Executive summary
4
practical conditions that decide between local and API deployment
Key conclusions
- Local deployment is an infrastructure investment. Below a certain volume, managed API services give more capability per dollar.
- The break-even point depends on sustained token volume, data-residency constraints, in-house MLOps capability and how narrow or broad the mix of tasks is.
- Most organisations that get this right use both: local inference for high-volume, well-understood work, and managed APIs for work that needs the most capable models.
Local deployment is an infrastructure investment first and an AI strategy second. Below a certain volume, managed API services give more capability per dollar. Money spent on a cluster then buys latency and control that you did not need.
Above that volume, and especially where data cannot leave your own systems, the economics reverse. The decision depends on a threshold specific to your workload. Treating it as a matter of principle is a mistake.
Four conditions decide between local and API deployment
| Condition | Points to local | Points to API |
|---|---|---|
| Sustained token volume | Several million tokens per day, verified over months | Spiky or modest volume |
| Data residency | A regulation or contract prevents data from leaving your premises | 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 |
Most organisations should combine local and API deployment
Most organisations end up with a mix. They use local inference for work that is high-volume, well understood and subject to data-residency rules, and managed APIs for work that needs the most capable models.
This mix is an integration outcome. It requires a routing layer, a shared point of governance and a named person accountable for the boundary between the two.
