Self-Hosted LLMs vs Cloud APIs for Enterprise
How to choose between self-hosted language models and hosted cloud APIs, covering data control, cost shape, operations and a decision framework.
By NEXURA.AI Team · Published · Updated · 3 min read
Direct answer
Use a hosted cloud API when you need speed to production, variable volume and the strongest available models. Self-host when data cannot leave your network, volume is high and steady, or you must pin an exact model version. Most B2B automation starts on a cloud API with data minimisation, and moves a workload in-house only when a concrete constraint demands it.
The two architectures
Cloud API Self-hosted
--------- -----------
Your app Your app
| HTTPS, prompt + data | private network
v v
Provider-run model Model server in your VPC / on-prem
(provider operates GPUs) (you operate GPUs, scaling, patching)The difference is who operates the inference layer, and where your prompt data travels. Everything else follows from that.
Side-by-side comparison
| Factor | Cloud API | Self-hosted |
|---|---|---|
| Time to first result | Hours | Weeks |
| Cost shape | Pay per token; scales with usage | Fixed infrastructure; scales with capacity |
| Data path | Prompts leave your network (contractual controls) | Prompts stay inside your network |
| Model quality ceiling | Typically the most capable models | Limited to models you can run and license |
| Operations burden | Low | High: GPUs, scaling, upgrades, monitoring |
| Version control | Provider deprecates on its schedule | You pin versions as long as you like |
| Compliance posture | Depends on provider terms and your data | Easier to satisfy strict residency rules |
Cost: the break-even logic
Per-token pricing is cheap at low volume and linear at high volume. Self-hosting has a high fixed floor (hardware or reserved GPU instances, plus engineer time) and a low marginal cost. The break-even depends on three inputs you can measure:
- Monthly token volume for the workload, not the whole company.
- Utilisation: self-hosted GPUs idle overnight still cost money.
- Engineering time: include the on-call cost of running inference.
As an illustration of the method (not a quote), if a workload costs a few hundred dollars per month on an API, a dedicated GPU server will almost never pay back. If it costs tens of thousands, the comparison is worth modelling properly. Always price the people, not only the hardware.
When self-hosting is justified
- Data residency or contractual limits forbid sending the data to a third party.
- Steady, high volume keeps hardware well utilised.
- Version pinning is required for validated or regulated processes.
- Latency requirements need inference next to your own systems.
When a cloud API is the better call
- The workload is spiky or still being validated.
- You need top-tier reasoning quality for hard tasks.
- You have no team to operate GPUs reliably.
The hybrid pattern
Many teams split by sensitivity. Route routine, low-risk tasks to a hosted API and keep regulated data on a model inside the private network. A thin routing layer in your own code decides per request, and the rest of the application does not care which model answered.
Reducing risk on either path
- Send the minimum data needed; strip identifiers before the model call where possible.
- Abstract the model behind your own interface so you can switch providers or move in-house.
- Log prompts and outputs under a defined retention policy.
- Test with your own evaluation set before and after any model change.
To be plain about our own position: NEXURA.AI is not SOC 2 certified, so we recommend asking any vendor, us included, for their current certifications and data-handling terms in writing. If you want help applying this framework to a real workload, book an audit.