Buyer’s Guide - August 2026

The 10 best PostgreSQL monitoring tools, ranked

PostgreSQL exposes its own statistics views, and the tools that read them properly see query behavior, bloat, vacuum, and replication issues that CPU dashboards never will. This ranking grades ten tools on Postgres metric depth, collection granularity, operating burden, and how predictable the bill is. Netdata leads on per-second visibility and zero-configuration setup; the rest each win somewhere specific.

Start with Netdata (free) Jump to rankings

Why this list exists

PostgreSQL monitoring is not generic infrastructure monitoring with a database logo. The database publishes its own statistics through views like pg_stat_database, pg_stat_statements, and pg_stat_replication, and those views reveal query-level behavior, table bloat, vacuum lag, lock waits, and replication delay that host-level dashboards miss entirely. A tool that never reads those views is monitoring the machine, not the database.

Buyers in this category make one of two mistakes. The first is picking a host monitor and assuming the Postgres checkbox on the integrations page means real coverage. The second is overpaying for query-plan analysis, index advisors, and tuning workbooks they will never open. The gap between those two failure modes is where the right tool sits, and it depends on your setup.

Three dimensions decide the outcome:

Depth versus breadth. Purpose-built tools like pganalyze go deep on EXPLAIN plans and tuning advice. Full-stack tools correlate Postgres with host, container, and application context. Know which problem you are actually solving.
Collection granularity. Per-second collection catches lock storms and connection bursts that 15-60 second polling averages away. PostgreSQL’s statistics are cumulative counters, so the interval determines how precisely you see rates.
Pricing shape. Flat per-node pricing behaves differently at scale than per-GB ingest, per-metric, or per-database-host add-ons. Open source is a distribution model, not a price: you pay in operating time.

We do not quote competitor list prices in this guide. Pricing pages change, negotiated rates vary, and a stale dollar figure is worse than none. Instead we describe the shape of each bill and what makes it grow, and we link every vendor’s pricing page so you can check current numbers yourself. For hands-on operator material, our PostgreSQL monitoring guides cover the metrics and alert thresholds in detail.

We assembled the shortlist from tools that actually read PostgreSQL’s statistics views, cross-checked against community comparisons, vendor documentation, and practitioner discussions. Every tool here monitors Postgres natively; generic uptime checkers and log-only tools were excluded.

The two heaviest criteria are PostgreSQL metric depth and data granularity, because they are where tools in this category genuinely differ. Query-plan analysis and per-second collection are not marketing claims; you can verify them in each vendor’s docs in an afternoon. Cost predictability and deployment burden carry the next most weight, since they are what teams get burned by after the trial ends.

Tester credit

Compiled by the Netdata team - Updated August 12, 2026

Sources verified

Scoring criteria

PostgreSQL metric depth and query analysis 25%
Query-level stats, EXPLAIN plans, wait events, bloat, vacuum, WAL, replication.
Data granularity and real-time visibility 20%
Per-second collection catches spikes that 15-60s polling misses.
Ease of deployment and operations 15%
Auto-discovery beats assembling exporters and dashboards by hand.
Cost predictability 15%
Flat per-node pricing versus per-GB, per-metric, and add-on stacks.
Correlation and context 10%
Tying database metrics to host, container, network, and traces.
Alerting and anomaly detection 10%
Postgres-specific alerts and ML anomaly detection reduce noise.
Openness and portability 5%
Open-source licensing and self-hosted options avoid lock-in.

Vendor 01 / 10 · #netdata

Netdata

Open-source, real-time monitoring with a zero-configuration PostgreSQL collector, per-second granularity, and ML anomaly detection on every metric.

Best for

Teams that want per-second PostgreSQL metrics with zero configuration and no per-metric or per-GB charges.
SREs who need Postgres correlated with host, container, and network context in one pane.
Fleets of any size that want ML anomaly detection without a usage-based bill.

Pricing

Agent is open source (AGPL) and free; containers on a monitored host are included.
Netdata Cloud Business starts at $4.50/node/month on annual plans, with the per-node price decreasing as node count grows.
Free Community Cloud tier for small fleets.
Bill grows with node count, not with metrics, queries, or data volume.

Pros

Per-second collection by default: the PostgreSQL collector’s update_every is 1 second, versus 15-60 seconds for most tools here.
100+ PostgreSQL metrics across 60+ composite charts: connections, transactions, cache hit ratio, bloat, locks, vacuum, replication, WAL, txid exhaustion.
Zero-configuration auto-discovery: detects the local Postgres service and starts monitoring with the default user, over TCP or UNIX socket.
Works with pg_monitor or pg_read_all_stats roles and detects pg_stat_statements and pg_stat_monitor when present.
Built-in Postgres alerts for connection utilization, lock utilization, txid exhaustion, cache hit ratio, deadlocks, and bloat.
Unsupervised ML anomaly detection on every metric, plus AI-assisted root-cause analysis.

Where teams pair it

No EXPLAIN plan capture or query-plan regression analysis. Teams doing deep query tuning pair it with pganalyze or Datadog DBM.
No index or VACUUM advisor-style recommendations: bloat metrics are surfaced without remediation guidance.
Per-query drill-down is shallower than pganalyze: no workbooks, plan comparison, or query normalization depth.

Verdict

Netdata leads this list because it is the only tool here with per-second collection by default, zero-configuration Postgres discovery, and flat per-node pricing with no usage-based traps. The metric coverage is genuinely deep: 100+ metrics including bloat, vacuum, locks, WAL, and txid exhaustion, with Postgres-specific alerts out of the box. It also correlates database metrics with host, container, and network context, which the purpose-built Postgres tools do not do. The honest caveat: if your primary need is EXPLAIN plan analysis and automated tuning advice, that is pganalyze’s territory, and many teams run both.

Start free -> See database monitoring ->

Vendor 02 / 10 · #pganalyze

pganalyze

Purpose-built PostgreSQL performance platform with automatic EXPLAIN plan collection, index and VACUUM advisors, and log insights.

Best for

DBAs running Postgres at scale who need query-plan analysis and tuning recommendations.
Teams on managed Postgres (RDS, Aurora, Cloud SQL, Azure) that want deep query insight without host access.
Organizations that need SOC 2 Type 2 tooling with SSO and on-premise deployment.

Pricing

Per billable server per month; replicas and readers count as a fraction of a billable server.
Tiers scale by server count, statistics retention, and Log Insights volume.
SaaS or fully self-hosted Enterprise Server; no free tier for production use.
Bill compounds across large fleets as billable servers grow.

Pros

Automatic EXPLAIN plan collection via auto_explain with visualization, plan comparison, and per-query insights.
Index Advisor and VACUUM Advisor give workload-aware, automated tuning recommendations.
Per-query performance history, query normalization, and workbooks for DBA-developer collaboration.
Log Insights with 100+ preconfigured log event filters.
Replication monitoring, wait event monitoring, and connection tracing; SOC 2 Type 2 certified.
Runs as SaaS or fully self-hosted behind your firewall.

Cons

Database-only scope: no host, container, or network monitoring to correlate with.
System-level statistics are unavailable for managed databases without host access.
Per-billable-server pricing grows with fleet size, and there is no free production tier.

Verdict

pganalyze is the reference point for PostgreSQL query-performance depth. Automatic EXPLAIN plans, index and vacuum advisors, and log insights are things no general-purpose platform fully matches. If your problem is slow queries and tuning, this is the tool the others are measured against. The trade-offs are scope and price: it sees only the database, and per-server pricing adds up on big fleets. Pair it with an infrastructure monitor for host context.

Visit pganalyze -> pganalyze pricing ->

Vendor 03 / 10 · #percona-pmm

Percona Monitoring and Management (PMM)

Open-source database observability for MySQL, PostgreSQL, and MongoDB with query analytics, advisors, and Grafana-based dashboards.

Best for

DBAs who want an open-source DBA console with query analytics and no SaaS dependency.
Multi-engine shops running PostgreSQL alongside MySQL and MongoDB.
Teams that want automated security, configuration, and performance checks.

Pricing

Open source (AGPL-3.0), self-hosted: you run and operate PMM Server and clients.
Commercial support subscriptions from Percona are optional.
Cost is the infrastructure and operating time you put into it, not metrics or hosts.

Pros

Query Analytics (QAN) ranks every query by load and drills down from fleet level to a single query.
Deeper PostgreSQL insight through pg_stat_monitor integration.
Percona Advisors run automated security, configuration, and performance checks continuously.
Built on Grafana and VictoriaMetrics with PostgreSQL Overview and Vacuum dashboards.
Fully open source with no vendor lock-in.

Cons

You self-host and operate the PMM Server, which is real operational overhead.
PostgreSQL support is historically less mature than its MySQL support.
No automated EXPLAIN plan regression detection or index advisor.

Verdict

PMM is the strongest open-source DBA console in this comparison. Query Analytics gives you per-query load ranking that most open-source stacks lack, and the advisors automate the checks a senior DBA would run by hand. The cost is operational: you run the server, manage upgrades, and own the stack. For multi-engine shops with the skills to operate it, it is a credible alternative to commercial database monitoring.

Visit Percona PMM -> PMM PostgreSQL monitoring ->

Vendor 04 / 10 · #datadog

Datadog

Full-stack observability platform whose Database Monitoring add-on brings query metrics, explain plans, wait events, and blocking-query insights to PostgreSQL.

Best for

Enterprises standardizing on one platform across infrastructure, APM, logs, and databases.
Teams that want query-level Postgres monitoring correlated with application traces.
Organizations that need 900+ integrations and mature dashboards, alerting, and RBAC.

Pricing

Per-host infrastructure monitoring plus a per-database-host Database Monitoring add-on.
Usage-based charges for logs, spans, and custom metrics stack on top.
SaaS only; no self-hosted option.
Bill grows with hosts, monitored database hosts, and ingested data volume, and cost surprises at scale are a common complaint.

Pros

Database Monitoring covers query-level metrics, live and historical query snapshots, wait events, and blocking-query insights.
Continuous EXPLAIN plan collection with normalized plan comparison.
Direct pivot from database metrics to APM traces for root-cause analysis.
900+ integrations and out-of-the-box health summaries for database fleets.

Cons

Per-host plus per-database-host add-on pricing compounds, with usage-based charges on top.
SaaS only, which rules it out for data-sovereignty requirements.
Postgres-specific tuning depth (index and vacuum advisors) is thinner than pganalyze.

Verdict

Datadog’s Database Monitoring is genuinely capable: explain plans, wait events, and blocking queries, all correlated with traces from the same platform. Capability is not the barrier; cost is. The bill is a stack of per-host, per-database-host, and per-GB line items that grows in several directions at once. If your organization has already standardized on Datadog, the Postgres coverage will not disappoint. If you are choosing fresh, model the bill at your real fleet size first.

Read full Netdata vs Datadog -> Datadog pricing ->

Vendor 05 / 10 · #prometheus-grafana

Prometheus + Grafana (postgres_exporter)

The default open-source metrics stack: the Prometheus PostgreSQL exporter feeding Grafana dashboards you build and operate yourself.

Best for

Teams already running Prometheus or Kubernetes who want Postgres metrics in the existing stack.
Engineers who want full control over dashboards, alerting, and retention.
OSS-first shops that accept the operating burden.

Pricing

Open source, self-hosted: you run Prometheus, the exporter, and Grafana.
Grafana Cloud offers a free tier and paid plans if you want hosted dashboards.
Cost is the storage and compute you provision for time-series data, plus operating time.

Pros

postgres_exporter exposes 20+ built-in metric groups from pg_stat* views and pg_stat_statements: replication, locks, WAL, autovacuum.
Custom metrics via YAML query files and multi-target probing of many instances from one exporter.
Large ecosystem of community PostgreSQL dashboards and PromQL alerting.
Apache-2.0 licensed exporter, CI-tested against PostgreSQL 13 through 18.

Cons

DIY assembly: you build, scale, and maintain every component yourself.
Granularity is whatever scrape interval you configure, commonly 15 seconds, not per-second.
No query-plan analysis, index advisor, or Postgres-specific recommendations.
Metrics only: logs and traces need separate components.

Verdict

Prometheus plus Grafana is the default open-source answer, and for teams already running the stack it is a reasonable one. The exporter covers the important pg_stat* views and the dashboard ecosystem is deep. What you give up is depth and time: there is no query-plan analysis, granularity is whatever you configure, and operating a Prometheus stack at scale is a job in itself. Go in with open eyes about the operating cost.

Read full Netdata vs Prometheus -> Grafana pricing ->

Vendor 06 / 10 · #new-relic

New Relic

Full-stack observability platform with an on-host PostgreSQL integration covering instance, database, table, and index metrics plus query performance monitoring.

Best for

Teams already on New Relic APM who want Postgres metrics in the same platform.
Organizations that prefer usage-based pricing over per-host pricing.
Full-stack correlation of database metrics with application traces.

Pricing

Usage-based: per-user seats plus per-GB data ingest.
Free tier with a monthly data-ingest allowance.
No per-host charge; unlimited hosts and agents included.
Bill grows with users and ingested data volume, which can be unpredictable.

Pros

On-host integration collects instance, database, table, index, and PgBouncer metrics including bloat, vacuum, and replication.
Query performance monitoring: slow queries, wait events, blocking sessions, and execution-plan metrics via pg_stat_statements.
Custom SQL metrics through configuration.
15-second default collection interval for the integration.

Cons

Query monitoring requires enabling pg_stat_statements and granting pg_read_all_stats.
Usage-based pricing can grow unpredictably with data volume.
No index or VACUUM advisor-style recommendations for Postgres.

Verdict

New Relic’s Postgres coverage is better than its generic reputation suggests: slow queries, wait events, and execution-plan metrics at a 15-second default interval, correlated with APM traces. It sits below Datadog here mainly on the thinner database-specific feature set. The pricing shape is the watch-out: seats plus ingest is flexible at small scale and hard to forecast at large scale. No Postgres tuning advisors, so pair it accordingly if you need them.

Read full Netdata vs New Relic -> New Relic pricing ->

Vendor 07 / 10 · #pgwatch

pgwatch (CYBERTEC)

Open-source, PostgreSQL-specific metrics monitoring and dashboarding built on Grafana, the actively developed successor to pgwatch2.

Best for

Postgres-only shops that want lightweight open-source monitoring with Grafana dashboards.
Teams monitoring many databases with minimal resources.
Users of PgBouncer, Pgpool-II, RDS, or Patroni who want coverage out of the box.

Pricing

Open source (BSD 3-Clause), self-hosted: you run the collector, metrics store, and Grafana.
Optional commercial support plans from CYBERTEC.
Cost is the infrastructure you provision and the time to operate it.

Pros

Postgres-specific: metrics are SQL queries over the statistics collector, with no extensions or superuser rights required for base functionality.
Very low footprint: roughly one CPU core handles thousands of monitored databases at 1GB RAM.
Storage backends include PostgreSQL, TimescaleDB, InfluxDB, and Prometheus; Helm chart for Kubernetes.
Active successor to pgwatch2, which was archived in December 2024.

Cons

No alerting engine of its own; alerting relies on Grafana or external systems.
No EXPLAIN plan analysis or automated tuning recommendations.
Smaller community and ecosystem than Prometheus or Zabbix.

Verdict

pgwatch is the most resource-efficient Postgres-specific open-source option here, and its SQL-defined metrics mean you can extend it without writing exporter code. The Grafana dependency cuts both ways: good dashboards, but no native alerting engine. It is a solid pick for Postgres-only fleets on a budget, as long as someone on the team is comfortable operating the collector and metrics store.

pgwatch on GitHub -> CYBERTEC pgwatch page ->

Vendor 08 / 10 · #zabbix

Zabbix

Open-source infrastructure monitoring platform with built-in PostgreSQL templates for agent-based and agentless ODBC collection.

Best for

IT teams already standardizing on Zabbix for infrastructure monitoring.
Organizations that want agent-based or agentless ODBC checks against Postgres.
On-premises-first shops that need mature alerting and trigger logic.

Pricing

Open source (AGPLv3 since version 7.0), self-hosted.
Paid support and services are optional.
Cost is the infrastructure you run it on and the Zabbix expertise to maintain it.

Pros

Built-in PostgreSQL templates in three variants: Zabbix agent, agent 2 native plugin, and agentless ODBC.
Broad metric coverage: connections by state, transactions, cache hit ratio, replication lag, WAL, locks, bloat, autovacuum, txid age.
Low-level discovery generates per-database and per-application items automatically.
Mature trigger and alerting engine with templates maintained across Zabbix 5.0 through 7.4.

Cons

No query-level analysis: no pg_stat_statements drill-down, no explain plans, no slow-query ranking.
Default polling intervals are tens of seconds to minutes, not per-second.
Template tuning and setup require Zabbix expertise.

Verdict

If your organization already runs Zabbix, its PostgreSQL templates give you broad metric coverage and battle-tested alerting without adding another platform. What you will not get is query-level depth: no explain plans, no slow-query ranking, and polling intervals measured in tens of seconds at best. It monitors the health of the database well; it will not help you tune a slow query. That is a fair trade for infrastructure teams, less so for DBAs.

Read full Netdata vs Zabbix -> Zabbix pricing ->

Vendor 09 / 10 · #solarwinds-dpm

SolarWinds Database Performance Monitor (DPM)

Enterprise database performance monitoring and query tuning across PostgreSQL, Oracle, SQL Server, MySQL, and MongoDB, with agentless collection.

Best for

Enterprise DBAs who want agentless query analysis across multiple database engines.
Teams already standardized on SolarWinds tooling.
Organizations that need execution plans and tuning advisors without installing agents.

Pricing

Quote-based; no public pricing, free trial available.
Bill grows with the number of monitored database instances.
Available as self-hosted DPA or within the SolarWinds Observability SaaS.

Pros

Agentless collection with low overhead (1% or less) and quick time-to-value.
Query performance monitoring, execution-plan analysis, and tuning advisors.
Runs on Windows or Linux, in Azure, or as an AWS subscription; SaaS option exists.
One tool covers multiple database engines.

Cons

No public pricing; enterprise quote-based and historically expensive.
Postgres-specific depth is less documented than for Oracle and SQL Server.
Heavier product than a small Postgres fleet needs.

Verdict

SolarWinds DPM (Database Performance Analyzer) is an enterprise DBA tool that treats Postgres as one engine among several. The agentless collection and tuning advisors are real strengths for mixed-database enterprises, and the SolarWinds estate integration matters if you are already there. For Postgres-only teams, pganalyze goes deeper and publishes its pricing. The quote-based model means you will not know the cost until you talk to sales.

Read full Netdata vs SolarWinds -> SolarWinds DPM page ->

Vendor 10 / 10 · #sematext

Sematext

Monitoring and log management platform with a dedicated PostgreSQL integration covering metrics, slow-query logs, and built-in dashboards.

Best for

Teams that want Postgres metrics and logs in one tool without full-stack platform cost.
Small-to-mid-size organizations that want built-in dashboards and slow-query analysis.
Users who value metrics-logs correlation for troubleshooting.

Pricing

Per-host infrastructure monitoring plus separate log management pricing.
Free trial and a limited free tier.
Bill grows with number of hosts and log volume.

Pros

Agent collects Postgres metrics across connections, transactions, rows, indexes, functions, replication delay, and table/index sizes.
Log integration parses slow queries and errors; Split Screen correlates metrics with logs.
Built-in Postgres dashboards plus OS-level context.
Anomaly detection available on higher plans.

Cons

No EXPLAIN plan analysis or query-plan comparison.
Limited support for custom metrics from some Postgres extensions.
Smaller ecosystem and integration catalog than Datadog or New Relic.

Verdict

Sematext rounds out the list as a pragmatic metrics-plus-logs option for Postgres at moderate cost. The slow-query log parsing and metrics-logs correlation are genuinely useful for troubleshooting without a full APM bill. What it lacks is query-plan depth and the breadth of the larger platforms. For smaller fleets that want Postgres coverage without enterprise pricing complexity, it is worth a trial.

Sematext PostgreSQL docs -> Sematext pricing ->

Frequently Asked Questions

What is the best way to monitor PostgreSQL performance?

Start with PostgreSQL’s own statistics: pg_stat_database, pg_stat_statements, and pg_stat_replication. Then decide whether you need metric-level monitoring (connections, cache hit ratio, bloat) or query-level analysis (EXPLAIN plans, slow queries), because those point to different tools. Collection interval matters more than most buyers expect: per-second tools catch short spikes and lock storms that 15-60 second polling averages away. Zero-configuration tools like Netdata auto-discover Postgres and start collecting immediately; DIY stacks like Prometheus require assembly but give you full control.

What PostgreSQL metrics should I monitor?

At minimum: connection utilization versus max_connections, cache hit ratio (buffer hits versus reads), transactions with commits, rollbacks, and deadlocks, replication lag and WAL generation/archiving, table and index bloat with vacuum and autovacuum activity, transaction ID exhaustion (wraparound) percentage, and temp file creation with lock waits. Tools like Netdata ship alerts for most of these out of the box; with template-based tools you configure them yourself.

Is Prometheus + Grafana enough for PostgreSQL monitoring?

For metric collection, mostly yes: postgres_exporter covers the pg_stat* views and pg_stat_statements. The caveats are operational and analytical. You run and maintain Prometheus, the exporter, and the dashboards, and your granularity is whatever scrape interval you configure, commonly 15 seconds. The stack has no query-plan analysis, no index or vacuum advisors, and no Postgres-specific recommendations. When you need EXPLAIN plans or automated tuning advice, add a purpose-built tool.

How much does PostgreSQL monitoring cost?

PostgreSQL monitoring ranges from free with open-source tools to $4.50/node/month for Netdata Cloud Business and higher for enterprise platforms like SolarWinds DPM, which requires a custom quote. Pricing shapes differ more than headline numbers: flat per-node (Netdata), per billable server (pganalyze), per-host plus a database add-on (Datadog), and usage-based seats plus ingest (New Relic). Open-source tools like PMM, Prometheus+Grafana, pgwatch, Zabbix, and the Netdata agent are free to download, but you pay to run and operate them. Watch for usage-based traps: per-GB ingest, per-metric, and per-host add-ons all grow with data volume.

Do I need pg_stat_statements enabled for PostgreSQL monitoring?

Only if you need query-level statistics. Query-level tools (Datadog DBM, New Relic query monitoring, pganalyze) require pg_stat_statements to see per-query execution statistics, while metric-level tools work from pg_stat_database and related views without it. Netdata’s collector detects pg_stat_statements and pg_stat_monitor when present and uses them for query statistics automatically. Percona’s pg_stat_monitor adds application name, client IP, and query classification columns where available.

Can I monitor Amazon RDS or Aurora PostgreSQL?

Yes. Netdata connects over TCP, pganalyze officially supports RDS and Aurora, and Datadog DBM, New Relic, PMM, and pgwatch all support managed Postgres. The limitation is system-level statistics: host CPU, memory, and I/O views are unavailable unless the provider exposes them, and pganalyze notes that Log Insights depends on the provider exporting logs via syslog. AWS RDS Performance Insights is an option, but it locks you into the AWS ecosystem and lacks the depth of standalone tools.

What is the difference between PostgreSQL monitoring and query performance analysis?

Monitoring tracks health metrics: connections, throughput, cache hit ratio, replication lag, bloat, vacuum. Query performance analysis digs into individual queries: EXPLAIN plans, plan regressions, missing indexes, and tuning recommendations. pganalyze and Datadog DBM specialize in the latter; Netdata, Zabbix, and pgwatch focus on the former. Most teams need both: metrics to detect problems, and query analysis to fix them.

Is there a free open-source PostgreSQL monitoring tool?

Yes: Percona PMM, Prometheus+Grafana with postgres_exporter, pgwatch, Zabbix, and the Netdata agent are all open source. Open source is a distribution model, not a price: you run and operate these tools yourself, and the operating cost is real. Netdata’s agent is open source (AGPL) and free, with a free Cloud tier for small fleets. Commercial support is available for most of them if you need it.

How often should PostgreSQL metrics be collected?

Per-second collection (Netdata’s default) captures short-lived spikes, lock storms, and connection bursts that 15-60 second polling misses. Common defaults elsewhere: 15 seconds for the New Relic integration, 60 seconds for the SigNoz OTel receiver, and whatever scrape interval you set for Prometheus. Coarse intervals are fine for capacity planning but dangerous for incident response. PostgreSQL’s statistics views are cumulative counters, so the collection interval determines how precisely you can see rates and deltas.

The 10 best PostgreSQL monitoring tools, ranked

How we evaluated PostgreSQL monitoring tools

Netdata

pganalyze

Percona Monitoring and Management (PMM)

Datadog

Prometheus + Grafana (postgres_exporter)

New Relic

pgwatch (CYBERTEC)

Zabbix

SolarWinds Database Performance Monitor (DPM)

Sematext

Frequently Asked Questions

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