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Comparison

Private AI vs cloud AI APIs

Should your AI run on your own infrastructure, or call a cloud API like OpenAI? An honest comparison — and when each one is the right call.

Most teams reach for a cloud API first; it is fast to start. But once your data is sensitive, your volume grows, or you need real control, running models on your own infrastructure changes the maths. Here is how the two compare on what actually matters.

Side by side

What mattersPrivate / on-premCloud AI API
Where your data goesStays on your infrastructureSent to the provider
Regulated / sensitive dataEasier — data never leaves your controlHarder — depends on the provider's terms
Cost modelCompute you own; predictable at scalePer-token; grows with usage
CustomizationFull — fine-tune and own the weightsLimited to the provider's API
Ownership & lock-inYou own the model and the codeTied to one provider
Time to startSlower — needs setupFast — an API key
Best forSensitive data, scale, controlPrototypes, low volume, non-sensitive data

When private wins

  • Your data cannot legally or contractually leave your environment.
  • You are in a regulated industry (finance, healthcare) and need to prove where data lives.
  • Your usage is high enough that per-token API costs add up.
  • You need to fine-tune on your own data and keep the result.

When a cloud API is fine

  • You are prototyping and want to move fast.
  • Your volume is low and the data is not sensitive.
  • You need a capability no open model matches yet.
  • You are testing whether AI helps before investing in infrastructure.

Our honest take

There is no single right answer, and we will tell you when a cloud API is the better choice for your case. But if your data is sensitive or your volume is real, private deployment usually wins on cost, control, and compliance. That is the work we do.

Not sure which fits your case?

Take the 2-minute readiness check, or book a call and we will walk through it with your data and constraints.