Systems integration
Local inference versus API: where the break-even actually sits
Running models on your own infrastructure is conditionally correct, not universally so. The break-even depends on sustained token volume, regulatory constraint, and whether you have the MLOps capability in-house.
Local deployment is an infrastructure investment, not an AI strategy. Below a volume threshold, managed API tiers deliver more capability per dollar, and the capital spent on a cluster buys latency and control you were not short of.
Above it, and particularly where data cannot leave the estate, the arithmetic reverses. The mistake is treating the decision as ideological rather than as a threshold question with an answer specific to your workload.
The conditions that actually decide it
| Condition | Points to local | Points to API |
|---|---|---|
| Sustained token volume | Several million tokens per day, verified over months | Spiky or modest volume |
| Data residency | Regulatory or contractual bar on data leaving the estate | No such constraint |
| MLOps capability | In-house team who can operate and update it | No one whose job this would be |
| Task profile | Narrow, stable, well-characterised tasks | Broad or shifting task mix needing frontier capability |
The practical path is tiered
Most estates end up mixed rather than binary: local inference for the high-volume, well-characterised, residency-constrained work, and managed APIs for everything that needs the capability ceiling.
That is an integration outcome, not a procurement one. It requires a routing layer, a shared governance point, and someone accountable for the boundary between the two.