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.
| What matters | Private / on-prem | Cloud AI API |
|---|---|---|
| Where your data goes | Stays on your infrastructure | Sent to the provider |
| Regulated / sensitive data | Easier: data never leaves your control | Harder: depends on the provider's terms |
| Cost model | Compute you own; predictable at scale | Per-token; grows with usage |
| Customization | Full: fine-tune and own the weights | Limited to the provider's API |
| Ownership & lock-in | You own the model and the code | Tied to one provider |
| Time to start | Slower: needs setup | Fast: an API key |
| Best for | Sensitive data, scale, control | Prototypes, 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.
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.
Take the 2-minute readiness check, or book a call and we will walk through it with your data and constraints. Get your AI score
What buyers ask before they choose.
For most business tasks, yes. An open model on your own hardware handles document questions, classification, extraction and drafting at a quality a reader cannot separate from a frontier API. The gap shows on the hardest reasoning, and it narrows every few months. The honest test is your own evaluation set, not a benchmark.
Less than people expect. A single workstation GPU runs a quantised mid-size model for a department. A server with two cards serves a company. The sizing follows how many people ask at once, not how big the company is, so we size it against your real question volume.
Not automatically. It is a processor relationship with a transfer question attached, and it works when the provider terms and the transfer basis are right. It stops working when a contract or a regulator says the data cannot leave your estate at all. That is the line that decides this, not the technology.
Yes, and it is often the right order. Prove the use case against an API, then bring it in-house when volume or sensitivity justifies the hardware. Keep the prompts, the evaluation set and the retrieval layer portable from day one and the move costs weeks rather than a rebuild.
Hardware you buy once, plus power and someone to keep it patched. A cloud API costs nothing up front and charges per use. The crossover depends on volume: low and occasional favours the API, steady and heavy favours your own hardware.
Bring us the problem nobody has cracked yet.
We are a small team of senior specialists. We pick the right model and the right layer, and we build the least machinery that does the job. You get a call with an engineer, not a sales deck.