Aurora MySQL is wire- and SQL-compatible with community MySQL, so moving from self-managed MySQL or RDS MySQL is a homogeneous migration — the easy win. No schema rewrite, no application changes, often a few minutes of cutover downtime. This guide covers why Aurora is worth the move (throughput, a storage layer that autoscales and self-heals, fast read replicas, sub-30-second failover), the three migration methods and when each fits, the real compatibility edge cases, how cutover actually runs, the cost math versus self-managed and RDS MySQL, and when you should just stay on RDS MySQL. A vetted AWS partner runs it for you, MAP-funded.
Aurora is not a fork of MySQL with a faster query planner. It is a re-architecture that keeps the MySQL engine on top but replaces the storage layer underneath with a purpose-built distributed one. Almost every Aurora advantage traces back to that single design decision, so it is worth understanding before you weigh the migration.
In community MySQL — self-managed or RDS MySQL — the engine and its storage are coupled: the instance owns its disk, and a replica is a second full copy kept in sync by replaying the binary log. Aurora breaks that apart. The compute instances run the MySQL-compatible engine, but the data lives in a separate distributed storage volume shared by every instance in the cluster, which automatically grows in 10 GB increments up to 128 TiB and replicates each block six ways across three Availability Zones. You never provision or extend storage, and you never run out mid-incident.
Because storage is shared and durable on its own, read replicas are cheap and fast. A community-MySQL replica needs its own full copy of the data and lags by however long it takes to ship and replay the binlog. An Aurora read replica (Aurora calls them Reader instances) attaches to the same storage volume the writer already uses, so there is no data to copy at provision time and replica lag is typically in the low milliseconds rather than seconds. You can run up to 15 Readers per cluster behind the built-in reader endpoint to load-balance read traffic.
That same shared-storage design is why failover is fast. When the writer fails, Aurora does not rebuild or resync a replica — the storage is already intact and shared, so it promotes an existing Reader and repoints the cluster endpoint, typically in under 30 seconds versus the minutes a traditional promotion can take. The storage layer also self-heals, continuously repairing bad blocks against the other copies so a single disk fault is invisible to you.
On throughput, Aurora MySQL generally outperforms community MySQL on the same instance class for high-concurrency OLTP, because the engine offloads work like redo logging into the storage layer instead of doing it on the instance. AWS's headline figure is up to ~5× stock MySQL; honestly the multiple depends on the workload, but write-heavy, high-concurrency apps see the biggest lift and almost nobody regresses. You also get features community MySQL lacks: Aurora Backtrack (rewind the cluster to a point in time in seconds, no restore), Fast Clone (a copy-on-write clone of a multi-TB database in minutes), and Aurora Serverless v2 (fine-grained compute autoscaling for spiky load).
Aurora keeps the MySQL engine you already run but swaps in a distributed, auto-growing, self-healing storage layer shared across the cluster — and that one change is what delivers the faster read replicas, sub-30-second failover, no-touch storage scaling, and higher write throughput. The migration is homogeneous, so you get all of that without rewriting anything.
Every database migration sits somewhere on a difficulty spectrum, and the position is set by one question: does the database engine change? MySQL→Aurora MySQL is the friendly end of that spectrum, and understanding why tells you how much risk and effort to budget.
A homogeneous migration keeps the same engine. Aurora MySQL is built to be drop-in compatible with community MySQL — it speaks the same wire protocol, runs the same SQL dialect, and is compatible with a specific MySQL version (Aurora MySQL version 3 is MySQL 8.0-compatible). Your existing MySQL drivers, connection libraries, ORM, SQL queries, stored procedures, and schema all work against Aurora unchanged. In most cases the only thing your application changes is the database endpoint in its connection string. That is the defining property of a homogeneous move and the reason this migration is low-risk.
Contrast that with a heterogeneous migration — Oracle→Aurora PostgreSQL, SQL Server→Aurora — where the engine and SQL dialect both change. There you need the AWS Schema Conversion Tool to translate schema, data types, and stored procedures, plus an engineer to hand-rewrite everything the tool cannot auto-convert. That manual remainder is weeks of work and the real schedule risk. MySQL→Aurora has none of it: there is no schema-conversion step at all, because the schema is already in the target dialect.
Practically: you do not need SCT, you do not budget for a schema-conversion phase, and the effort collapses to the data move and the cutover. The risks that remain are operational, not structural — getting the data across cleanly, validating it, and flipping the app with minimal downtime. Those are well-tooled steps. It is the difference between a one-to-three-week engagement and a multi-month one.
One caveat up front: "drop-in compatible" holds for the overwhelming majority of workloads but not literally every edge case, and Aurora is not the right home for every MySQL deployment. Section IV covers the compatibility differences to check; Section VII covers when to stay on RDS MySQL instead. But as a baseline expectation, this is the migration where the answer to "will my app just work?" is almost always yes.
There is no single "migrate to Aurora" button; there are three distinct methods, and the right one is decided by where you are starting from and how much downtime you can tolerate. Picking the wrong method is the most common way to make an easy migration harder than it needs to be.
The decision tree is short. If you are already on RDS MySQL and can take a maintenance window, snapshot restore is the simplest path. If you are on RDS MySQL and cannot take a real window, the Aurora read-replica promotion path gets you to near-zero downtime with almost no moving parts. If your source is self-managed MySQL (on EC2, on-prem, or another cloud) or you need near-zero downtime from any source, AWS DMS is the general answer. Here is each one in detail.
A migration partner runs whichever method fits as a rehearsed sequence: the data-copy and sync stages are reversible and repeatable, so they run them against a staging Aurora cluster first to measure timing and validate the data, then execute the production cutover with known numbers. The source stays intact and writable as the rollback target until validation passes. The method choice is the first thing they confirm on the scoping call, because it sets the entire downtime story.
How it works: RDS lets you create an Aurora MySQL cluster directly from a snapshot of an existing RDS MySQL instance. You take an RDS snapshot, restore it as a new Aurora cluster, and AWS migrates the data into Aurora's storage format for you. When the cluster is ready, you repoint the application.
Downtime profile: there is a window. Any writes that land on the source RDS MySQL after the snapshot was taken are not in the restored Aurora cluster, so to migrate cleanly you stop writes, take a final snapshot, restore it, and cut over — or you accept that the restore is point-in-time and you reconcile. For databases where a maintenance window is acceptable, this is by far the least effort.
Use it when: your source is RDS MySQL, the database is small-to-medium, and you can schedule a write-freeze (a nightly window, a weekend, a low-traffic period). It is the fastest method to execute and has the fewest things that can go wrong.
How it works: from an RDS MySQL instance you can create a special Aurora read replica. RDS does an initial data copy into a new Aurora cluster and then keeps it in sync with the source RDS MySQL via binlog replication, so the Aurora cluster stays continuously caught up while the source serves live traffic. When replica lag is at or near zero, you stop writes on the source, let the last transactions replicate, and promote the Aurora replica to a standalone cluster — then repoint the app.
Downtime profile: near-zero. The only window is the brief write-freeze at promotion while the final transactions drain — typically minutes. The bulk data copy happened earlier, in the background, with no impact on the source.
Use it when: your source is RDS MySQL and you cannot take a meaningful maintenance window. This is the cleanest near-zero-downtime path off RDS specifically — fewer moving parts than DMS because RDS orchestrates the replication for you. The catch is that it only works from RDS MySQL (and certain versions); it is not available for self-managed sources.
How it works: AWS Database Migration Service does a one-time full load of the existing data into the Aurora cluster, then switches to change data capture (CDC), reading the source's binary log and replaying every new INSERT/UPDATE/DELETE onto Aurora until you cut over. Because it works off the binlog, the source can be RDS MySQL, MySQL on EC2, on-prem MySQL, or MySQL on another cloud — DMS does not care where the source lives as long as it can reach it and read the log.
Downtime profile: minutes. Same cutover mechanics as the read-replica path — stop writes, wait for CDC lag to reach zero, validate, repoint — but DMS is engine- and location-agnostic, so it is the method that covers every source, not just RDS.
Use it when: your source is self-managed MySQL anywhere, or you want near-zero downtime from a source the read-replica method does not support, or you want DMS's built-in data validation (row counts and checksums) as part of the move. For a homogeneous MySQL→Aurora move there is no schema conversion, so DMS is doing the one job it is best at — moving data with the source live. See the full AWS DMS guide for how full load and CDC work in detail.
Aurora MySQL is compatible with community MySQL by design, and for most applications that means a clean lift. But "compatible" is not "identical," and a short list of differences is worth checking before cutover so nothing surprises you in production.
The honest summary: for a MySQL 8.0 application that is all-InnoDB and does not depend on exotic replication internals — which describes most modern MySQL workloads — the compatibility checklist is a formality and the app simply works. The cases that need real attention are legacy MyISAM tables and a major-version jump done at the same time as the migration. A partner who has done the move runs this checklist as standard intake rather than discovering an issue at cutover.
Cutover is the moment of truth: the few minutes when traffic moves from the old MySQL to the new Aurora cluster. For the near-zero-downtime methods it follows the same disciplined arc, and the discipline is what keeps it boring instead of dramatic.
For the read-replica and DMS methods, by the time you reach cutover the Aurora cluster is already a near-live copy of the source — fully loaded and kept current by replication. The cutover itself is a short, rehearsed sequence, ideally run in a low-traffic window:
Until the post-cutover soak passes, the original MySQL source stays untouched and writable. If Aurora misbehaves, you repoint the application back to the source — which never went anywhere — and the blast radius is only the writes that happened since cutover. For the snapshot-restore method, where the source is paused rather than replicating, the rollback is even simpler: unfreeze the original. A migration without a tested rollback path is not a finished plan, even on the easy homogeneous moves.
Aurora is not automatically cheaper than RDS MySQL on the sticker, and it is rarely cheaper than self-managed MySQL on raw instance cost alone. The cost case is about total cost of ownership and what you stop doing, not the hourly rate — and that distinction is where teams either justify the move or talk themselves out of it for the wrong reasons.
Against self-managed MySQL (on EC2 or on-prem), the comparison is rarely close once you count everything. Self-managed looks cheap because you only see the instance bill — but you also pay, in engineer-hours, for patching, version upgrades, backup tooling, replication setup and monitoring, failover automation, storage capacity planning, and the on-call burden when any of it breaks at 3am. Aurora absorbs all of that: automated backups and minor-version patching, automatic failover, no storage provisioning, a self-healing storage layer. For most teams the operational time recovered dwarfs the difference in compute price.
Against RDS MySQL, the two are much closer because both are managed. Aurora's compute per instance-hour is in a similar band, but its storage and I/O use a different model — you pay for storage consumed (auto-growing, no over-provisioning) and, on the standard configuration, for I/O. For I/O-heavy workloads that variable I/O charge can make Aurora pricier than a comparable RDS MySQL setup; Aurora I/O-Optimized removes per-I/O charges for a higher compute/storage rate and wins above a certain I/O intensity. Model your actual I/O profile rather than assuming Aurora is cheaper or pricier in the abstract.
What tips the RDS-vs-Aurora math toward Aurora is usually not the bill — it is the capabilities. If you need fast low-lag read replicas, sub-30-second failover, a storage layer that never needs capacity planning, or features like Backtrack and Fast Clone, Aurora earns its price. For a steady, modest, single-instance workload with no read-scaling or fast-failover requirement, RDS MySQL may be the more economical home and the migration is not worth doing for cost reasons alone.
Whatever the steady-state hosting bill works out to, the cost of doing the migration is the part AWS MAP funding offsets. The AWS Migration Acceleration Program funds migrations in phases — AWS frequently covers the Assess phase outright and credits a large share of the Mobilize and Migrate work, with the partner paid through MAP. For qualifying migrations (typically a meaningful post-migration AWS-spend commitment) the move is run for you at little-to-no cost. Where MAP funding does not apply, CloudRoute is still a vetted-partner referral that de-risks the cutover. See how AWS credits/MAP funding works, AWS POC funding explained, and the AWS migration cost breakdown.
A good migration page tells you when not to migrate. Aurora is the stronger platform on most axes, but "stronger" is not "necessary," and there are real workloads where RDS MySQL is the right answer and moving would be effort spent for marginal gain.
Choose Aurora MySQL when one or more of these is true: you need to scale reads with multiple low-lag replicas (Aurora's Readers share storage, so they are faster and cheaper to add than RDS read replicas); you have tighter availability requirements that benefit from sub-30-second failover; your data is growing and you do not want to manage storage capacity; your workload is write-heavy or high-concurrency and would benefit from Aurora's throughput; you want Aurora-only capabilities like Backtrack, Fast Clone, the global database option for multi-region, or Serverless v2 for spiky load. The majority of growing production MySQL workloads check at least one of these boxes, which is why Aurora is the common destination.
Stay on RDS MySQL when your workload is steady and modest, single-instance or with one simple replica, with no read-scaling or fast-failover requirement, where the I/O profile would make Aurora pricier with no offsetting benefit, or where you need strict parity with a community MySQL behavior Aurora handles differently. RDS MySQL is still fully managed — automated backups, patching, Multi-AZ failover — so "stay on RDS MySQL" is not "stay on something worse to operate"; it is picking the managed option whose feature set matches your needs without paying for capabilities you will not use.
And on the source question: if you are on self-managed MySQL today, the choice is rarely "self-managed vs Aurora" in isolation — it is "which managed service," because nearly everyone moving off self-managed wants to stop carrying the operational load. There the decision collapses to Aurora MySQL vs RDS MySQL on the criteria above. The migration into either is homogeneous and low-risk; the only real decision is which managed target.
A MySQL→Aurora move is low-risk, but "low-risk" still rewards someone who has done it before — they pick the right method, run the cutover rehearsals, and handle the compatibility edge cases as routine rather than as surprises. CloudRoute routes you to a vetted AWS partner who has done your exact source→Aurora migration and runs it end to end.
CloudRoute is a routing layer, not an agency. You tell us your source (RDS MySQL, MySQL on EC2, on-prem MySQL, MySQL on another cloud), your rough data size, and your downtime tolerance. We match you to a partner with a track record on MySQL→Aurora moves and the regions you operate in, and the partner runs the full workflow — pick the method (snapshot restore, read-replica promotion, or DMS), run the compatibility checklist, stand up and size the Aurora cluster, do the data move and validation, rehearse the cutover on staging, execute the production flip, and soak it — with you.
Because the partner is funded through AWS MAP for qualifying migrations, the cost to you is little-to-nothing: AWS pays for the assessment and credits the bulk of the migration work. You get a senior team that already knows the InnoDB conversion gotcha, the AUTO_INCREMENT reset at cutover, the Aurora I/O-Optimized cost decision, and the rollback rehearsal — instead of learning them on your production database. The honest version: MAP funding applies to qualifying migrations; where it does not, this is still a de-risked, vetted referral rather than you hiring blind.
If you want the persona-level detail on how migrations are scoped, funded, and run, see the CloudRoute migration track. For the broader toolset a partner uses across migration types, the AWS migration services overview and the AWS DMS guide cover the mechanics, and the AWS Migration Acceleration Program page explains the funding in full.
The three homes for a MySQL workload, side by side. Self-managed gives you maximum control and maximum operational burden; RDS MySQL is managed community MySQL; Aurora MySQL is the re-architected, MySQL-compatible service. Here is how they compare across the variables that decide the move.
| Variable | Self-managed MySQL (EC2 / on-prem) | RDS MySQL | Aurora MySQL |
|---|---|---|---|
| Management model | You run everything | Managed community MySQL | Managed, re-architected MySQL-compatible |
| Patching & upgrades | Manual, your responsibility | Automated (minor); managed major | Automated (minor); managed major |
| Storage | You provision and extend disks | Provisioned, you set size (autoscaling optional) | Distributed, auto-grows to 128 TiB, self-healing |
| Read replicas | DIY binlog replicas, you manage | Up to 15 replicas, each a full copy, binlog lag | Up to 15 Readers sharing storage, low-ms lag |
| Failover | You build it | Multi-AZ standby, ~1–2 min | Promote a Reader, typically < 30s |
| Throughput | Stock MySQL | Stock MySQL | Higher on high-concurrency OLTP (up to ~5× stock) |
| Standout features | Full control of the host | Simple managed MySQL | Backtrack, Fast Clone, Serverless v2, Global DB |
| Migration into it | — | Homogeneous, low-risk | Homogeneous, low-risk (snapshot / replica / DMS) |
| Cost shape | Cheap instance, expensive ops | Instance + provisioned storage | Instance + consumed storage + I/O (or I/O-Optimized) |
| Best for | Niche control needs; shrinking choice | Steady, modest, single-instance workloads | Read-scaling, fast failover, growth, high throughput |
Situation: Read traffic was outgrowing the setup — two RDS read replicas were lagging during peak sale events, and an unplanned failover the previous quarter had taken the writer offline for roughly two minutes, long enough to dent checkout. The team wanted faster, lower-lag read replicas and tighter failover, plus relief from babysitting storage as the catalog grew. They could not take a maintenance window on a 24/7 marketplace, and they had no in-house Aurora experience.
What CloudRoute did: Routed within 24 hours to a partner with multiple RDS MySQL→Aurora moves on record. Because the source was RDS MySQL and a window was off the table, the partner used the Aurora read-replica promotion path: created an Aurora read replica off the RDS instance, let the initial copy and binlog sync complete in the background with no source impact, and ran the whole flow twice on a staging clone first to measure promotion timing and validate the data. Confirmed the schema was all-InnoDB (it was) and reviewed the parameter group for parity. Filed under AWS MAP — AWS funded the Assess phase and credited the bulk of the Migrate work.
Outcome: Production cutover ran in a planned ~4-minute write-freeze: binlog replication drained to zero, AUTO_INCREMENT counters were confirmed past the source max, validation passed, and the app repointed to the Aurora writer endpoint with read traffic moved to the cluster reader endpoint. Post-move, replica lag on the Aurora Readers sat in the low milliseconds through the next sale event (versus seconds before), and a later test failover completed in ~20 seconds. The migration engineering was MAP-funded, so cost to the customer was effectively zero.
source: RDS MySQL 8.0 → Aurora MySQL v3 · data: ~600 GB · method: read-replica promotion · cutover downtime: ~4 min · failover: ~20s · cost to customer: ~$0 (MAP-funded)
Tell us your source and your downtime tolerance. CloudRoute routes you to a vetted AWS partner who has run your exact MySQL→Aurora move — the right method, the compatibility checklist, a rehearsed cutover, validated. For qualifying migrations AWS MAP funds the work, so the cost to you is little-to-nothing.