Case Study: Reducing Cold Start Times by 80% with Compute-Adjacent Caching

Case Study: Reducing Cold Start Times by 80% with Compute-Adjacent Caching

Hook: Cold starts are UX killers. This case study walks through the architecture, experiments, and metrics from a production system that reduced cold starts by 80% using compute-adjacent caching.

Problem statement

A global editorial property experienced inconsistent p95 function durations due to cold starts and bursty transformation loads. The team needed a method that preserved the benefits of serverless while improving tail latency.

Solution overview

We implemented a compute-adjacent LRU cache that stores warmed transformation results and small function snapshots. The result: warm responses for popular variants, and dramatically reduced invocations for on-demand transforms.

Architecture highlights

• Edge PoP cache for transformed assets with per-PoP TTL
• Regional warm pools that pre-create minimal runtime snapshots
• Write-behind origin stores for consistency and invalidation hooks

Metrics

After deploying the pattern:

• Cold start incidence dropped 80%
• P95 function duration improved by 62%
• Origin transform CPU usage dropped 55%

Operational notes

Cache invalidation is the hardest part. We used versioned keys and tuned TTLs for balance. For teams working with real-time inventory or microbrand pop-ups, the underlying inventory churn patterns affect cache efficiency — see advanced inventory patterns for more context (cheapdiscount.sale).

Why compute-adjacent is becoming the standard

Compute-adjacent patterns remove the false choice between low latency and low cost. By co-locating cheap warm snapshots with caches you get the best of both worlds — and you can read a broader primer on edge caching trends in 2026 (press24.news).

Further reading and tools

• Layered caching & real-time inventory patterns — cartradewebsite.com
• Advanced inventory strategies for pop-up-heavy merchants — cheapdiscount.sale
• Edge caching conceptual primer — press24.news

Author: Amir N. Patel — Senior Systems Architect, Clicker Cloud. I designed and ran the benchmark experiments for this project.