AWS Schema Conversion Tool (SCT) cost, 2026

The tool is free. The conversion is not. AWS SCT and the in-console DMS Schema Conversion automate most of a heterogeneous database migration, but the cost lives in the remediation labour for what they cannot convert. Auto-conversion rates by source-target pair, and a worked Oracle to Aurora PostgreSQL build.

AWS Schema Conversion Tool (SCT) converts a database schema and its code from one engine to another, for example Oracle to Aurora PostgreSQL or SQL Server to MySQL. It is the companion to AWS Database Migration Service (DMS): SCT converts the structure and logic, DMS moves the data. The headline fact about SCT cost is that the tool is free, so the cost question is really about the labour to finish what the tool cannot automate. This page is the 2026 reference for that cost.

Pricing verification

AWS Schema Conversion Tool and the in-console DMS Schema Conversion are both free of licence fees; AWS does not charge for either, confirmed against AWS DMS documentation in June 2026. The auto-conversion rates and remediation-labour bands below are the same figures used on the AWS DMS pricing reference and reflect typical mid-market heterogeneous migrations; the actual figure for a given database depends on its schema complexity and is sized by the SCT assessment report. Verified June 2026.

The tool is free; the labour is the cost

SCT itself carries no fee. You download the desktop application (or use the equivalent DMS Schema Conversion capability inside the AWS DMS console), point it at the source database, and it produces a conversion report: the target schema, a list of objects it converted automatically, and a list of objects it could not, each with a complexity rating. The report is the estimate. The cost is the labour to remediate the objects on the second list.

That labour is concentrated in the parts of a database that are engine-specific: stored procedures, triggers, proprietary built-in functions, custom data types, and any application logic that lives inside the database rather than the application tier. Each unconverted object has to be rewritten for the target engine and tested for functional equivalence, and the hardest objects (deeply nested packages, performance-critical procedures) can take days each.

Auto-conversion rates and remediation labour by source-target pair

The single biggest driver of conversion cost is the source-target pair. The closer the source and target engines, the more SCT converts automatically and the smaller the remediation backlog.

SCT auto-conversion rate and typical remediation labour per database (2026)

Source	Target	Auto-conversion rate	Typical remediation labour per database
Microsoft SQL Server	Aurora PostgreSQL (via Babelfish)	80 to 95%	$30K to $150K
Microsoft SQL Server	PostgreSQL	75 to 90%	$40K to $200K
Microsoft SQL Server	MySQL	70 to 85%	$40K to $200K
Oracle	Aurora PostgreSQL	70 to 90%	$60K to $300K
Oracle	PostgreSQL	75 to 92%	$50K to $250K
Sybase ASE	PostgreSQL or MySQL	60 to 80%	$80K to $400K
IBM Db2	PostgreSQL	55 to 75%	$100K to $500K
Teradata	Amazon Redshift	65 to 85%	$150K to $800K

The conversion-cost rule of thumb

The remediation labour scales with the count and complexity of objects SCT cannot convert, not with data volume. A 100 GB Oracle database with thousands of stored procedures costs far more to convert than a 10 TB database with a simple schema. Always run the SCT assessment before quoting a heterogeneous migration; the object backlog, not the data size, is the number that sets the budget.

SCT desktop versus DMS Schema Conversion in the console

There are two ways to run the conversion, and both are free. The standalone AWS Schema Conversion Tool is a desktop application you install and run locally; it is the long-standing option and remains useful for offline assessment and for source environments that cannot reach the AWS console directly. DMS Schema Conversion is the same capability built into the AWS DMS console, so you assess and convert schema without installing the desktop tool, which is the more convenient path for AWS-native workflows and where AWS is steering new users.

The choice between them does not change the cost. Both produce the same conversion report, the same auto-conversion rate, and the same remediation backlog. Pick DMS Schema Conversion for convenience inside the DMS console, or desktop SCT when you need offline assessment or richer local reporting. Neither adds a fee to the migration.

Worked Oracle to Aurora PostgreSQL conversion build

A representative cost build for converting a single moderately complex Oracle database to Aurora PostgreSQL: roughly 1,200 schema objects, of which SCT auto-converts about 80 percent, leaving roughly 240 objects (mostly PL/SQL packages and triggers) for manual remediation.

Worked SCT conversion build, one Oracle database to Aurora PostgreSQL

Cost line	Low estimate	Typical estimate	High estimate
AWS SCT / DMS Schema Conversion licence	$0	$0	$0
SCT assessment and report	$4,000	$8,000	$15,000
Auto-converted object validation (~960 objects)	$8,000	$15,000	$28,000
Manual remediation of unconverted objects (~240)	$45,000	$110,000	$220,000
Functional-equivalence testing and sign-off	$15,000	$40,000	$80,000
Conversion total (excluding DMS data load)	$72,000	$173,000	$343,000

The licence line is zero; the cost is entirely labour, and the manual remediation of the unconverted 20 percent is the dominant line. The typical total here, roughly $173,000, sits inside the $60K to $300K Oracle to Aurora PostgreSQL band, and is separate from the DMS data migration cost (typically a four-figure tooling line) and the destination Aurora run-rate. The run-rate saving from removing Oracle licensing is what justifies this one-off conversion spend, with break-even usually under 18 months for licence-heavy estates.

How to reduce schema-conversion cost

Choose the target that maximises auto-conversion. SQL Server to Aurora PostgreSQL via Babelfish converts more automatically than a clean PostgreSQL target.
Retire and simplify before converting. Drop unused tables, dead stored procedures, and legacy objects so you do not pay to convert code nobody runs.
Run the SCT assessment early. Sizing the object backlog up front turns the remediation into a scoped, quoted task instead of a mid-project discovery.
Move application logic out of the database where practical. Logic in the application tier does not need engine-specific conversion.
Batch similar objects. Converting twenty near-identical procedures together is far cheaper per object than converting them in isolation.
Use DMS Schema Conversion in the console for AWS-native workflows to avoid desktop-tool setup overhead; the conversion result and cost are identical.

AWS Schema Conversion Tool is free, and that is the trap: the free tool hides a labour cost that is frequently the single largest line in a heterogeneous database migration. The discipline is to treat the SCT assessment report as the estimate it is, scope the remediation backlog before committing, and choose the source-target pair that leaves the smallest manual remainder. Get that right and the conversion is a predictable, one-off spend that pays back in removed licensing; get it wrong and it is the line that blows the budget.

Q&A

Frequently asked

Q. What does AWS Schema Conversion Tool cost?

A. AWS Schema Conversion Tool (SCT) is free to download and use. There is no licence fee. The real cost of a schema conversion is the labour to remediate the objects SCT cannot convert automatically: typically $30,000 to $150,000 per database for SQL Server to PostgreSQL, $60,000 to $300,000 for Oracle to Aurora PostgreSQL, and up to $500,000 to $800,000 for complex sources such as IBM Db2 or Teradata. SCT does the automated 55 to 95 percent; the cost is in the manual remainder.

Q. What is the difference between SCT and DMS Schema Conversion?

A. SCT is the standalone desktop application you install and run locally. DMS Schema Conversion is the equivalent capability built into the AWS DMS console, so you can assess and convert schema without installing the desktop tool. Both are free. DMS Schema Conversion is the more convenient option for AWS-native workflows and is where AWS is steering new users; the desktop SCT remains available for offline assessment and for source environments that cannot reach the DMS console directly. Neither carries a fee; both produce the same conversion report and the same remediation backlog.

Q. What auto-conversion rate should I expect?

A. It depends entirely on the source-target pair and the schema's complexity. Homogeneous-leaning conversions (SQL Server to PostgreSQL via Babelfish) auto-convert 80 to 95 percent. Oracle to Aurora PostgreSQL auto-converts 70 to 90 percent. Harder sources auto-convert less: Sybase ASE 60 to 80 percent, IBM Db2 55 to 75 percent, Teradata to Redshift 65 to 85 percent. The unconverted remainder is concentrated in stored procedures, triggers, proprietary built-in functions, and engine-specific SQL, which is where the remediation labour goes.

Q. Why is the labour so expensive if the tool is free?

A. Because the objects SCT cannot auto-convert are the hardest ones: deeply nested PL/SQL packages, proprietary built-in functions, engine-specific isolation and locking behaviour, and application logic embedded in the database. Each has to be rewritten by hand for the target engine and then tested for functional equivalence. A single complex stored procedure can take days. The conversion report tells you how many objects need work and roughly how complex each is, which is what lets you estimate the labour before committing.

Q. Does SCT migrate the data as well as the schema?

A. No. SCT (and DMS Schema Conversion) converts the schema and code; it does not move the data. The data migration is handled separately by AWS Database Migration Service (DMS), which replicates the table contents from source to target once the converted schema is in place. The standard heterogeneous-migration pattern is: SCT or DMS Schema Conversion to convert the schema, manual remediation of the unconverted objects, then DMS to load and replicate the data. SCT cost and DMS cost are separate lines in the migration budget.

Q. Can I reduce the conversion labour?

A. Yes, in three main ways. First, choose a target that maximises auto-conversion: SQL Server to PostgreSQL via Babelfish for Aurora PostgreSQL converts more automatically than a clean PostgreSQL target. Second, retire or simplify unused and legacy database objects before conversion, so you are not paying to convert code nobody runs. Third, run the SCT assessment early to size the backlog accurately, so the remediation work is scoped and quoted rather than discovered mid-project, which is where conversion budgets usually overrun.