Comparison
Local AI server vs cloud AI
When running an LLM on your own server beats calling a cloud API, and when it doesn't.
In short
A local AI server runs an LLM on hardware you control, so data stays in and cost is fixed; cloud AI calls a model on a provider's servers, billed per token with data leaving your perimeter. Local wins on control, residency, and sustained cost; cloud wins on instant scale and zero upfront hardware.
When local wins
- Sensitive data: When prompts or documents can't leave your perimeter.
- Sustained usage: Heavy, steady workloads where per-token billing becomes expensive.
- Air-gap needs: Environments with no permitted external connectivity.
When cloud wins
- Spiky or low usage: Occasional workloads where you don't want to provision hardware.
- Fast experimentation: Prototyping before committing to infrastructure.
The cost crossover
Cloud per-token pricing scales with usage forever, while a local server is a fixed cost. At sustained enterprise volume there's a clear crossover after which owning a local server is substantially cheaper, and you keep the asset. See our on-prem AI cost guide for the breakdown.
Local AI server vs cloud AI
| Local AI server | Cloud AI | |
|---|---|---|
| Data location | Your perimeter | Provider's servers |
| Cost model | Fixed, owned | Per-token, recurring |
| Latency | Local, predictable | Network-dependent |
| Scales instantly | Limited by hardware | Yes |
| Air-gapped | Yes | No |
| Own the model | Yes | No |
What this looks like with Locai
An AI computer is only as useful as what comes inside it, the model, the application layer, and the deployment story.
Locai Labs believes organisations should own their intelligence. Renting access to a general-purpose model that lives on someone else's servers is fine for low-stakes work; for the AI that touches your data, your customers and your decisions, the model itself should be yours. That is the bet behind everything we build.
It is also a bet that an expert model beats a generalist on the work that actually matters to your business. A smaller model trained on your data, your language, your workflows and your edge cases routinely outperforms much larger generalists on the tasks you care about, and it does so on infrastructure you control. The goal is not the biggest model; the goal is the right model for your business.
And it is deployed sovereignly: an owned model that runs inside your perimeter, on-prem via Locai One, in your private cloud tenant, in a UK sovereign cloud, or fully air-gapped, depending on your residency and security requirements. Your prompts, your documents and your outputs stay inside your environment, under UK jurisdiction, with a data path designed to fit GDPR and the procurement standards regulated organisations are held to.
Frequently asked questions
Local vs cloud AI, which is better?
It depends on usage and sensitivity. Local wins for sensitive data, sustained usage, and air-gapped needs; cloud wins for spiky, low-sensitivity, experimental workloads.
Is a local AI server worth it?
For sustained enterprise usage, yes, the fixed cost typically beats years of per-token API fees, and you keep an owned, private asset.
Can I run an LLM locally?
Yes. Modern open models run on local GPU hardware; Locai One packages a sovereign model and serving as a turnkey local AI server.
What hardware do I need?
It depends on model size, a 35B model runs on a single A100 80GB server, larger models use multi-GPU nodes.
Book a sovereign AI briefing
A 30-minute session on owning your model: deployment options, the data path, and a clear cost range for your use case.