The break-even point: when owning your clinical AI costs less than renting it

The logic is simple. Divide the fully loaded monthly cost of running your own AI by what a pay-per-use service would charge per request. The result is your break-even volume. Below it, paying per use is cheaper. Above it, owning the capacity wins, and the gap widens with every additional request.

Why Many Private AI Projects Never Break Even

The fixed cost line is where most business cases go wrong. Accelerators left running around the clock, oversized hardware for simple tasks, and a platform team to keep everything patched and available can make owned AI so expensive that it never catches up with pay-per-use pricing.

How Platform Engineering Moves the Line

The goal is to push the fixed cost down, so break-even arrives sooner:

• Follow the clinic’s rhythm. Capacity stays warm during clinic hours and is released at night and at weekends.
• Answer small first. Small models handle most requests on modest hardware; large models run only on escalation.
• Share the hardware. Several models and tasks share one accelerator instead of each needing its own.
• Serve more per accelerator. Batching requests and reusing repeated context raise throughput.
• Move batch work to cheaper capacity. Document ingestion and backlogs run overnight at lower cost.
• Remove the staffing line. An operated platform replaces the team you would otherwise need to hire.

Be Honest About Volume

For a very small practice, pay-per-use may remain cheaper on cost alone. That is worth saying plainly. But for hospitals and groups with steady daily volumes, a lean private platform usually crosses the line, and sovereignty often justifies private AI even before it does.

The best practice is to calculate break-even with your own figures, then recalculate as usage grows. Our break-even calculator is a good place to start.