AI Migration Tools Comparison: How to Actually Compare Them

How to Compare, More Usefully Than a Feature Grid

We build one of these tools, so treat this as an interested party explaining what the axes are rather than an impartial ranking. The useful thing we can offer is the set of questions that actually separate these tools — including the ones where we are not the obvious answer.

The Categories

• Open-source converters (ora2pg and similar). Mature, widely used, free, strong at schema and PL/SQL conversion. You supply the orchestration, the validation strategy, and the operational glue.
• Cloud-vendor services (AWS SCT/DMS, GCP and Azure equivalents). Well integrated with their own destinations and strong at continuous replication. Naturally oriented toward their own platform.
• Commercial platforms (EDB and others). Broad, supported, often bundled with a distribution or support contract.
• Focused desktop tools — where we sit. One migration path, done end to end, run by the DBA rather than by a platform team.

The Questions That Separate Them

What happens to what it cannot convert? The most revealing question you can ask. Silence is the answer you do not want. A named gap list with reasons is the answer you do.

How does it prove the data arrived? Row counts are table stakes and insufficient — they match when a numeric was truncated or a timestamp lost its time. Ask about column-level checksums, and about how a mismatch gets localised in a large table.

Where does your schema go? For any tool with an AI feature, ask whether inference is local or hosted. This is a governance question that often decides the shortlist before any capability comparison starts.

What is the cutover story? Bulk load only, or CDC for near-zero downtime? Acceptable downtime determines this, and it is a business input rather than a technical preference.

Who operates it? A platform team running pipelines and a DBA doing a one-off migration want genuinely different products, and neither is wrong.

Where We Are Not the Answer

If you are migrating into a specific cloud and already live in that vendor’s ecosystem, their native service will integrate more smoothly than anything third-party. If you need heterogeneous continuous replication between many engines as an ongoing capability rather than a migration, that is a different product category. And if your migration is genuinely small — a few dozen tables, no PL/SQL — pg_dump, a script, and a careful afternoon will do it, and you should not buy a tool.

What We Optimise For

One path — Oracle to PostgreSQL — covered end to end: assess, plan, convert, load, validate, cut over. A local AI assistant that requires no key and sends nothing anywhere. An honest gap list rather than a flattering percentage. And validation that goes past row counts to column checksums and per-partition proof.

Whatever you shortlist, run the assessment step of two or three tools against your real schema before choosing. The gap lists will differ, and the differences will tell you more than any comparison table — including this one.