A caching and CDN strategy
that survives a traffic spike, not just a demo
A fast site in a Lighthouse test and a fast site under real traffic are not the same thing. We build a caching and CDN strategy across the edge, the application and the database, tuned to survive the traffic spike that actually matters, not just to score well on a tool nobody's customers run.
What it is
Caching and CDN strategy is the set of decisions about what gets stored closer to the user, at a CDN edge location, in an application-level cache like Redis, or not cached at all, and for how long, before it needs to be refreshed. Done well, it means most requests never touch your origin server or your database at all. Done badly, either everything hits the origin anyway, making the CDN mostly decorative, or content gets cached too aggressively and users see stale prices or outdated pages for hours after a change.
When you need it (and when you do not)
You need this once your site slows down under real traffic, a sale, a viral post, an ad campaign that actually works, even though it feels fine in normal day-to-day browsing, because the gap between light and heavy load is exactly where an undersized caching strategy shows itself. It is also worth a dedicated look once a site has expensive database queries running on every page load, a product listing with complex filters, a dashboard with aggregated numbers, that would be cheap to serve from cache but currently are not.
You do not need a deep caching overhaul for a low-traffic site that already loads acceptably, optimizing further there is a cost with no real payoff. The investment makes sense once traffic is high enough, or growth plans assume it will be, that a spike has real consequences if the site cannot handle it.
How we build it
We tier caching by content type rather than applying one rule everywhere: static assets (images, scripts, fonts) get long cache lifetimes with versioned filenames so an update is instant and safe; semi-static pages (a product page, a blog post) get shorter lifetimes with explicit invalidation on update; anything genuinely dynamic and user-specific does not get cached at the edge at all, that is what application-level caching is for. Redis sits in front of expensive queries and computed results, a product listing with filters, a dashboard aggregation, with a sensible expiry and an invalidation hook tied to the data actually changing. We load test against a realistic spike scenario, not just a synthetic benchmark, before calling a setup production-ready, because the failure mode that matters is the one that happens during your actual busiest moment, not during a quiet Tuesday afternoon test. We applied this tiered approach to an archaeological atlas serving close to two million records and a D2C store rebuild where checkout speed under real traffic was the entire point of the project.
What to watch
The single most damaging caching mistake is invisible: a cache invalidation bug that serves stale content silently, a price that changed an hour ago but still shows the old number, with nothing in the user interface hinting that anything is wrong. We build invalidation as an explicit, testable step tied to the actual data change, not a time-based guess that happens to usually work. The other risk is treating caching as a substitute for fixing a genuinely slow query or an inefficient page, which works until the cache expires at a bad moment and the underlying slowness is suddenly everyone’s problem at once. Vendor lock-in is minimal with Cloudflare or a similar CDN, the configuration is portable in concept even if the specific rules need rebuilding on a different provider; Redis is open source and the caching logic lives in your application code.
We also test what happens when the cache is deliberately cleared under load, not just when it is warm, because a cold-cache traffic spike, right after a deploy, for instance, behaves very differently from steady-state traffic and is a common moment for an otherwise well-tuned setup to struggle.
Price and timeline
| Scope | Price | Timeline |
|---|---|---|
| CDN configuration, basic app cache | from $900 | 1 week |
| Full strategy with load testing | from $2,200 | 2 weeks |
Related
Built as part of custom development. Often paired with search engine infrastructure and monitoring and observability to watch cache performance in production. See the results in the archaeological atlas with 1.9 million objects and the Thailand D2C store audit and rebuild. Tell us what slows down under real traffic: get in touch.
FAQ
How much does a caching and CDN setup cost?
A setup covering CDN configuration and basic application caching starts at $900. A fuller strategy including database query optimization and load testing against a real traffic scenario runs $1,800 to $3,500.
How long does it take?
1 to 2 weeks for most sites: configuring the CDN correctly per content type, adding an application cache layer where queries are expensive, and load testing before calling it production-ready.
We already use Cloudflare, why would we need this?
Having a CDN turned on and having it configured correctly are different things. The most common issue we find is caching rules that are too conservative, serving everything from origin, or too aggressive, serving stale content after an update. Both are fixable with the right rules.
Does caching fix a genuinely slow database?
Caching hides a slow query, it does not fix it, and a cache that expires at the wrong moment exposes the real problem anyway. We look at whether the underlying query or schema needs work before relying on caching to paper over it.
What happens when content changes, does the cache go stale?
We build an invalidation strategy specifically to avoid that: updated content triggers a cache purge for the affected paths, so a price change or a new article shows up correctly instead of serving an old version for hours.