19 Distributed Patterns to Reduce Non-Model Latency in AI Apps
bibryam · x · 2026-08-30
This post maps 19 distributed-system patterns to reduce non-model latency in AI applications. While models and agents create new critical paths, the underlying optimization patterns remain familiar. The patterns are categorized into four strategies:
- Locality: Bringing requests and data closer
- Work Reduction: Decreasing load via caching, compression, and deduplication
- Concurrency: Executing independent work in parallel
- Anticipation: Predicting and pre-executing predictable work
The article illustrates these patterns through comparisons between traditional and AI applications (e.g., RAG, model gateways, orchestration loops), emphasizing the need to optimize the full end-to-end path rather than isolated components.
Related event: 19 Architectural Patterns for Cutting Non-Model Latency in AI Apps(5 posts)→
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