Is Private AI the Right Move for You?
We would rather tell you in the first conversation than the sixth month. Answer four questions below for an instant read, then book a working session to pressure-test it.
Engineered on the enterprise stack IVI already runs
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The single strongest signal is what you already spend on frontier APIs for workloads that touch proprietary or sensitive data. Combine that with how much of your differentiation lives in that data, and the picture gets clear fast. This is directional, not a quote, and it is deliberately honest about where private AI is not the right call.
What the Assessment Actually Looks At
The assessment is a working session, not a sales pitch. We map four things: your candidate workloads and which ones touch proprietary or regulated data; your true consumption, since per-token API pricing hides it; your data-sovereignty and governance requirements; and your in-house ML capability, which decides where you sit on the co-managed spectrum, from full-solution to platform-only.
Where frontier APIs still lead
- •General-purpose reasoning on novel tasks
- •Low-volume, low-sensitivity workloads
- •Commodity content generation
- •Experimentation and prototyping
Where private models pull ahead
- ✓Tasks fine-tuned on your proprietary data
- ✓High-volume production workloads
- ✓Data-sovereign or regulated environments
- ✓Predictable, controllable economics
See What the Compute Actually Costs
Before you book, run your own spend through the break-even calculator and see where private infrastructure beats API pricing.
Open the economics calculator →
How the Engagement Works
Four stages, one team, from first conversation to steady-state operations. No handoff between the people who scoped it and the people who run it.
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01
Assess
Readiness assessment. Workload triage. Data sensitivity scoring. Sizing framework.
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02
Deploy
Environment provisioning at the rented GPU provider. Base model selection. Initial fine-tune. Production cutover.
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03
Baseline
Six months of live workload telemetry via Aegis PM. Utilization curves, cost per workload, growth trajectory.
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04
Migrate
On-prem cluster design against baseline. Colocation buildout. Controlled workload cutover. No production disruption.
Is This the Right Fit?
Private AI isn't the right call for every team, and we'd rather tell you that in the first conversation than the sixth month. Here's how we think about fit.
- ✓You spend $15K+ per month on frontier APIs touching proprietary data
- ✓Your differentiation lives in your data, processes, or domain knowledge
- ✓Executive alignment on private AI as a strategic direction
- ✓You don't want to hire an ML platform team to get there
- −API spend under $8K per month with no sovereignty driver
- −Exploring, not committing to production AI
- −Workloads are commodity and well-served by APIs
Continue reading · The private AI series
Your competitive moat is training someone else's model
Why crown-jewel data doesn't belong on shared frontier infrastructure.
The right sequence for building private AI infrastructure
Rent, baseline, own: size for evidence, not estimates.
What GPU compute actually costs
Hyperscalers, specialty providers, and API spend, compared honestly.
Is Aegis Private AI the Right Fit for You?
A 45-minute conversation with an IVI solution architect. No slideware, just informative conversation.
Get a readiness assessment →Last reviewed July 07, 2026 · Next review September 30, 2026 · Content owner IVI