the Scale plan). PMM can use pg_stat_statements or Percona's own pg_stat_monitor, which PMM's documentation describes as adding bucket-based aggregation, query examples and histograms at slightly higher overhead, compatible with PostgreSQL 13 to 18. PMM recommends pg_stat_monitor for detailed production analysis and documents a known issue: keep pg_stat_monitor.pgsm_enable_query_plan off because plan timings can be wrong.

The PMM documentation gives these settings for the pg_stat_statements route; pganalyze also needs the extension loaded:

# postgresql.conf (restart required)
shared_preload_libraries = 'pg_stat_statements'
track_activity_query_size = 2048
pg_stat_statements.track = all
track_io_timing = on

-- then, in the postgres database
CREATE EXTENSION pg_stat_statements;

On managed services, settings like shared_preload_libraries are changed through the provider's parameter groups or flags rather than the file. pganalyze notes that system-level statistics such as memory and I/O cannot be collected from managed services by its collector.

Deployment, operations and data location

With PMM you run PMM Server yourself on Docker, Podman, a virtual machine, AWS Marketplace or Kubernetes with Helm; from 3.10.0 an HA Cluster mode with 1, 3 or 5 replicas is generally available on Kubernetes. You own its storage, upgrades and backups. Teams on PMM 2 should note Percona's statement that migration from 2.x will no longer be possible after PMM 3.13.0, expected in January 2027.

pganalyze on Production and Scale is SaaS: you run only the collector, and query text and statistics are sent to pganalyze. Scale includes PII filtering for Log Insights. If monitoring data must stay inside your network, pganalyze Enterprise Server runs on Docker on a VM or on Kubernetes, but only on the quote-based Enterprise plan. PMM keeps data in-house at no licence cost from the start.

Scope beyond PostgreSQL

If you also run MySQL or MongoDB, PMM covers them in the same server with the same dashboards and alerting, and adds backup and restore management for MySQL and MongoDB (the documentation lists no PostgreSQL backup management). pganalyze will never cover those engines, so a mixed estate would need a second tool. If your estate is PostgreSQL only, that breadth is irrelevant and the comparison comes down to advice depth versus cost and operations.

Pricing and licensing

pganalyze. Listed on pganalyze.com/pricing in November 2026 (prices shown with a dollar sign): Production $149 per month for one database server with 14 days of history; Scale $399 per month for up to four billable servers, then $100 per month per additional server, with 35 days of history, auto_explain plans, VACUUM Advisor and Log Insights; Enterprise custom pricing, billed annually, with 100 days of history and the self-hosted Enterprise Server option. A primary counts as one billable server and a replica as half on Scale and Enterprise. All plans have unlimited users. There is a 14-day free trial with Scale features and no credit card, and no free plan. Billing uses the highest number of active servers seen in the last 24 hours. Example: four primaries with two replicas on Scale count as 4 + (2 x 0.5) = 5 billable servers, which lists at $399 + $100 = $499 a month.

PMM. Free and open source, with no licence fee for any number of servers. You pay for the infrastructure that runs PMM Server and for the time to maintain it. Percona sells support and services separately and does not publish those prices.

Pricing checked on the vendors' official pages on 7 November 2026. Prices change; confirm before buying.

Where each one leads

Limitations

When to choose each

When neither is right

• You want a lighter open source PostgreSQL option: see pgAdmin vs pgBadger for log analysis, and pgwatch for metrics.
• You want database data inside a full observability platform: see Datadog vs New Relic and New Relic vs Dynatrace.
• You prefer to build your own stack: see Prometheus vs Datadog and Grafana vs Datadog.
• You want to see a wider field: see the best PostgreSQL monitoring tools.

Final recommendation

Frequently asked questions

Sources

• pganalyze pricing
• pganalyze documentation
• pganalyze Enterprise Server documentation
• pganalyze collector repository
• Percona PMM repository and licences
• Percona PMM 3.10.0 release notes
• PMM 3 documentation
• PMM 3: connect a PostgreSQL database

Checked November 2026.

How we research comparisons: our editorial method.