Caching Strategy 2026

CDN; app cache; DB cache.

Overview

Modern caching is layered. CDN at the edge serves static assets and cacheable API responses; per-process app cache absorbs hot reads; shared DB cache (Redis or Memcached) absorbs cross-process traffic. Each layer has its own invalidation strategy and its own consistency trade-offs. Picking one layer and ignoring the others leaves easy performance on the table.

• CDN, app cache, DB cache. Three layers, three different purposes. Layered together they cover the full request path.
• CDN for static and edge-cacheable. Per-asset cache at the edge. User latency drops; origin egress drops.
• App cache for per-process hot reads. In-memory per process. Sub-millisecond hits at the cost of duplication across replicas.
• DB cache plus per-tier invalidation. Redis or Memcached for shared state across processes; invalidation strategy tuned per tier.

The approach

Three habits make a layered caching strategy work in production: pick the right tier per access pattern, document invalidation per tier, and tie the strategy to the data’s consistency requirements rather than copying the previous service.

• Per-tier cache placement. CDN for edge-cacheable, app cache for hot per-process reads, DB cache for shared state.
• CDN for static plus cacheable APIs. Per-asset cache headers and TTLs. High cache-hit ratio is where the savings come from.
• App cache for hot reads. Per-process in-memory store for the data accessed thousands of times per request.
• DB cache plus documented strategy. Shared cluster cache with per-tier invalidation; per-cache the rationale and TTL documented.

Why this compounds

Each correctly-placed cache layer reduces backend load every minute it runs. The team’s caching mental model deepens; new services inherit the layered pattern instead of relearning it through outages.

• Performance improves. Right cache at right tier compounds across every request.
• Cost efficiency. Backend load drops; CDN and origin both spend less.
• Operational fit. Right invalidation matches consistency. Stale-data incidents shrink.
• Year-one investment, year-two habit. First layered design is heavy lift. By the third service, the pattern is settled.