Most teams weighing a move to Amazon Aurora treat it as a tooling question: Which converter to run, how to handle the cutover, how long it will take. That was a fair concern a few years ago. It is much less of one today. The conversion tooling has become managed and largely automated, which means the mechanics are no longer where migrations succeed or stall. The outcome is set earlier, by three planning decisions you make before any tool runs. Get those right, and the rest follows.
Aurora is popular for a simple reason: It offers the performance and reliability people associate with commercial databases at something closer to open-source economics. It is compatible with MySQL and PostgreSQL, and Amazon Web Services (AWS) reports that it delivers up to five times the throughput of standard MySQL and up to three times that of standard PostgreSQL, with high availability built in. For a business, that translates into faster applications, fewer reliability worries, and a lower database bill. Those are good reasons to consider a move. They are not, by themselves, reasons the move will go well.
Here is what has actually shifted. AWS now offers schema conversion as a managed, serverless capability inside the AWS Database Migration Service (DMS), rather than a desktop tool you install and maintain. It reads your source database, produces an assessment of what will convert cleanly and what needs a human, and increasingly uses generative artificial intelligence (AI) to handle the harder code. That removes a lot of manual effort. It does not remove the need to plan. If anything, easier tooling makes the planning decisions matter more, because they are now the part that is still yours to get right. Three of them do the heavy lifting.
You cannot plan a move you have not measured. Before anything else, get clear on what you are actually moving: The database engine and version, its size, how heavily it is used, and what depends on it upstream and downstream. A small reporting database and a busy transactional system that other applications lean on are very different migrations, even to the same target. This picture is what tells you how to size Aurora correctly and where the real effort will sit. Skipping it is how teams end up surprised mid-project.
Once you know the source, generate a migration assessment report. The managed schema-conversion capability in AWS DMS produces one automatically: It shows what converts to Aurora cleanly and flags the objects that need manual attention, with guidance on each. This is the single most useful artifact in the early stages, because it turns unknowns into a scoped list of work. Read it before you commit to a timeline. A plan built on the assessment is one you can defend; A plan built on optimism is one you will end up revising.
Aurora can lower your database costs, but "can" depends on how you configure it. Use the AWS Pricing Calculator to estimate your target footprint, and try a few combinations of instance size, storage, and deployment before you settle. Modeling the cost up front does two things: It protects the business case you used to justify the move, and it surfaces the trade-offs while you can still act on them. This is a short exercise that saves a long conversation later.
If you are already mid-migration, none of this asks you to restart. These three checks slot into what you are already doing: They sharpen your sizing, they tighten your scope, and they firm up your cost estimate. Treat them as a way to raise confidence in a plan that is already in motion, not a reason to unwind it.
Notice what ties the three together. A clear view of the source, an assessment-led plan, and a modeled cost estimate are simply the difference between a migration you are guessing at and one you can predict. The tooling will keep getting better. These three decisions are what keep the outcome in your hands.
This is the approach KPI Partners brings to Aurora and to broader AWS migrations. As an AWS partner, KPI Partners helps enterprises plan and run these moves assessment-first: Understand the source, let the report scope the work, model the target cost, then migrate with validation at each step. Where a database move is part of a larger platform modernization, the same discipline carries into the Data Platform Migration Accelerator, which applies an automation-led, validation-first method to reduce risk and rework. The goal is the same in every case: A move that is predictable, defensible, and built to last.