Recent global outages affecting major internet infrastructure providers exposed a critical weakness in how many companies use AI. As soon as routing, DNS, or CDN layers went down, thousands of businesses relying on public AI APIs like ChatGPT experienced slowdowns, errors, or complete downtime—even if the AI providers themselves were fully operational.
These incidents highlight a fundamental architectural truth:
public AI depends on a long chain of external services, and any weak link can break your entire workflow.
Private LLMs avoid this risk entirely.
A request to a public AI model isn’t a simple call. It travels through:
your app
DNS
routing
CDNs/security layers (e.g., Cloudflare)
API gateways
the provider’s internal infrastructure
Every one of these layers is a potential failure point. During recent outages, many companies didn’t lose access because the AI model was down—but because traffic never reached the provider.
If one layer fails, your entire AI stack fails.
For companies that built key processes on public APIs, the impact is immediate:
Customer support tools stop responding.
Document processing pipelines freeze.
Internal AI assistants become unavailable.
Developer copilots stop generating or reviewing code.
Risk-scoring and compliance automation halts.
E-commerce personalization breaks, hurting conversions.
Outages are no longer just “IT problems.”
They affect customers, employees, revenue, and compliance.
Private LLMs run on infrastructure you control—on-premise or in your private cloud.
No public APIs. No multi-layer dependency chain. No waiting on someone else’s incident report.
They offer three strategic advantages:
Inference happens locally. If the internet goes down, your AI stays up.
The path becomes:
Your App → Your Network → Your Model
No CDNs. No external DNS. No third-party gateways.
Sensitive information never leaves your environment—critical for finance, healthcare, public sector, legal, or IP-heavy industries.
Private LLMs require planning and integration, but deliver something public APIs cannot: control.
Local inference keeps workflows running during external outages.
No dependency on global routing or CDN infrastructure.
No vendor-side rate limits or API disruptions.
Predictable latency and performance.
Alignment with internal redundancy and failover standards.
Full sovereignty over data and model behavior.
In short: fewer external dependencies = fewer surprises.
Public AI APIs are great for prototyping and non-critical tasks.
But when AI becomes part of your core operations, relying on external infrastructure exposes your business to unnecessary risk.
Recent outages underscore a key lesson:
If a workflow must always work, it needs to run on infrastructure you control.
Private LLMs deliver that control—ensuring reliability, continuity, and stability even when the rest of the internet falters.