When deploying autonomous workflows in production, architects hit a stark crossroads: the localized privacy of small language models versus the raw reasoning capabilities of the cloud. This fast-paced lightning talk breaks down the technical, economic, and security boundaries that dictate how to split workloads between local environments (Phi via Ollama) and cloud engines (Azure).
We bypass the theoretical marketing hype to dissect real-world performance metrics: context window depletion, round-trip API latency, API token consumption costs, and strict data perimeters. Attendees will walk away with a production-tested decision matrix for securely routing tasks across a hybrid AI infrastructure.
What you will learn:

- The Scaling Formula: Mapping simple tasks to local hardware while escalating complex reasoning to Azure AI.
- Performance Metrics: Cold analytics comparing execution speeds, API round-trips, and compute overhead.
- Compliance Perimeters: Architecting a data wall to protect corporate assets while leveraging cloud intelligence.