Comparison
AWS Transform vs OpenRewrite: when each one fits a Java upgrade.
One is rule-based and repeatable. The other is AI-driven and more flexible. Most estates need both, in a set order.
The short answer
OpenRewrite applies deterministic recipes, so the same input gives the same diff every time. AWS Transform uses AI and can handle cases no recipe covers, but its output can vary between runs. For Java upgrades, run recipes first, then an AI tool on what is left, then test and review. Ace8 is not affiliated with AWS.
Summary
What is the short version?
Details on each tool live on our pages for OpenRewrite and AWS Transform. This page is about choosing between them.
| Aspect | OpenRewrite | AWS Transform |
|---|---|---|
| Approach | Rule-based recipes on a parsed code tree | AI-driven analysis and rewrite |
| Same input, same output? | Yes, by design | Not guaranteed; check on your code |
| Cost model | Open source; you pay engineer time | A metered AWS service; check current pricing |
| Reach | Only what a recipe covers | Can attempt changes no recipe exists for |
| Audit trail | A diff from explicit rules | A diff plus a generated summary |
| Custom internal patterns | Write a custom recipe | Describe or demonstrate the change; results vary |
| Review needed | Yes, every diff | Yes, every diff |
Predictability
Why does determinism matter for upgrades?
An upgrade across thirty repositories is a repeated job. With a recipe you can run it, review the diff, fix the recipe if something is off, and run it again to get a clean result. You can also explain to an auditor exactly which rules changed the code.
An AI service may produce a different change on a second run, or handle two similar files in two different ways. That is not a flaw in every case, and it is why we measure repeatability in our benchmark rather than assume it.
Coverage
What does AI cover that recipes do not?
A recipe exists only where someone wrote one. Libraries with no recipe, odd build setups, and code that needs a judgement call sit outside it. An AI tool can attempt these, and it is useful for the long tail where writing a recipe is not worth the effort.
The cost of that reach is checking. Each AI-suggested change needs a reader who understands the intent, because a plausible change can still alter behavior.
Choose OpenRewrite
When should you lead with OpenRewrite?
- The change is mechanical and repeats across many files or repositories, such as javax to jakarta or a Spring Boot version bump.
- You need the same result across a large estate and an audit trail.
- The same internal pattern appears hundreds of times, so a custom recipe pays back.
- You want a result you can reproduce next quarter on a new branch.
Choose AWS Transform
When should you add AWS Transform?
- Recipes have run and the remaining errors are varied, one-off or tied to specific libraries.
- The application is small enough that writing a custom recipe is not worth it.
- You want a generated summary of what changed to speed the reviewer's first pass.
- Your team already works in AWS and is happy to meter the service.
Sequence
What order do we run them in?
Recipes first, because they are cheap to check. Then an AI tool on the residue, so the least predictable step stays small. Then tests, then engineer review of the whole diff. Whichever tool made a change, the reviewer signs it.
Tests decide how fast any of this goes. If coverage is thin, the first job is to add tests around the code you are about to change. That is the first principle of our method.
Cost
What are the real cost drivers?
OpenRewrite costs engineer time, not licence fees. AWS Transform adds a metered charge, which depends on how it is priced at the time; check AWS for the current terms. In both cases the larger cost is review and fixes after the tool finishes. We record review minutes and manual fixes in the benchmark for that reason. For the wider picture see application modernization cost.
Evidence
What does our own testing show?
Our sample-repo run is not complete, so we do not claim results here. The method, scoring table and failure policy are on the benchmark page. This comparison will be updated with measured differences when that run is published.
Ace8 is not affiliated with AWS and has no commercial interest in which tool you choose.
Neither is better across the board. OpenRewrite is repeatable and covers what recipes exist for. AWS Transform can attempt more, with output that needs closer checking. Many projects use both.
We have not confirmed how AWS builds its transformations internally, so we do not state it. Check AWS documentation for the current description.
OpenRewrite for rule-based changes, GitHub Copilot app modernization for IDE-driven AI changes, and Moderne for running OpenRewrite recipes at scale.
Yes. Run recipes first, then the AI tool on what remains, then test and review.
Yes. Both produce diffs that need review, and neither can prove the result is correct for your system. Tests and a reviewer do that.
Know the cost before anything moves.
A fixed-price assessment gives you the inventory, the risk score and a phased plan. Scope and cost are fixed before the migration starts.
Book a Fixed-Price AssessmentGet in touch
Talk to a modernization engineer
Tell us what you run today. An engineer, not a sales script, replies within one business day.