Prompt

I'm building a high-traffic site and want to reduce origin load with edge caching

Technology / CDN & Edge Infrastructure1 observationLast seen Oct 12, 2026

Latest observation

Oct 12, 2026OpenAI APIWeb search: off

Great goal. Edge caching can dramatically cut origin traffic, reduce latency, and absorb traffic spikes—especially for high-traffic sites.

What to cache at the edge

Best candidates:

  • Static assets: images, CSS, JS, fonts, video thumbnails
  • Public HTML pages that don’t change every request
  • API responses that are safe to share across users
  • SSR pages with short TTLs or stale-while-revalidate
  • Redirects and error pages that are frequently requested

Avoid caching:

  • Personalized pages
  • Authenticated responses unless carefully varied
  • Highly dynamic data that must be fresh per request

Core caching strategy

A strong pattern is:

  1. Cache static content aggressively

    • Long TTLs
    • Fingerprinted filenames, e.g. app.3f2c1.js
    • Use Cache-Control: public, max-age=31536000, immutable
  2. Cache HTML carefully

    • Short TTLs or stale-while-revalidate
    • Example: Cache-Control: public, max-age=60, stale-while-revalidate=300
  3. Use surrogate/edge caching headers

    • If your CDN supports it, set separate cache controls for browser vs edge
    • Example concepts:
      • Cache-Control for browsers
      • CDN-Cache-Control or surrogate headers for edge
  4. Vary correctly

    • Only vary on what truly changes the response
    • Overusing Vary reduces cache hit rate
    • Common pitfalls: Cookie, Authorization, Accept-Language
  5. Serve stale on origin issues

    • Use stale-while-revalidate or stale-if-error
    • This improves resilience during origin slowdowns

Practical header examples

Static asset

Cache-Control: public, max-age=31536000, immutable

HTML page

Cache-Control: public, max-age=60, stale-while-revalidate=300, stale-if-error=86400

API response

Cache-Control: public, max-age=30, stale-while-revalidate=60

Key implementation considerations

  • Cache key design: path + query string + select headers
  • Purge/invalidation: decide whether to version assets or actively purge
  • Compression: cache separate compressed variants if needed
  • Observability: track hit ratio, origin offload, TTFB, stale serves
  • Protection from cache fragmentation: normalize URLs and strip irrelevant query params

Common mistakes

  • Caching pages with cookies by default
  • Including unnecessary query params in the cache key
  • Short TTLs on static assets
  • No purge strategy
  • Forgetting to test cache behavior across deployments

A good starting policy

If you want a simple, effective baseline:

  • Static assets: cache 1 year, immutable
  • Public HTML: cache 1 minute with stale-while-revalidate
  • APIs: cache only if responses are shared and safe
  • Use versioned asset names and a purge mechanism for HTML/content updates

If you want, I can help you design:

  1. a CDN caching policy,
  2. specific cache headers for your stack (Cloudflare/Fastly/Akamai/Nginx), or
  3. a cache key and invalidation strategy for your app.

Observation history

How did Obsurfable measure this prompt?

Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.

Which AI systems does Obsurfable collect answers from?

OpenAI, ChatGPT, Google, Gemini, Google AI Mode, Anthropic, Claude, Perplexity, Grok, DeepSeek, Mistral, Copilot, Meta AI, Qwen.

How can brands improve their AEO?

Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.

Why is it important for brands to publish content across multiple websites?

Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.

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