Our Decision Tree: Self-Host or Cloud?

The Opteria Decision Tree is a four-question method that pragmatically routes each individual AI workload to one of three paths (cloud, self-hosted, or hybrid) along data sensitivity, volume, vendor risk, and operating capacity.

When to Use It

Whenever you decide the architecture of a concrete AI use case, not “AI strategy” in the abstract. The tree works per workload because the right answer differs per workload. Forcing a whole company into one camp (“all cloud” / “all self-hosted”) is almost always the more expensive choice.

The Decision Tree: Four Questions in Order

Run the questions for one workload in sequence; the first clear “yes” often sets the direction.

Data sensitivity: does this workload process data that shouldn’t leave the building? (GDPR personal data, critical IP, contract or health data.) If yes: lean self-hosted, regardless of cost; the most expensive scenario is a breach, not a GPU bill. Proceed to Q4.
Volume: does this workload run sustained above the break-even volume? Not the pilot volume, but the sustained one. If yes: lean self-hosted on cost. If no (low/spiky): lean cloud. (Compute break-even via Cloud LLM vs. Own Infrastructure: The Honest Cost Calculation.)
Vendor risk: how critical is this process to a single provider’s price, rate limits, and model lifecycle? If business-critical and hard to replace: argues for self-hosted or at least a fallback. If uncritical: cloud is fine.
Operating capacity: can we run an inference environment reliably, ourselves or with a partner? If no with no fix in sight: cloud, even if 1–3 favor self-hosting; a poorly run machine is no sovereignty gain. If yes: self-hosting is viable.

Reading the Result

Majority self-hosted + operations secured → in-house.
Majority cloud → cloud.
Mixed (e.g. sensitive but low volume and no operations) → hybrid: self-host or EU-residency that one workload, keep the rest in the cloud. Hybrid isn’t a weak compromise; it’s the most common correct answer.

What Makes It Work

Per workload, not blanket. The tree forces case-by-case judgment exactly where blanket decisions cost money and security.
Operations as its own question. Q4 prevents the most common mistake: wanting to self-host without being able to run it reliably.
Honest inputs. The tree is only as good as measured volume and fully costed numbers. We run our own production on one node plus staging and know the operating reality first-hand, so Q4 isn’t theory.

Common Mistakes

Confusing pilot volume with sustained volume (mislocating break-even).
Skipping Q4: deciding to self-host without settling operations.
Forcing the whole operation into one camp instead of deciding per workload.
Zeroing out operating cost, making an owned machine look artificially cheap.

Next Step

Bring us your three to five most important AI workloads. We run each through the decision tree and give you an honest, per-workload architecture: cloud, self-hosted, or hybrid.

Request a decision-tree session →