# Best PostgreSQL Query Optimization and Performance Tuning Tools in 2026 Slow queries are the single most common cause of PostgreSQL performance problems. A single unoptimized query -- a sequential scan on a multi-million-row table, a nested loop join with no supporting index, or a correlated subquery that executes once per row -- can consume 80% of your database's CPU and I/O budget, degrading every other operation running on the same instance. The problem compounds in production: query plans shift as data distributions change, new application features introduce unanticipated access patterns, and autovacuum falls behind on high-churn tables, causing the planner to make progressively worse decisions. Finding the slow query is only the first step. The harder question is understanding *why* it is slow. Is the planner choosing a sequential scan because statistics are stale, or because the table is small enough that a seq scan genuinely wins? Did a plan regression happen after a PostgreSQL minor version upgrade changed a cost constant? Is the query waiting on a lock held by an autovacuum worker, or is it genuinely I/O-bound? The right tool does not just surface the problem -- it explains the root cause, shows you how execution plans have changed over time, recommends specific indexes with ready-to-run DDL, and tells you exactly which configuration knobs to turn. This article compares the ten best tools for PostgreSQL query optimization in 2026, ranked by the depth and breadth of their query-level analysis capabilities. We focus specifically on what matters for finding and fixing slow queries: EXPLAIN plan collection, index recommendations, plan regression detection, wait event analysis, lock diagnostics, and actionable remediation. Whether you are a solo DBA managing a handful of instances or a platform team running hundreds of PostgreSQL clusters, this guide will help you choose the right tool for the job. * * * ## **1\.** [**myDBA.dev**](http://myDBA.dev) [myDBA.dev](http://myDBA.dev) is a purpose-built PostgreSQL performance monitoring platform designed around one core principle: every performance insight should come with a ready-to-run SQL fix. Where most monitoring tools stop at showing you dashboards and graphs, [myDBA.dev](http://myDBA.dev) goes further by providing the complete query optimization workflow -- from identifying the slowest queries to collecting their execution plans automatically, detecting when plans regress, recommending indexes, and generating the exact DDL statements to fix the problem. The platform uses a lightweight Go collector that connects directly to your PostgreSQL instances, gathering metrics at three tiers (15-second, 60-second, and 5-minute intervals). There is no application server sitting between your data and the dashboard -- the frontend reads directly from the analytics database via PostgREST, which means sub-second page loads even when you are analyzing thousands of query templates across dozens of connections. ### **Query Optimization Capabilities** * **Query Performance Tracking**: Full `pg_stat_statements` integration tracks every query template with execution time, call frequency, rows returned, and I/O consumption. When `pg_stat_kcache` is available, [myDBA.dev](http://myDBA.dev) breaks down resource usage into CPU time vs. I/O time, letting you immediately distinguish between compute-bound and storage-bound queries. Trend sparklines show how each query's performance has evolved over hours, days, or weeks. Statement type filtering (SELECT, INSERT, UPDATE, DELETE) with sortable column headers makes it straightforward to isolate write-heavy workloads from read-heavy ones. * **Automatic EXPLAIN Plan Collection**: The collector automatically runs EXPLAIN on the top 50 slowest queries every collection cycle (every 5 minutes). On PostgreSQL 16 and later, it uses `GENERIC_PLAN` mode for safe plan capture that does not execute the query or require parameter values. The visual rendering engine provides four complementary views: an interactive tree view showing node-by-node costs, a flame graph for identifying the widest execution bottlenecks, a timing waterfall for understanding sequential vs. parallel execution, and a bottleneck bar analysis that highlights the single most expensive operation in the plan. Even without an account, you can paste any EXPLAIN output into the free browser-based visualizer and get full interactive rendering with specific SQL recommendations. * **EXPLAIN ANALYZE Support**: For connections where you need actual execution statistics (not just estimates), [myDBA.dev](http://myDBA.dev) offers a per-connection toggle that enables EXPLAIN ANALYZE collection. This gives you real row counts, actual loop iterations, and true I/O timing -- critical for diagnosing cases where the planner's row estimates are wildly wrong. * **Plan Regression Detection**: This is where [myDBA.dev](http://myDBA.dev) stands apart. The platform tracks query plan fingerprints over time and detects the moment a plan changes. When a previously fast query suddenly switches from an index scan to a sequential scan -- whether due to stale statistics, a PostgreSQL upgrade, or data growth crossing a tipping point -- [myDBA.dev](http://myDBA.dev) flags the regression and shows you a side-by-side comparison of the old and new plans. Plan source badges indicate where each captured plan came from (auto\_explain, manual EXPLAIN, or collector), so you know the provenance of every plan in the history. No other tool in this list provides continuous plan change tracking with automated regression detection. * **auto\_explain Intelligence**: When `auto_explain` is enabled on your PostgreSQL instance, [myDBA.dev](http://myDBA.dev) detects it automatically, understands the plan data level being captured, and implements smart skipping to avoid collecting duplicate plans that auto\_explain has already captured. This means you get the best of both worlds: auto\_explain captures plans for queries that exceed your duration threshold, and [myDBA.dev](http://myDBA.dev) fills in the gaps for the top queries that auto\_explain misses. * **Cluster-Aware Index Advisor**: The index advisor identifies missing indexes by analyzing sequential scan patterns, detects unused indexes consuming disk space and slowing writes, and finds duplicate indexes that serve the same queries. What makes it unique is cluster-aware analysis: it aggregates index usage statistics across primaries AND their replicas before recommending an index drop. This prevents the dangerous scenario where you drop an index on the primary that read replicas depend on for their query workload. Every recommendation includes ready-to-execute `CREATE INDEX` or `DROP INDEX` statements. * **Wait Event Heatmaps**: A visual heatmap breaks down database wait events by category -- I/O waits, lock waits, CPU waits, and internal waits -- across a configurable time range. You can drill through from any wait event category directly to the specific queries causing that wait type, closing the loop between "the database is waiting on I/O" and "these three queries are responsible." * **Lock Chain Visualization**: When queries are blocked, [myDBA.dev](http://myDBA.dev) renders the full blocking dependency graph as an interactive chain diagram. You can trace from the root blocker (often an idle-in-transaction session holding an `AccessExclusiveLock`) through every intermediate blocked query to the leaf sessions waiting at the end of the chain. This turns lock debugging from a manual exercise in joining `pg_locks` with `pg_stat_activity` into a single visual. * **Query Sample Parameters**: The platform extracts actual parameter values from PostgreSQL log entries, substitutes `$N` placeholders in normalized query templates, and generates fully executable sample queries. This means you can take a slow query directly from the dashboard, paste it into `psql`, and run it with the exact parameters that caused the performance problem in production. * **Function Monitoring**: PL/pgSQL and SQL function performance is tracked with call counts, average duration, and self-time metrics, so you can identify slow functions that hide expensive queries behind a single function call. * **75+ Health Checks with SQL Fixes**: Many of the built-in health checks are directly query-performance focused -- identifying missing indexes on foreign keys, bloated tables causing slow sequential scans, suboptimal `work_mem` settings causing disk-based sorts, autovacuum falling behind on high-churn tables, and dozens more. Every finding includes an explanation of the performance impact and a ready-to-run SQL fix script. ### **What Could Be Better** The managed collector fleet is currently limited to specific cloud regions. On-premises deployment requires running the Go collector binary as a systemd service, which is straightforward but not a one-click Docker setup. ### **Pricing** Free tier includes full query tracking, automatic EXPLAIN collection, the index advisor, and all health checks for one connection with 7 days of retention. Pro tier expands to unlimited connections, 30-day retention, and alerting. ### **Verdict** [myDBA.dev](http://myDBA.dev) provides the most complete query optimization toolkit available for PostgreSQL. The combination of automatic EXPLAIN collection, plan regression detection, cluster-aware index advice, wait event heatmaps, and lock chain visualization -- all with ready-to-run SQL fixes -- makes it the strongest choice for DBAs and developers who need to systematically find and fix slow queries. * * * ## **2\. Percona Monitoring and Management (PMM)** Percona PMM is an open-source, self-hosted monitoring platform with strong PostgreSQL support. Its Query Analytics (QAN) module is one of the more capable free options for query-level performance analysis. PMM integrates with `pg_stat_monitor` (Percona's enhanced replacement for `pg_stat_statements`) to provide time-bucketed query statistics, which gives you better granularity into when slowdowns occurred. ### **Query Optimization Capabilities** * Query Analytics (QAN) with per-query execution time, rows examined, and response time histograms * `pg_stat_monitor` integration for time-bucketed query stats and actual parameter capture * EXPLAIN plan collection for individual queries (manual trigger, not automatic) * Table-level statistics showing sequential scan ratios and index usage * Query filtering by database, schema, client host, and username * Built on Grafana with customizable dashboards for query metrics ### **What's Missing** No automatic EXPLAIN plan collection -- you must manually trigger EXPLAIN for individual queries. No plan regression detection or plan history tracking. No index advisor or index recommendation engine. No lock chain visualization. No wait event heatmaps with query drill-through. Setup is complex: you need to deploy the PMM server (Docker or VM), install PMM agents on each monitored host, and configure `pg_stat_monitor` separately. ### **Pricing** Completely free and open source. Percona offers paid support contracts for enterprise deployments. ### **Verdict** PMM is the best free option for query-level PostgreSQL monitoring. The QAN module is genuinely useful for identifying slow queries, and `pg_stat_monitor` provides better time-bucketed statistics than vanilla `pg_stat_statements`. However, the lack of automatic EXPLAIN collection, plan regression tracking, and an index advisor means you are still doing significant manual investigation work once you identify the slow query. * * * ## **3\. pgBadger** pgBadger is a mature, open-source log analysis tool that parses PostgreSQL log files and generates detailed HTML reports with query-level statistics. It has been a staple in the PostgreSQL ecosystem for over a decade, and for batch analysis of historical query performance from log data, it remains excellent. ### **Query Optimization Capabilities** * Parses `log_min_duration_statement` output to identify slow queries with full SQL text * Top-N slowest queries ranked by total duration, average duration, and call count * Query normalization to group similar queries into templates * Per-hour and per-minute query traffic histograms * Lock analysis from log entries (deadlocks, lock waits) * Temporary file usage tracking to identify queries spilling to disk * Checkpoint and autovacuum activity correlation with query performance * Parallel log processing for fast analysis of multi-gigabyte log files ### **What's Missing** pgBadger is a batch reporting tool, not a real-time monitoring system. There is no live dashboard, no alerting, no EXPLAIN plan analysis, no index advisor, and no plan regression detection. You run it periodically (typically via cron) and review the HTML output. It also requires that your PostgreSQL logging is configured with a compatible `log_line_prefix` and that you have `log_min_duration_statement` set to capture the queries you care about. It cannot analyze queries that are fast individually but slow in aggregate (high-frequency queries with moderate per-call cost). ### **Pricing** Free and open source (BSD license). ### **Verdict** pgBadger is a zero-cost, zero-dependency way to get a comprehensive picture of your slow query landscape from log data. It is an excellent complement to real-time monitoring tools. However, it cannot replace a proper query optimization platform -- it tells you which queries were slow but does not explain why or suggest how to fix them. * * * ## **4\. pg\_stat\_monitor** `pg_stat_monitor` is Percona's drop-in replacement for `pg_stat_statements`. It is not a standalone tool but a PostgreSQL extension that provides significantly richer query statistics than the built-in extension. It is included here because it is a foundational component that several monitoring tools build on top of. ### **Query Optimization Capabilities** * Time-bucketed query statistics (configurable bucket size) instead of cumulative-only stats * Actual query parameter capture (not just normalized templates) * Per-query histograms showing response time distribution * Client application name and IP tracking per query * Top query and slow query tracking with configurable thresholds * Query plan text capture (the plan used for the first execution in each bucket) * Relations (tables) accessed per query for cross-referencing with index usage ### **What's Missing** `pg_stat_monitor` is an extension, not a tool. It has no UI, no dashboards, no visualization, no alerting, and no recommendations. You need a separate tool (PMM, custom Grafana dashboards, or your own scripts) to actually consume and display the data it collects. It also requires installation as a shared preload library, which means a PostgreSQL restart, and it is not available on all managed PostgreSQL services. ### **Pricing** Free and open source (Percona, distributed under the PostgreSQL license). ### **Verdict** If you are building your own monitoring stack or using PMM, `pg_stat_monitor` is a strictly superior replacement for `pg_stat_statements`. The time-bucketed statistics and parameter capture are genuinely useful features. But on its own, it is a data source, not a query optimization tool. * * * ## **5\. pgDash** pgDash is a commercial PostgreSQL monitoring service that provides a clean, focused dashboard with decent query-level diagnostics. It positions itself as a simpler alternative to full-stack monitoring platforms, concentrating exclusively on PostgreSQL. ### **Query Optimization Capabilities** * `pg_stat_statements` integration with query execution trends * EXPLAIN plan visualization for individual queries (manual trigger) * Query plan display with node-level cost breakdown * Table bloat detection and index bloat analysis * Replication lag monitoring with per-replica metrics * System-level metrics (CPU, memory, disk I/O) correlated with query activity * SQL-level diagnostics for common performance issues ### **What's Missing** No automatic EXPLAIN plan collection -- plans are collected on-demand only. No plan regression detection or plan history. No index advisor that generates `CREATE INDEX` recommendations. No wait event heatmaps. No lock chain visualization. No query parameter extraction. The diagnostic capabilities are informative but do not include ready-to-run SQL fix scripts. ### **Pricing** Starts at $49/month for up to 5 monitored servers. Higher tiers for more servers and longer data retention. ### **Verdict** pgDash is a solid, straightforward PostgreSQL monitoring tool with a clean interface. Its query diagnostics are adequate for basic slow query identification, but it lacks the depth needed for systematic query optimization -- no automatic EXPLAIN, no plan tracking, no index advisor, and no remediation scripts. * * * ## **6\. pganalyze** pganalyze is a well-established PostgreSQL monitoring platform known for its Index Advisor and VACUUM Advisor. It provides query-level analysis with some unique features around index simulation and vacuum tuning, but has notable gaps in plan regression tracking and lock diagnostics. ### **Query Optimization Capabilities** * `pg_stat_statements` integration with query performance trends and per-query sparklines * Log-based EXPLAIN plan collection (requires `auto_explain` configuration) * Index Advisor with a "What If?" simulation engine that estimates performance improvement before you create an index * VACUUM Advisor with a simulator that models autovacuum behavior and recommends per-table tuning parameters * Wait event analysis with categorization by event type * Connection pooling analysis (PgBouncer integration) * Schema statistics and table-level I/O tracking * Automated query performance alerts with Slack and PagerDuty integrations ### **What's Missing** No plan regression detection -- pganalyze collects EXPLAIN plans but does not track plan fingerprints over time or alert when a plan changes. No cluster-aware index analysis -- index recommendations do not consider replica workloads, so dropping a "unused" index on the primary may break read-replica queries. No lock chain visualization for debugging blocking dependency graphs. No wait event heatmaps with drill-through to causing queries. No query parameter extraction for generating executable sample queries. The EXPLAIN visualization is functional but does not include flame graph or bottleneck bar views. ### **Pricing** Starts at $149/month for the Accelerate plan (10 servers). The Scale plan at $399/month adds the Index Advisor "What If?" engine, extended retention, and priority support. There is a free tier, but it does not include the Index Advisor. This makes pganalyze one of the more expensive options in this list. ### **Verdict** pganalyze has a genuinely useful Index Advisor (especially the "What If?" simulation at the Scale tier) and the VACUUM Advisor fills a real gap that most tools ignore. However, the lack of plan regression detection, cluster-aware index analysis, lock chain visualization, and the high price point ($149-$399/month) make it a good but incomplete query optimization solution. If your primary pain point is identifying which indexes to create and tuning autovacuum, pganalyze delivers. If you need the full optimization picture -- plan regressions, lock debugging, wait event correlation, and actionable SQL fixes -- it falls short. * * * ## **7\. Datadog Database Monitoring** Datadog's Database Monitoring module brings query-level metrics into its broader full-stack APM platform. For teams already using Datadog for application and infrastructure monitoring, adding PostgreSQL query analysis integrates naturally into existing workflows. ### **Query Optimization Capabilities** * `pg_stat_statements` integration with query execution metrics normalized per second * Automatic EXPLAIN plan collection for sampled queries * Query samples with actual parameters from `pg_stat_activity` * Trace-to-query correlation linking application spans to database queries * Wait event categorization with time-series views * Active sessions breakdown by query, wait event, and user * Database host-level metrics (CPU, IOPS, connections) correlated with query patterns * Alerting on query latency thresholds with Datadog's full notification ecosystem ### **What's Missing** No plan regression detection -- plans are collected but not tracked for changes over time. No index advisor or index recommendation engine. No lock chain visualization. No PostgreSQL-specific health checks or configuration recommendations. Query analysis depth is limited compared to PostgreSQL-dedicated tools because Datadog must support dozens of database engines with a common interface. EXPLAIN plans are sampled, not comprehensive -- you may not get a plan for every slow query. The PostgreSQL-specific optimization intelligence is shallow: Datadog shows you the data but expects you to interpret it yourself. ### **Pricing** Database Monitoring is $70/host/month on top of base Datadog pricing. For a team already paying for Datadog APM, adding database monitoring is incremental. For a team evaluating Datadog solely for PostgreSQL query optimization, the total cost (APM + DBM + infrastructure) is significant. ### **Verdict** Datadog Database Monitoring is the right choice if your team is already invested in the Datadog ecosystem and you want unified trace-to-query correlation. The application-level context is genuinely valuable for understanding how slow queries impact end-user experience. However, as a PostgreSQL query optimization tool specifically, it lacks the depth of dedicated platforms -- no index advisor, no plan regression tracking, and no PostgreSQL-specific remediation guidance. * * * ## **8\. pgHero** pgHero is an open-source, lightweight PostgreSQL performance dashboard originally built by Instacart. It provides a quick overview of slow queries, missing indexes, and basic database health with minimal setup -- either as a standalone web app or a Rails engine. ### **Query Optimization Capabilities** * Slow query identification from `pg_stat_statements` with total time ranking * Missing index detection based on sequential scan analysis * Unused index detection with drop recommendations * Duplicate index identification * Table and index bloat estimation * Active query listing from `pg_stat_activity` * Connection count monitoring * Basic EXPLAIN for individual queries (manual) ### **What's Missing** No automatic EXPLAIN plan collection. No plan history or plan regression detection. Missing index suggestions are basic (sequential-scan based heuristics) and do not include multi-column index recommendations or "What If?" simulation. No wait event analysis. No lock chain visualization. No query parameter extraction. No health checks beyond the basics. No alerting. The interface, while clean, does not support time-range analysis -- you see current state only, not trends over time. ### **Pricing** Free and open source (MIT license). ### **Verdict** pgHero is an excellent starting point for teams that need quick visibility into slow queries and obvious missing indexes without any cost or complex setup. It is not a query optimization platform -- it is a lightweight health check dashboard. Use it to get oriented, then graduate to a more capable tool when you need EXPLAIN analysis, plan regression tracking, or systematic index optimization. * * * ## **9\. AWS RDS Performance Insights** RDS Performance Insights is Amazon's native database performance monitoring tool, available for RDS and Aurora PostgreSQL instances. Its core strength is the Average Active Sessions (AAS) visualization, which provides an intuitive view of database load decomposed by wait events. ### **Query Optimization Capabilities** * Average Active Sessions (AAS) chart with wait event decomposition -- the single best visualization of database load available on any tool * Wait event analysis broken down by I/O, lock, CPU, LWLock, and other PostgreSQL wait event types * Top SQL queries ranked by database load (AAS contribution) * Per-query wait event breakdown showing which waits each query experiences * Counter metrics for `pg_stat_statements` statistics * Dimension filtering by SQL, wait event, user, host, and database * Up to 2 years of performance data retention (paid tier) ### **What's Missing** No EXPLAIN plan collection or visualization whatsoever -- this is the most significant gap. No index advisor or index recommendations. No plan regression detection. No lock chain visualization (you can see lock waits but not the dependency graph). No query parameter extraction. No health checks or configuration recommendations. AWS-only -- not available for self-hosted PostgreSQL, Azure, or GCP. The free tier retains only 7 days of data. Performance Insights shows you that a query is slow and what it is waiting on, but does not tell you how to fix it. ### **Pricing** Free tier: 7-day retention, included with RDS/Aurora instances. Long-term retention (up to 2 years): $0 for t-class instances; for other instance types, pricing varies by instance size. ### **Verdict** If you are running PostgreSQL on AWS RDS or Aurora, Performance Insights is a must-enable feature -- the AAS visualization with wait event decomposition is genuinely excellent for understanding *what* your database is spending time on. However, it is not a query optimization tool. It tells you which queries are consuming resources and what they are waiting on, but provides zero guidance on how to fix them. You will need a complementary tool for EXPLAIN analysis, index recommendations, and remediation. * * * ## **10\. Grafana + Prometheus (with postgres\_exporter)** The Grafana + Prometheus stack with `postgres_exporter` is the default choice for teams that want open-source, self-hosted PostgreSQL monitoring integrated into their existing observability infrastructure. It provides query-level metrics through `pg_stat_statements` and broad database health dashboards. ### **Query Optimization Capabilities** * `pg_stat_statements` metrics via `postgres_exporter` including per-query call count, total time, rows, and block I/O * Customizable Grafana dashboards with community templates for PostgreSQL * Flexible alerting via Grafana Alerting or Prometheus Alertmanager * Long-term metric retention with Prometheus TSDB or remote storage (Thanos, Mimir) * Correlation with application metrics, infrastructure metrics, and logs in a single Grafana instance * Table-level metrics (seq scans, index scans, live/dead tuples) for identifying optimization targets ### **What's Missing** No EXPLAIN plan collection, visualization, or analysis of any kind. No plan regression detection. No index advisor or index recommendations. No lock chain visualization. No wait event heatmaps (wait events require custom exporters and significant configuration). No query parameter extraction. No health checks or configuration recommendations. No SQL fix generation. The stack gives you time-series metrics and dashboards, but all query-level intelligence must be built by your team -- writing PromQL queries, building custom dashboards, and developing your own runbooks for every issue you discover. ### **Pricing** Completely free and open source. Grafana Cloud offers a managed version starting at $0 for small workloads, with paid plans based on metrics volume. ### **Verdict** Grafana + Prometheus is the right foundation for PostgreSQL metric collection and visualization, especially if you are already running the stack for other services. But calling it a "query optimization tool" is a stretch. It shows you metrics -- you provide all the intelligence. For teams with deep PostgreSQL expertise who want full control over their monitoring stack, it works. For teams that need guidance on what to fix and how to fix it, it will leave you on your own. * * * ## **Comparison Table: Query Optimization Features**

Feature

myDBA.dev

PMM

pgBadger

pg_stat_monitor

pgDash

pganalyze

Datadog

pgHero

RDS PI

Grafana

Query tracking

Yes

Yes

Yes (batch)

Yes (extension)

Yes

Yes

Yes

Yes

Yes

Yes

EXPLAIN auto-collection

Yes (top 50)

No

No

No

No

Via auto_explain

Sampled

No

No

No

EXPLAIN visualization

Tree, flame, waterfall, bottleneck

Basic

No

No

Basic

Basic

Basic

Basic

No

No

Plan regression detection

Yes

No

No

No

No

No

No

No

No

No

Index advisor

Yes (cluster-aware)

No

No

No

No

Yes ("What If?")

No

Basic hints

No

No

Cluster-aware index analysis

Yes

No

No

No

No

No

No

No

No

No

Wait event analysis

Heatmap + drill-through

Basic

No

No

Basic

Yes

Yes

No

Yes (excellent)

No

Lock chain visualization

Yes

No

From logs

No

No

No

No

No

No

No

Parameter extraction

Yes (executable samples)

Via pg_stat_monitor

From logs

Yes (extension)

No

No

Yes (sampled)

No

No

No

Function monitoring

Yes

Basic

Yes

No

No

Yes

No

No

No

Via exporter

Health checks with SQL fixes

75+ checks

No

No

No

Basic

VACUUM Advisor

No

Basic

No

No

Free tier

Yes (1 conn, 7 days)

Yes (self-hosted)

Yes (open source)

Yes (open source)

No

Limited (no Index Advisor)

No

Yes (open source)

Yes (7-day retention)

Yes (open source)

* * * ## **Choosing the Right Query Optimization Tool** The right tool depends on where you are in your PostgreSQL optimization journey and what infrastructure you already have in place. **If you need a complete query optimization workflow** -- from identifying slow queries to understanding why they are slow, tracking plan regressions, getting index recommendations, and receiving ready-to-run SQL fixes -- [**myDBA.dev**](http://myDBA.dev) is the most comprehensive option. It is the only tool that combines automatic EXPLAIN collection, plan regression detection, cluster-aware index advice, wait event heatmaps, and lock chain visualization in a single platform. Start with the free tier to evaluate it against your own workload. **If you want a free, self-hosted solution** and have the operational capacity to manage it, **Percona PMM** with `pg_stat_monitor` provides strong query analytics. Pair it with **pgBadger** for batch log analysis to cover what PMM's real-time view misses. **If your primary concern is index optimization** and you have the budget, **pganalyze**'s Index Advisor with "What If?" simulation is a capable tool -- but be aware that you are paying $149-$399/month for a platform that lacks plan regression detection and lock diagnostics. **If you are running on AWS RDS/Aurora**, enable **RDS Performance Insights** immediately for its wait event analysis, then layer a dedicated query optimization tool on top for EXPLAIN plans, index recommendations, and remediation guidance. **If you are already in the Datadog ecosystem**, Database Monitoring adds useful query-level visibility within your existing workflow, but you will still need PostgreSQL-specific tooling for deep optimization work. **If you are just getting started** and want a zero-cost, zero-commitment way to see your slow queries and obvious missing indexes, **pgHero** takes five minutes to set up and gives you immediate visibility. The key principle: monitoring tools show you that something is slow; optimization tools show you why and how to fix it. Choose accordingly.