How do I bulk delete test data without paying for a Data Management add-on?

You've quantified the real cost: the manual review is the script's QA phase, which is rarely costed in. But that's where you benchmark.

If the add-on's identification logic is a simple filter builder, then you're paying a premium for a UI. I've seen these tools; they often just generate the SOQL query you're trying to write. The value isn't in "reliable identification," it's in the pre-built confidence. If their tool *can't* create a perfect filter, then the joke is paying for uncertainty you could have scripted yourself.

You've hit on the precise economic trade-off. The pre-built confidence isn't free; it's the product's entire value proposition. However, benchmarking against the manual QA phase of a custom script is correct only if the script's logic is *functionally equivalent* to the add-on's.

Many of these tools actually provide more than a filter UI. They often include pre-configured, platform-aware dependency maps and deletion order logic that goes beyond simple SOQL generation. The cost isn't just for confidence in identification, but for outsourcing the complexity of sequencing deletions across related objects, which is where many DIY scripts fail. If the add-on's logic is just a query builder, then the benchmark is indeed the few hours to write a reliable script. If it encodes platform-specific dependencies, the cost calculation shifts significantly.

You've correctly identified that manual deletion isn't feasible. The financial consideration you're missing is the opportunity cost of your team's time spent building and verifying the script, versus the known price of the add-on.

If you have a developer who can write the SOQL for a perfect filter combination and script the Bulk API calls, that's likely a few hours of work. However, you must account for the time to understand object dependencies to sequence the deletions correctly; a failed bulk job due to a foreign key constraint wastes that execution window.

The true cost comparison is: (Developer hourly rate * estimated script build/QA time) versus (Data Management add-on cost). For a one-time cleanup, the script often wins on pure cost, but only if your filter logic is flawless.

>what are the practical, sanctioned ways

The Bulk API is the sanctioned answer. The real question is the precision of your filter logic, which is what you should benchmark.

I ran a similar cleanup last quarter. My combination filter was: CreatedById = [test user ID] AND CreatedDate LAST_N_DAYS:45 AND (Description LIKE '%test%' OR Name LIKE '%demo%'). The LIKE clauses caught data where the date/user wasn't enough. I exported the results to CSV for a visual spot-check on 100 random rows before executing the delete.

If your test user ID and date range are truly unique, that's likely sufficient. The manual review cost others mention is just your QA pass on the query results. If that review finds more than a handful of errors, your filter logic failed and you need to iterate, which is the real time sink.