SaaStr says an AI agent moved 10 years of marketing data and roughly 450,000 contacts from Adobe Marketo to Salesforce in one hour. Agencies had quoted about $100,000 and a year for the migration. The reported model cost was $14.28.

That is the kind of number that makes every expensive software renewal look negotiable. It also needs context.

The lesson is not that every $100,000 migration now costs $14. The lesson is that much of the old bill paid for repetitive transformation work that AI can compress. Data verification, business rules, consent records, and rollback planning do not disappear with it.

Why Software Migrations Cost So Much

A migration is rarely just an export followed by an import.

One platform stores a customer as a lead. Another calls the same record a contact. A campaign status that means "attended" in one system might map to two objects in the replacement. Fields have different names, automations fire under different conditions, and years of manual work leave duplicates that nobody wants to discuss.

Traditional migration teams spend weeks on four kinds of work:

• discovering where data and automations live;
• mapping fields and workflow rules between systems;
• writing scripts to transform records;
• checking whether the destination matches the source.

The third item is where AI changes the economics most. A capable coding agent can inspect schemas, write transformation code, classify unfamiliar fields, and repair a failed batch much faster than a team working through tickets.

In SaaStr's July 17 account, its agent migrated about 300 campaigns along with a decade of member data. That is a real result. It is not a universal benchmark.

The API Still Sets the Speed Limit

AI does not get a private door into legacy software. It uses the same APIs and exports available to everyone else.

Adobe's current Marketo bulk extract documentation permits two concurrent export jobs, keeps files for seven days, and provides a default daily export allocation of 500 MB. Date-based exports can cover no more than 31 days at a time. Marketo also limits standard integrations to 100 API calls per 20 seconds.

Those constraints matter. An agent can plan batches and recover from rate-limit errors, but it cannot wish the limits away.

This is why the most impressive migration demos often involve a system with usable exports, clean destination APIs, and a team that already understands its data. If the source database is undocumented or the vendor blocks bulk access, the job is still hard.

Where AI Can Reduce the Bill

AI is useful when the work is large in volume but easy to verify.

Field mapping is a good example. An agent can compare source and destination schemas, propose matches, and flag ambiguous fields for a person. It can generate transformation scripts and tests from the approved mapping.

Historical campaign conversion is another. Hundreds of similar workflows can be translated from a common pattern, then checked against a representative sample.

Reconciliation is especially valuable. A migration script should answer basic questions after every batch:

• Did the source and destination record counts match?
• Which records failed, and why?
• Were dates, currencies, and time zones preserved?
• Did unsubscribe and consent states survive the move?
• Can the batch be reversed without touching valid destination data?

These checks are not administrative overhead. They are the product.

The $14 Number Is Not the Project Cost

Model usage is only one line in the budget.

Someone still has to decide what moves, approve the field mapping, provide access, inspect exceptions, and sign off on the cutover. The destination may need new workflows. Staff may need training. Both subscriptions may run in parallel for a short period.

We would reject any proposal that promises a complex migration for the token bill alone. It confuses the cost of generating code with the cost of taking responsibility for business data.

A better way to price the work is by risk and verification effort. Moving a public content library is not the same as moving payroll. Migrating 20,000 clean CRM contacts is not the same as preserving consent history across 450,000 records.

AI lowers the labor curve. It does not lower the consequence of a bad cutover.

A Safer Migration Plan for a Growing Business

Start with a read-only inventory. List the objects, fields, workflows, integrations, and reports that people actually use. Old systems often contain years of features that nobody needs to recreate.

Next, migrate one narrow slice. Pick a recent date range or one business unit. Run the source and destination in parallel long enough to compare outputs.

Then make reconciliation automatic. Every batch needs counts, checksums where practical, an exception report, and a rollback path. A human should review ambiguous mappings rather than letting the model guess.

Only after those checks pass should the migration expand.

This approach is less exciting than "one hour for $14." It is also how a business gets the cost advantage without betting its customer history on a demo.

When the Economics Have Changed Enough to Move

Vendor lock-in used to work partly because leaving cost too much. If AI cuts the transformation and scripting effort by 80 percent, a price increase that once had to be tolerated can justify a migration.

That changes build-versus-buy decisions too. A company can keep the reliable parts of its stack and replace only the overpriced layer. SaaStr did not rebuild e-signature or its CRM. It used agents to move data and remove one bottleneck.

We think that is the right posture. Do not rebuild infrastructure merely because coding became cheaper. Rebuild the part that is holding the business hostage.

At Axentia, we build migration tooling and the full-stack systems around it, including mapping logic, validation, exception handling, and the workflows that need to keep running after cutover. If an old platform is expensive to leave, book a call with us and we can estimate the smallest safe path out.