What Type of AI Usage? Deployment Patterns and Implementation Considerations

Deciding whether to deploy an LLM locally or in the cloud isn't binary—it depends on understanding different usage patterns and their technical requirements. This taxonomy breaks down AI implementation types and the architectural implications of each choice, helping engineers make informed decisions about local deployment viability.

The framework covers various scenarios from real-time inference with strict latency requirements to batch processing, streaming outputs, and interactive chat interfaces. Each pattern has different resource, consistency, and scalability implications that directly affect whether local deployment makes sense. For teams evaluating whether to run inference on-device, understanding these patterns helps identify which workloads are actually suitable for self-hosted infrastructure.

This reference material is essential context for anyone architecting local LLM systems, as it provides language and structure for discussing trade-offs. It helps clarify when local deployment provides genuine advantages versus when you're overcomplicating infrastructure unnecessarily.

Source: Hacker News · Relevance: 7/10