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7 pganalyze Alternatives for Postgres in 2026

September 17, 2026 by Chat2DB Team

pganalyze built its reputation on one thing: turning pg_stat_statements into something a human can act on. It normalizes queries, tracks plans over time, surfaces index recommendations and explains why a plan flipped. It is a good product, but it is not the right shape for every team. Some organizations cannot ship query text to a third party. Some already pay for an APM and do not want a second observability bill. Some just need to see what is running right now, not a month of history.

This guide walks through seven alternatives, what each one is genuinely good at, and the trade-off you accept when you choose it.

What you are actually replacing

Before comparing products, it helps to separate the four jobs pganalyze does, because most alternatives only do some of them:

1. Query performance history — normalized statements with call counts, total time and I/O, tracked over days or weeks.
2. Plan capture — the actual execution plan for a slow statement, ideally with the plan that was used at the time it was slow.
3. Server health — connections, cache hit ratio, replication lag, vacuum progress, bloat, transaction ID age.
4. Recommendations — "add this index", "this table needs a vacuum", "this query is missing a join condition".

Everything below is scored against those four jobs.

1. Chat2DB

Chat2DB (opens in a new tab) approaches the problem from the SQL client side rather than the agent-and-dashboard side. You connect to the database directly, and the monitoring views live next to the schema tree and the editor where you are already working.

The practical advantage is the loop: you spot a slow statement, open it in the editor, run EXPLAIN (ANALYZE, BUFFERS), adjust the query or add an index, and re-run — without switching tools or waiting for the next agent snapshot. Its AI features also explain a plan in plain language, which shortens the gap between "this query is slow" and "this query is slow because the planner chose a nested loop on a bad row estimate".

What you give up compared to pganalyze is long-horizon history. Chat2DB shows you the current contents of pg_stat_statements and pg_stat_activity beautifully, but it is not a time-series store that retains six weeks of per-query trends. For the very common case — "something is slow now, or was slow since the last stats reset" — that distinction rarely matters.

Chat2DB runs on macOS, Windows and Linux, plus a browser version, and it speaks Postgres, MySQL, ClickHouse, SQL Server, Oracle and more, so it doubles as the client for the rest of your estate. Download it at chat2db.ai/download (opens in a new tab) or open the web version at app.chat2db.ai (opens in a new tab).

Best for: engineers who want diagnosis and fixing in one place.

2. pg_stat_statements plus Grafana (the DIY stack)

The honest baseline. pg_stat_statements is already in your database; postgres_exporter scrapes it, Prometheus stores it, Grafana draws it. Cost is infrastructure only, and no query text leaves your network.

Enable it first:

-- postgresql.conf
-- shared_preload_libraries = 'pg_stat_statements'
-- pg_stat_statements.max = 10000
-- pg_stat_statements.track = top
-- Requires a restart.
 
CREATE EXTENSION IF NOT EXISTS pg_stat_statements;

Then the query that does 80% of the work:

SELECT
    substring(query, 1, 90)                      AS query,
    calls,
    round(total_exec_time::numeric, 1)           AS total_ms,
    round(mean_exec_time::numeric, 2)            AS mean_ms,
    round((100 * total_exec_time /
           sum(total_exec_time) OVER ())::numeric, 1) AS pct_of_total,
    shared_blks_hit,
    shared_blks_read,
    round((100.0 * shared_blks_hit /
           nullif(shared_blks_hit + shared_blks_read, 0))::numeric, 1) AS hit_pct
FROM pg_stat_statements
WHERE query NOT LIKE '%pg_stat_statements%'
ORDER BY total_exec_time DESC
LIMIT 20;

Sort by total_exec_time, not mean_exec_time. A query averaging 8 ms that runs two million times an hour costs far more than a 4-second report someone runs at breakfast.

The gap is everything pganalyze adds on top: no plan capture, no history beyond your Prometheus retention, and no recommendations. You also inherit the operational work of running the stack.

Best for: teams with existing Prometheus and Grafana, and a hard no-third-party-data rule.

3. Percona Monitoring and Management (PMM)

PMM is the closest open-source product to a packaged pganalyze. It is free, self-hosted, and covers Postgres, MySQL and MongoDB from one install. Query Analytics normalizes statements, shows per-query trends and ties them to host metrics, and the dashboards for replication, vacuum and connections are genuinely thorough.

The trade-offs are real but manageable: you run the PMM server yourself, its Postgres coverage is less deep than its MySQL coverage, and the recommendations are thinner than pganalyze's. If you are already a Percona shop, this is the obvious first stop.

Best for: mixed-engine estates that want one self-hosted pane of glass.

4. Datadog Database Monitoring

Datadog DBM captures normalized queries, samples execution plans, and — its real advantage — correlates a slow query with the application trace that issued it. When the question is "which endpoint caused this database spike", nothing else answers it as directly.

The cost model deserves scrutiny: DBM is billed per database host on top of your existing Datadog spend, which makes a fleet of small Postgres instances disproportionately expensive. Plan-level detail is also less granular than pganalyze's.

Best for: teams already standardized on Datadog for APM.

5. pgwatch2

An open-source, metrics-first monitor built specifically for Postgres. It polls configurable metric sets on a schedule into Postgres, TimescaleDB or Prometheus, and ships a solid set of Grafana dashboards. Lighter to run than PMM and more Postgres-native.

It is metrics-oriented rather than query-forensics-oriented. You will see that your cache hit ratio dropped and vacuum is behind; you will not get plan capture or index advice.

Best for: self-hosted fleets that want good Postgres dashboards without running a heavyweight platform.

6. Your cloud provider's built-in tooling

If you run RDS, Aurora, Cloud SQL or Azure Database for PostgreSQL, you already have Performance Insights or Query Insights. They cost little or nothing, need no agent, and show wait-event breakdowns that are hard to get any other way — Performance Insights in particular tells you whether time went to CPU, lock waits, or I/O.

The limits: retention is short on the free tier, you are locked to one cloud, and the recommendations are minimal. But for a first look at a production incident it is often the fastest answer available.

Best for: single-cloud teams who want wait-event analysis with zero setup.

7. pgBadger

The outlier: a log analyzer, not a live monitor. Point it at your Postgres logs and it produces a static HTML report of slow queries, error distributions, checkpoint behaviour, lock waits and temporary file usage.

# Log the statements worth analyzing
# postgresql.conf:
#   log_min_duration_statement = 500
#   log_checkpoints = on
#   log_lock_waits = on
#   log_temp_files = 0
#   lc_messages = 'C'
 
pgbadger /var/log/postgresql/postgresql-*.log -o report.html

Because it reads logs, it sees things pg_stat_statements cannot: actual parameter values, lock wait details, temp file spills and the exact timing of a checkpoint storm. Because it reads logs, it is retrospective — there is no live view, and aggressive logging costs I/O on a busy server.

Best for: post-incident forensics and periodic health reviews.

Choosing between them

A pattern worth noting: most teams end up with two of these, not one. A continuous metrics layer (a cloud tool, pgwatch2 or PMM) answers when something changed, and an interactive client answers why. The failure mode is buying a heavyweight platform and still SSH-ing in with psql to do the actual investigating.

A note on resetting statistics

Whichever tool you pick, remember that pg_stat_statements is cumulative since the last reset. Numbers that include a long-finished migration will mislead you for weeks.

-- Snapshot before you reset, so you keep the history
CREATE TABLE IF NOT EXISTS pgss_history AS
SELECT now() AS captured_at, * FROM pg_stat_statements WITH NO DATA;
 
INSERT INTO pgss_history
SELECT now(), * FROM pg_stat_statements;
 
SELECT pg_stat_statements_reset();

Run that before and after a deploy and you can diff the two windows to see exactly which statements the release changed — which is, in the end, the question most monitoring tools are bought to answer.