Framing
Most organizations building AI applications today make an implicit architectural choice early in the project: build on top of public APIs (OpenAI, Anthropic, Google, Azure OpenAI), or host inference on infrastructure they control (their own cloud environment, a colocation provider, or a specialized private AI cloud like Skyview’s).
This choice gets made early, often gets made without much deliberation, and is expensive to reverse. This piece lays out the real tradeoffs.
Public APIs: where they win
Speed to start. A working prototype on a public API is a day’s work. A working prototype on self-hosted infrastructure is a week, minimum.
Access to frontier capability. The most capable models, at any given moment, are typically available through public APIs first. Open-weight models follow, often with meaningful capability gaps.
No infrastructure investment. You pay per use. Capital expenditure is zero. Operations are outsourced.
Automatic capability upgrades. When the provider ships a better model, you benefit without engineering work.
These are real advantages. For a significant fraction of workloads, they are the deciding factors.
Public APIs: where they lose
Data handling becomes the bottleneck. Every public API provider publishes data retention, logging, and training policies. These are reasonable for most purposes. They are not reasonable for regulated workloads — healthcare, legal, public-sector, certain financial services — where procurement will read the policy and conclude that sending client data to a third party is not acceptable.
Per-token economics break at scale. At low volume, per-token pricing is cheap and predictable. At high volume — especially in agentic workloads that generate large token loads per interaction — it becomes a line-item concern and a forecasting problem.
Latency is not your problem to solve. Tail latency in a public API is outside your control. For workloads where consistent low-latency response matters, that’s a product issue.
You don’t own the stack. Pricing changes, policy changes, availability changes, and API changes are all the provider’s decisions. You adapt or re-architect.
Private AI cloud: where it wins
Data flows you can document. Every component is known. Every data path is named. The procurement review has something specific to evaluate.
Predictable economics. Workload-scaled pricing. Your bill moves with your capacity plan, not with your users’ behavior.
Regulatory alignment. Facility-level attestations — SOC 2, ISO 27001, HIPAA/HITECH, PCI DSS — are available. Your architecture can take advantage of them.
Independence from any single provider. Model selection is per-workload. Provider failure, pricing change, or policy change does not force a re-architecture.
Latency you can control. Direct-connect, private peering, and co-location options give you consistent performance for workloads that need it.
Private AI cloud: where it loses
Slower to start. Standing up a production-grade private inference environment takes engineering work.
Capability gap with frontier models. Open-weight models are good and getting better rapidly. They are not always at parity with the best closed-weight models for every task.
Operational complexity. Capacity planning, model updates, observability, and infrastructure management are real work. If you’re not a specialist, you’re paying someone else to be.
Up-front investment. Even in a colocation model, there’s a floor on capacity cost that doesn’t exist in a per-token model.
The hybrid answer most real systems end up at
In practice, the best production architectures are hybrid. The hot path — high-volume, repetitive inference — runs on private infrastructure. Specific high-value operations — frontier reasoning, top-tier vision analysis, low-frequency high-impact tasks — route to public APIs transparently and with client approval.
This is the architecture Skyview Labs runs for most client workloads. It is not ideologically pure on either side. It is designed to produce good production outcomes at reasonable cost with defensible data handling.
How to pick for your workload
Pick primarily public API if: your data is not sensitive, your volume is low or predictable, your workload benefits materially from frontier model capability, and speed to production is your primary constraint.
Pick primarily private cloud if: your data is sensitive or regulated, your volume is high or growing, your procurement process will scrutinize data handling, or your workload has specific latency or cost-predictability requirements.
Pick hybrid if: you’re serious about production, you have mixed workload characteristics, and you want the architecture to survive changes in the underlying market.
Most of our clients end up hybrid. Most of them find that decision easier when someone has thought through the tradeoffs specifically for their workload.