Teams standardizing on Aurora often treat the flavor choice as an afterthought, as if Aurora MySQL and Aurora PostgreSQL were two skins on the same product. They are not. Aurora's distributed storage layer is shared: six copies of data across three availability zones, storage-level replication to up to fifteen readers, fast crash recovery. Everything above that layer is the actual database, and MySQL 8.x and PostgreSQL are different databases with different SQL dialects, different MVCC designs, different planners, and different ecosystems.
I have operated both engines, inside and outside Aurora. This piece is the comparison I give teams who ask which flavor to pick, and it deliberately avoids the thing most comparisons lean on: performance numbers invented or borrowed from someone else's workload. The honest answer on performance is that you must measure your own workload, and I will show you what to measure. Everything else, the feature deltas, the extension story, and the gravitational pull of your existing systems, can be reasoned about up front.
What the shared storage layer does and does not equalize
Aurora's architecture removes some classic differences between the engines. Replica behavior changes on both flavors, because readers share the storage volume rather than replaying a full change stream shipped over the network, and reader lag is typically far below what self-managed replication produces. Crash recovery is fast on both. Storage grows without pre-provisioning on both. If your mental model of MySQL versus PostgreSQL replication comes from self-managed binlog replication versus WAL shipping, set it aside; inside Aurora, that particular fight mostly disappears.
What the storage layer does not equalize is everything the query layer owns: how the planner chooses plans, how transactions and isolation behave, how DDL locks, what data types exist, what the SQL dialect can express, and what happens under concurrency. Those differences are inherited straight from the upstream engines, and they are the real decision surface.
Feature deltas that actually matter
On the PostgreSQL side, the differentiators I see teams actually use: transactional DDL, so a failed migration rolls back cleanly instead of leaving a half-altered schema; richer indexing, including partial indexes, expression indexes, GIN indexes over jsonb, and covering indexes with INCLUDE; a deeper procedural layer when you need logic close to the data; and stronger standards conformance in the SQL dialect, which matters when you write window-heavy or CTE-heavy queries. Aurora PostgreSQL is also the flavor that carries Babelfish, if a SQL Server compatibility layer is relevant to your migration story.
On the MySQL side, the differentiators are equally real: InnoDB's clustered primary key layout is excellent for primary-key-centric OLTP; the undo-log MVCC design means update-heavy tables do not accumulate dead tuples for a vacuum process to reclaim; Aurora MySQL offers parallel query for pushing analytical scans down to the storage layer on supported configurations; and the operational muscle memory of a MySQL-fluent team transfers directly. Neither list is a knockout. Map them against your backlog, not against each other in the abstract.
The extension ecosystem is PostgreSQL's structural edge
The single biggest asymmetry between the flavors is not a feature, it is an extensibility model. PostgreSQL was built to be extended, and Aurora PostgreSQL supports a long list of extensions: PostGIS for geospatial work, pgvector for embedding search, pg_stat_statements for query statistics, pg_partman for partition management, foreign data wrappers, and many more. MySQL has plugins and components, but nothing with the breadth or community velocity of the PostgreSQL extension catalog. You can see exactly what your Aurora PostgreSQL version offers from the database itself:
SELECT name,
default_version,
installed_version,
left(comment, 60) AS description
FROM pg_available_extensions
ORDER BY name;The forward-looking question matters more than the current one. If there is any chance your product grows toward geospatial features, vector search, or time-series-shaped data, the PostgreSQL flavor keeps those doors open inside the database you already run. On Aurora MySQL, each of those usually becomes a separate system to operate. This is what I mean by structural edge: it is not that PostgreSQL does everything better today, it is that its answer to a new requirement is more often an extension than an architecture change.
Performance: what to test instead of what to believe
Marketing pages for both flavors quote throughput multipliers against community editions. Published third-party benchmarks contradict each other freely, because results depend on schema design, working set size relative to memory, durability settings, and concurrency model. I will not add numbers to that pile, and you should distrust anyone who hands you numbers without your workload attached.
What I will give you is the test protocol. Take your top query families from production, not synthetic ones. On the MySQL side, pull them from performance_schema:
SELECT LEFT(DIGEST_TEXT, 120) AS query_family,
COUNT_STAR AS calls,
ROUND(SUM_TIMER_WAIT / 1000000000000, 1) AS total_s,
ROUND(AVG_TIMER_WAIT / 1000000000, 2) AS avg_ms
FROM performance_schema.events_statements_summary_by_digest
ORDER BY SUM_TIMER_WAIT DESC
LIMIT 20;Replay a realistic mix against candidate clusters of the same instance class, with production-scale data and equivalent durability settings, and run long enough for caches to warm and background maintenance to participate: hours, not minutes. Measure p95 and p99 latency, not just averages and throughput. Then test the events that bite later: your largest schema migration on a copy of the biggest table, failover time under load, behavior when a reader saturates, and, on the PostgreSQL flavor, sustained update throughput once autovacuum is running against the workload. Instance class interacts with all of this; the PostgreSQL sizing guide covers how memory and connection headroom feed those choices on the PostgreSQL side.
Migration gravity: the force most comparisons ignore
If you already run MySQL, moving to Aurora MySQL is close to a lift-and-shift: same dialect, same drivers, mostly the same operational instincts, often a replication-based cutover with modest risk. Moving from MySQL to Aurora PostgreSQL is a genuine migration: schema conversion, query dialect changes, driver and ORM behavior differences, and operator retraining. The reverse holds for teams arriving from PostgreSQL. This gravity is legitimate decision input, not cowardice. A modest feature preference rarely outweighs it; a structural need, like the extension ecosystem or transactional DDL for a migration-heavy product, sometimes does.
The trap is letting gravity decide silently. Make it explicit: write down what the workload needs in two years, price the migration honestly, then choose with both numbers on the table. Teams weighing this alongside broader engine decisions can start from our comparison pages for feature-level detail.
A short decision checklist
Pick Aurora MySQL when your team is MySQL-fluent, the workload is primary-key-centric OLTP, your critical tooling is MySQL-shaped, and nothing on the roadmap needs the PostgreSQL extension catalog. Pick Aurora PostgreSQL when the roadmap includes geospatial, vector, or analytics-flavored SQL inside the OLTP store; when transactional DDL and richer indexing map to real backlog items; when the organization is consolidating onto PostgreSQL; or when you are arriving from PostgreSQL already. When the answer is genuinely balanced, weight team fluency heavily. Databases fail operationally more often than they fail architecturally, and the flavor your team can debug at 3 a.m. is worth more than a marginal feature win.
Monitoring the PostgreSQL flavor with MonPG
Whichever flavor you choose, CloudWatch and Database Insights cover the platform layer, and the database layer still needs engine-specific evidence. If you land on Aurora PostgreSQL, that means pg_stat_statements history, wait events, lock chains, autovacuum behavior, and per-reader lag, and that is the layer MonPG covers. The Aurora PostgreSQL monitoring page shows the Aurora-specific setup, and the broader PostgreSQL monitoring guide explains the signals that matter on every PostgreSQL deployment.
MonPG monitors PostgreSQL only. If you choose Aurora MySQL, you will want performance_schema-based tooling instead, and that is a fine outcome; the goal of this piece is the right flavor for your workload, not a predetermined vendor conclusion. But if the extension ecosystem and SQL depth pull you to the PostgreSQL side, wire up query-level monitoring before the first production deploy rather than after the first production incident. New engine, new instincts, and the evidence trail has to carry you while the instincts catch up.