Cloud or on-premise AI inference? Follow the utilisation.
Where you run AI models affects your monthly bill, your data exposure and how fast you can adopt better models. The honest answer depends on volume, sensitivity and the team you have. Here is how the options compare and how to choose without a hardware salesperson in the room.
For most companies, cloud inference wins until usage is high, steady and predictable.
Inference is the work of running a trained model to answer requests. You can pay a provider per token through an API, rent GPUs in the cloud and run models yourself, or buy hardware for your own data centre or office. Each step towards ownership lowers the price per request at scale and raises the fixed cost, the operational burden and the risk of sitting on idle capacity.
An API is almost always the right starting point: no hardware, access to the strongest models, and costs that scale down to zero when nobody is using it. Self-hosting starts to make sense when GPUs would be busy most of the day, when data cannot leave your environment, or when an open-weight model is good enough for a narrow, high-volume task.
The mistake we see most is deciding on ideology ("we must own our AI") or on a single month of API bills. Model prices change often, and open-weight models keep improving, so the break-even point moves. Revisit the decision at least yearly.
Cloud API vs cloud GPUs vs on-premise inference
Three realistic deployment models, compared on what actually drives the decision. Figures vary widely by provider and model, so treat these as directions rather than quotes.
| Managed model API | Self-hosted on cloud GPUs | On-premise hardware | |
|---|---|---|---|
| Cost model | Pay per token, no fixed cost | Pay per GPU hour, whether busy or idle | Large upfront purchase, then power and staff |
| Cheapest when | Usage is low, spiky or uncertain | Load is steady for much of the day | Load is high and constant for years |
| Model choice | Strongest proprietary models | Open-weight models you select | Open-weight models that fit your hardware |
| Data control | Provider terms and regions | Your cloud account and region | Never leaves your premises |
| Operational effort | Minimal | Significant: serving, scaling, monitoring | Highest: hardware, cooling, failures |
| Main risk | Bills grow with success, provider changes | Paying for idle GPUs | Obsolete or underused hardware |
Hybrid setups are common: sensitive or high-volume tasks on self-hosted models, everything else routed to an API.
What inference cost comparisons often leave out
Comparing an API price with a GPU rental rate is only the start. These items regularly change the answer.
Utilisation
A GPU running at a fraction of its capacity can cost more per request than an API. Batch jobs and steady traffic improve the maths; office-hours traffic hurts it.
People to run it
Serving, scaling, patching and monitoring models is real engineering work. Even a small self-hosted setup needs someone accountable when it fails at night.
Quality gap
An open-weight model may need more careful prompting, evaluation or fine-tuning to match a frontier API on your task. That work has a cost.
Upgrade cycles
Hardware bought today is typically depreciated over several years, while model requirements and GPU generations move much faster than that.
Redundancy
One GPU server is a single point of failure. Production usually needs spare capacity, which roughly means paying for hardware that sits waiting.
Building the plumbing
Gateways, logging, evaluation and routing still need to be built. AI-native engineering teams deliver this tooling with fewer engineer-hours, but it is not free.
When on-premise or self-hosted inference is worth a serious look
If only one of these applies, an API with good data terms is usually still the better option. If three or more apply, run the numbers properly.
Regulation or contracts forbid sending data to third parties
Some healthcare, financial and government workloads require data to stay in a specific environment or jurisdiction.
Your monthly API spend is large and still climbing
Once spend is significant and steady, the break-even against dedicated GPUs becomes worth modelling carefully.
The task is narrow and repetitive
Classification, extraction and summarisation at volume are often handled well by smaller open-weight models.
Latency or offline operation matters
Factory floors, field devices and real-time systems may need models close to where the data is produced.
You already run infrastructure competently
Teams with existing on-premise operations, monitoring and on-call rotas absorb the extra effort far more easily.
Workloads can be batched
Overnight document processing or bulk enrichment keeps GPUs busy and makes owned or reserved capacity efficient.
A sensible path from API to self-hosting
Start on an API and log everything
Record tokens, latency and cost per task. Real usage data is worth more than any forecast made before launch.
Optimise before migrating
Caching, shorter prompts and routing simple requests to cheaper models often reduce spend enough to postpone any infrastructure change.
Benchmark open-weight models on your data
Test whether a self-hostable model meets your quality bar on real examples, not on public leaderboards.
Model total cost over three years
Include GPUs or hardware, redundancy, staff time, power and upgrades against projected API spend, then pilot the workload that benefits most.
Questions we often hear
Is it cheaper to run AI models on-premise or in the cloud?
For low or unpredictable usage, cloud APIs are nearly always cheaper because you pay nothing when idle. On-premise becomes cheaper only when hardware stays highly utilised for years and you already have the skills to operate it. The break-even point depends heavily on model size, traffic pattern and current API prices.
When should a company self-host an LLM?
Self-hosting makes sense when data must stay in your environment, when a narrow high-volume task can be served by an open-weight model, or when API spend is large and steady enough that dedicated GPUs pay back. If none of these apply, a business API plan with clear data terms is usually simpler and safer.
Are cloud AI APIs safe for confidential data?
Major providers offer business terms that exclude your data from training, regional processing and retention controls. Whether that is sufficient depends on your contracts and regulators. Read the data processing terms carefully and involve your compliance lead before sending personal or regulated data.
What hardware do you need to run an LLM on-premise?
It depends on the model size and traffic. Small models can run on a single modern GPU, while large models need multiple high-memory GPUs plus fast networking and cooling. Techniques such as quantisation reduce memory needs, but production also requires spare capacity for failures and peaks.
Can you mix cloud and on-premise AI inference?
Yes, and many teams do. A routing layer can send sensitive or high-volume requests to self-hosted models and everything else to a cloud API. This keeps the fixed infrastructure small while protecting the data and workloads that justify it.
Related reading and tools
Weighing up where to run your AI models?
Share your current usage, data constraints and what you are paying today. We will model the realistic options with you and say plainly if staying on an API is the smarter choice.
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