Bottom line: Organizations should evaluate dependency on public AI APIs as an operational risk and incorporate private or self-hosted models into their IT risk strategy.
Outages at Google and Anthropic highlight the operational risk that organizations face when relying on public large language models. Control over critical AI infrastructure does not rest with the operators themselves.
Recent disruptions at Google and Anthropic expose a central challenge of API-based AI usage: organizations are directly dependent on the availability, performance, and policy changes of providers. These dependencies cannot be fully mitigated contractually or controlled internally.
For CTOs, this creates a classic IT risk scenario. When public AI services fail or become restricted—for instance, due to regulatory requirements—the impact is immediate on productive business processes. Using Claude, GPT, or similar public APIs is therefore not merely a convenience choice but creates operational dependencies that may not align with the organization’s own risk tolerance.
Private AI models—whether hosted in-house or on private cloud infrastructure—shift control back into the organization’s own responsibility domain. This requires additional investment in infrastructure, training, and operations, but offers predictable risks and autonomous decision-making authority over availability, data processing, and compliance.
Source: itwelt.at · Published 30 June 2026
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