AI Answer Library
Short answer
Answer the data-residency question first, then do the math. If the data is bound by compliance — classified material, regulated infrastructure, medical records, financial customer data — self-hosting is mandatory and no cost comparison is needed. Otherwise, if volume is low and the use case is still being validated, call a public API. Hardware and operations for self-hosting only amortize under sustained, high-frequency usage. The common path is to prove the business on an API, then migrate once volume and compliance pressure grow.
Do not reduce this to "is it expensive". Four variables actually drive the decision: data sensitivity, call volume, how strong the model must be, and whether you have people who can operate an inference service long-term. The first two decide whether to self-host; the last two decide whether self-hosting goes well. Most failed on-premise projects did not buy the wrong hardware — they had nobody accountable for upgrades, monitoring and capacity after the purchase.
| Dimension | Public cloud API | Self-hosted deployment | Hybrid |
|---|---|---|---|
| Data leaving the network | Yes — vendor terms and compliance posture must be reviewed | No — data stays inside your network | Route by data classification; sensitive content stays in |
| Upfront investment | Near zero — sign up and start | High — servers, accelerators and facilities up front | Medium — size hardware only for the sensitive slice |
| Marginal cost per call | Per-token billing, grows linearly with volume | Marginal cost approaches electricity; cheaper at scale | Push steady high-volume load local to cut the total |
| Time to first value | Prototype the same day | Bounded by procurement and implementation | Start on API, add local capacity later |
| Ceiling on model capability | Higher — latest flagship models available | Bounded by your memory budget and open-model progress | Hard problems to the API, routine work local |
| Operational burden | Low — the vendor owns availability | High — monitoring, upgrades and scaling are yours | Medium, but a unified gateway becomes necessary |
Four situations need little deliberation. One: compliance explicitly forbids data leaving the premises — classified environments, parts of healthcare and finance, or contracts that mandate localization. Two: volume is large and steady, such as an assistant everyone uses daily or a bulk document pipeline; that is the load profile that amortizes hardware. Three: latency-sensitive or offline-capable requirements, like a shop floor or an air-gapped network. Four: you need long-term determinism and cannot afford a vendor repricing, rate-limiting or deprecating a model version. Conversely, if you are still validating whether anyone will use the feature at all, an API is the cheaper way to find out.
On the self-hosting side, the usual omissions are facilities and power, the redundancy required for real uptime (one dead node taking the whole service down is not acceptable), re-evaluation and migration at each model generation, and the time of at least one engineer who can debug an inference service. On the API side: the redaction and audit pipeline compliance demands, rate-limit exposure under traffic spikes, and the refactoring cost if you later have to move off it. A more practical approach is to run one or two months of real traffic, capture actual daily call volume and average input/output lengths, and only then do the math — everything before that is guesswork.
Where this applies
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