Choosing how to host an AI model comes down to three broad options: a managed inference API where a provider runs the model for you, self-hosted infrastructure where you run the model on your own or rented GPUs, and on-device inference where the model runs locally on the end user's hardware. Each has a genuinely different cost, latency, and operational profile, and the right choice depends more on your specific workload than on any general rule.
What changed in 2026
- Serverless GPU inference matured, narrowing the gap between managed API convenience and self-hosted cost control for teams running open-weight models.
- On-device inference became viable for a wider range of tasks as smaller, more capable models and better mobile and edge hardware closed some of the capability gap with cloud-hosted models.
- Multi-provider and multi-model routing became common practice, with teams splitting traffic across providers or between managed and self-hosted infrastructure based on cost and latency needs per request type.
The three options, briefly
A managed inference API (the major model providers' hosted endpoints) requires no infrastructure management and bills per token or per request — the fastest path to production, at the cost of less control over latency, data handling, and model customization. Self-hosted infrastructure means running an open-weight model on GPUs you own or rent, giving full control over data residency, customization, and cost structure at high utilization, at the cost of real operational burden. On-device inference runs a (usually smaller) model directly on the user's device, eliminating network latency and per-request cost entirely, at the cost of using a less capable model than what cloud hosting can offer. See our on-device vs cloud comparison for more on that specific tradeoff.
Hosting options compared
| Factor |
Managed API |
Self-hosted |
On-device |
| Setup effort |
Minimal |
Significant |
Moderate, app-dependent |
| Cost at low volume |
Low |
High (idle GPU cost) |
Low, but device-constrained |
| Cost at high volume |
Can exceed self-hosted |
Often lower at high utilization |
Fixed, no marginal cost |
| Data control |
Depends on provider terms |
Full control |
Full control, stays on device |
| Latency |
Network round-trip |
Network round-trip, tunable |
None, fully local |
| Model capability ceiling |
Highest, frontier models |
High, open-weight models |
Lower, constrained by device |
Choosing based on workload
Start with a managed API for anything early-stage or unproven — it removes infrastructure risk while you validate the product. Move toward self-hosting once volume is high and consistent enough that GPU utilization stays reasonably high, or when data residency or customization requirements rule out sending data to a third party. Consider on-device inference specifically for latency-critical or offline-capable features, or when per-request cost at scale would be prohibitive with cloud hosting.
Cost and ops tradeoffs
Self-hosting's real cost driver is utilization, not sticker price per GPU-hour — idle capacity between traffic spikes is where self-hosted budgets quietly leak relative to pay-per-use managed pricing. Track actual cost per request, not just infrastructure spend, using the same discipline covered in our cost monitoring guide. Self-hosting also adds real operational surface: model updates, scaling, failover, and security patching become your team's responsibility rather than a provider's.
FAQ
Is self-hosting always cheaper at scale?
Often, but not automatically — it depends on sustained utilization. Self-hosted GPUs sitting idle much of the time can cost more than pay-per-use managed pricing at the same actual usage volume.
Can I mix hosting strategies for different parts of the same product?
Yes, and many teams do — for example, a managed API for complex reasoning tasks and a smaller self-hosted or on-device model for simple, high-volume tasks like classification.
Does on-device inference mean no cloud calls at all?
Not necessarily. Some products use on-device models for common cases and fall back to a cloud-hosted model for harder queries, blending the two approaches.
What is the biggest hidden cost of self-hosting?
Idle GPU capacity and the engineering time spent on scaling, failover, and maintenance — costs that a managed API absorbs into its per-token price.
Where to go next