Why TTFB is almost meaningless for cached pages

On cached pages, the TTFB cache Above all, it's about the cache hitting – not how quickly users can see or interact with content. I explain why TTFB becomes almost meaningless for consistently cached pages and what I focus on instead for real Performance eighth.

Key points

I will briefly summarize the following key points.

• cache hits make TTFB small, but say little about visible speed.
• CDN removal Affects TTFB, not backend quality.
• Core Web Vitals reflect the user experience, TTFB only the start.
• measurement strategy Separate: cached vs. uncached endpoints.
• cache ratio and LCP/INP count for conversion and satisfaction.

Classifying TTFB correctly: What the value shows

I see TTFB as a technical start time between the request and the first byte, not as a measure of visible speed. This figure includes latency, handshakes, and cache or server processing, i.e., primarily Network and infrastructure. A low value may originate from the cache, the nearby edge, or fast DNS, without the page rendering quickly afterwards. That's precisely why I never measure TTFB in isolation, but rather classify the value in conjunction with FCP, LCP, and INP. This allows me to expose false conclusions and focus on what users really perceive.

Cache layers shift the bottleneck

As soon as a page cache, reverse proxy, or object cache takes effect, the infrastructure delivers finished Answers and TTFB shrinks to milliseconds. The value then primarily reflects the efficiency of the cache hit, not the quality of the backend. I therefore always check whether I am measuring a hit or a miss before drawing any conclusions. This is normal for homepages, landing pages, and articles: they come from the cache and therefore appear very fast, even if there is a lot of logic in the background that rarely runs. The decisive factor remains how quickly the visible content appears and how responsive interactions are.

CDN removal and edge hits distort the assessment

A CDN can drastically reduce TTFB because the nearest Edgenode is close to the user. This allows me to evaluate TTFB at the edge separately from the origin, because both paths tell different stories. A great value at the edge says little about the origin server, which is only queried in case of misses or after invalidation. For well-founded statements, I combine edge measurements with targeted origin checks and look at the cache hit rate. If you want to dive deeper, you'll find a good introduction at CDN hosting and TTFB, where the influence of distance becomes very tangible.

Clearly separate lab values and field data

I make a strict distinction between laboratory measurements and real-world measurements. User data. Tools such as Lighthouse simulate certain device and network profiles, but do not cover every real-life usage situation. Field data (e.g., real user signals) show how pages perform in everyday use and which browser versions cause problems. I use lab checks specifically for diagnosis and field checks for prioritization and performance monitoring. Only the combination of both perspectives provides a clear picture. Image about impact and potential.

TTFB in the context of Core Web Vitals

I consistently classify TTFB as part of Core Web Vitals because these values reflect the designed loading experience. measure. A slightly higher TTFB can be compensated for by good rendering, critical CSS, early-loaded web fonts, and lean JavaScript. The decisive factors are when the largest visible element appears and whether inputs respond quickly. This is precisely where noticeable speed and conversion gains occur. The following overview shows how I use TTFB together with other metrics. valued.

Metrics	What it measures	Relevance on cached pages	Typical adjustment screws
TTFB	Time until the first byte	Low, as cache hits dominate	DNS, TLS, edge proximity, cache hit rate
FCP	First visible Element	High, since start of rendering	Critical CSS, inlining, minimal JS block
LCP	Largest visible Block	Very high, direct perception	Image optimization, preload, server push/103 early hints
INP/TBT	Response time to Inputs	High, noticeable interaction	JS splitting, defer, web worker, compression
CLS	Layoutdisplacements	High, ensures peace and quiet	Placeholders, fixed heights, no late resource jumps

Hosting metrics that I prioritize

I first look at throughput, error rate, and consistency. Latencies under load, because these factors influence sales and satisfaction. A high cache hit rate on the CDN and server side relieves the origin and smooths out peaks. At the same time, I measure LCP and INP during traffic peaks to find bottlenecks in rendering or in the main thread. TTFB then helps me as a diagnostic tool, not as a performance target. This creates a clear Prioritization for effective measures.

This is how I measure TTFB effectively

I specifically check TTFB on uncached endpoints such as login, checkout, and APIs, because that's where the application really works. For clean results, I set test parameters that bypass caches, or I separate measurement windows after a targeted purge. I then compare misses to hits to understand the impact of the cache on the value. A structured TTFB analysis helps me distinguish between network, server, and database. This allows me to find real Brakes instead of just good numbers.

Check cache hits vs. cache misses cleanly

I always document whether the answer comes from the Cache comes, for example via response headers for hit/miss. This is the only way I can interpret TTFB correctly and make decisions. A high TTFB on rarely visited subpages doesn't bother me as long as business-critical paths run smoothly. What matters is how often content needs to be fresh and which TTLs make sense. These decisions pay off in noticeable Speed and operational safety.

Practical setup: Page cache, object cache, reverse proxy

I combine page cache for HTML, object cache for data, and a reverse Proxy for efficient delivery. These layers reduce load peaks and stabilize response times for real users. For WordPress, I rely on persistent object caches so that frequent queries are immediately available. The page cache delivers finished pages, while the proxy controls headers and uses GZip/Brotli. This keeps the origin relaxed, allowing me to focus on Rendering and interaction.

Evaluate cached vs. uncached paths

I separate key figures by page type so that no incorrect conclusions I primarily measure cached pages using FCP, LCP, CLS, and INP, and uncached endpoints using throughput and TTFB. What matters for decisions is what users see and use—the delay in the first byte is rarely decisive here. If you optimize TTFB in isolation, it's easy to lose sight of the overall speed. This overview shows why the first byte number often seems excessive. First byte count overrated very vivid.

CDN and cache rules that matter

I set clear TTLs, use stale-while-revalidate, and invalidate specifically via Tags or paths. This keeps pages fresh without unnecessarily burdening the source. For media, I use long runtimes and version files so that browser caches can take effect. I keep HTML moderate so that editorial teams remain flexible. These rules increase cache hits, reduce latency, and strengthen the perceived Speed.

Personalization without breaking the bank

Many shops and portals need to personalize—and that's exactly where cache strategies often fall apart. I make a strict distinction between anonymous and logged-in sessions and minimize VarySignals. Cookies that are set globally but do not affect rendering must not be cached. bypass. Instead, I solve personalization in a targeted manner:

• Hole punching/ESI: I render the page statically and insert small, personalized fragments (e.g., mini shopping cart) via Edge Side Includes or downstream via API.
• Key design: I make sure not to fragment cache keys unnecessarily with lots of headers/cookies. A few clear variants keep the hit rate high.
• Progressive enhancement: I load uncritical personalization after FCP/LCP so that the visible speed does not suffer.
• AB testing: I isolate variation IDs via server- or edge-side assignment and avoid creating each user state as its own cache key.

This way, the majority benefits from the cache, while only the fragile Parts remain dynamic. TTFB remains small, but more importantly, the visible time until interaction remains stable.

Header strategy: Revalidation instead of computational load

I set Cache-Control so that the origin has to calculate as rarely as possible. Revalidation is cheaper than re-rendering, and errors should not be a problem for users.

• Cache Control: public, s-maxage (for proxies), max-age (for browsers), stale-while-revalidate, stale-if-error.
• ETag/Last-Modified: I ensure that conditional requests (If-None-Match, If-Modified-Since) reliably deliver 304.
• Vary specifically: I only vary headers that actually change the markup (e.g. Accept-Language for language variants). Accept-Encoding is standard, more only if necessary.
• Surrogate control: For CDNs, I set differentiated lifetimes without keeping browser caches too short.

Cache-Control: public, max-age=300, s-maxage=3600, stale-while-revalidate=30, stale-if-error=86400
ETag: "w/1234abcd" Last-Modified: Tue, 09 Jan 2025 10:00:00 GMT Vary: Accept-Encoding, Accept-Language

This combination keeps TTFB moderate for the first byte despite cache misses because revalidations are fast and StaleStrategies to conceal failures.

Measurement Playbook: From Management to Template

When TTFB increases, I break down the path. I start at the edge, go to the origin, and measure each phase. Headers such as Server timing help me see the time shares in the backend (e.g., DB, cache, template).

• Network: Check DNS, TCP, TLS, RTT. A close edge reduces TTFB—this is to be expected, but it is not a sign of fast rendering.
• Origin: Provoke Miss and observe the differences between start transfer and total duration.
• Server timing: Custom markers such as server;dur=…, db;major=…, app;dur=… set and read.

# Quick profile with cURL (shows phases in seconds) curl -w "dns:%{time_namelookup} connect:%{time_connect} tls:%{time_appconnect} ttfb:%{time_starttransfer} total:%{time_total}n" 
 -s -o /dev/null https://example.org/ # Test origin (bypass DNS, direct IP + host header)
curl --resolve example.org:443:203.0.113.10 https://example.org/ -I # Bypass cache (force miss) curl -H "Cache-Control: no-cache" -H "Pragma: no-cache" https://example.org/ -I

From these building blocks, I can clearly see whether TTFB is network-, cache-, or application-dependent rises – and act purposefully.

HTTP/2, HTTP/3, and Priorities

I always plan performance to be transport protocol-agnostic. HTTP/2/3 help, but they are no substitute for clean rendering:

• Multiplexing: Many assets load in parallel without additional connections. This usually improves FCP/LCP, but has little effect on TTFB.
• 0-RTT/QUIC: Recurring users benefit from the handshake. This is noticeable with many short requests, but not with a large HTML response.
• Priorities: I prioritize critically: HTML first, then critical CSS/fonts, then images with priority hints and lazy loading. This keeps the render path lean.

The result: even if TTFB fluctuates, the vitals remain stable because the browser gets the right resources first.

Cache warmup and rollouts

After deployments, I plan the cache curves. A cold start can increase TTFB at the source—I proactively mitigate this.

• Pre-warm: Target the most important URLs (sitemap, top sellers, home pages) until the hit rate is satisfactory.
• Staggered invalidation: First categories, then detail pages; HTML before media, so that the visible part is quickly cached again.
• Canary rollouts: Redirect partial traffic to the new version and observe cache behavior before invalidating globally.
• Early Hints (103): Signal critical resources before the HTML so that the browser starts working earlier – regardless of the TTFB of the main response.

This keeps the user experience smooth and the operating metrics (error rates, load peaks) flat.

WordPress and e-commerce: navigating tricky paths with ease

In WordPress and shop setups, I make even finer distinctions. Cards, shopping carts, logins, and AdminAreas remain uncached and are optimized specifically:

• WooCommerce/Checkout: No flat rates no cache-Headers across the entire site. I isolate the dynamic endpoints and aggressively cache the remaining pages.
• Object cache: Persistent object caches keep expensive queries warm. They reduce TTFB for misses and smooth out load peaks.
• REST/Admin Ajax: Rate limits, lean payloads, and short runtimes prevent interaction paths from blocking the main thread.
• Assets: Long TTLs with versioning (query or path busting) so that browser caches take effect and LCP/RUM values become stable.

My goal: Critical, dynamic paths are fast enough, while 90% of traffic comes from the cache and the vitals shine.

SLOs, budgets, and alerts

I define clear service goals so that optimization does not become a matter of taste. For cached HTML pages, I control via Vitals (p75), and for uncached endpoints via backend SLOs:

• LCP p75: Set target values for each page type and monitor them continuously.
• INP p75: Link interaction budget with maximum main thread block time.
• Cache hit rate: Thresholds below which alerts are triggered (Edge and Origin separately).
• TTFB (uncached): Define SLOs for login/checkout/API because these paths show real processing.
• Error rate/throughput: Pay attention to peak loads and test stale strategies so that users don't notice anything.

This way, I always know whether an outlier in the TTFB is just a cache effect or whether it is genuine. risk paths are affected.

Web host selection with a focus on cache and load

I evaluate hosting based on caching capabilities, CDN integration, monitoring, and Support-Quality. An environment with fast storage, modern proxies, and a clean PHP stack delivers more reliable results in everyday use than a minimally lower TTFB. In comparisons, webhoster.de often performs well because the platform consistently focuses on performance and WordPress optimization. Especially under load, it is this architecture that counts, not a one-time lab measurement. This is how I ensure that pages run smoothly in operation and Scale.

Briefly summarized

I use TTFB as a diagnostic tool, but I give visible metrics the priority. On cached pages, TTFB primarily provides information about cache hits and the network, not about the user experience. For decision-making purposes, I consider LCP, INP, cache ratio, throughput, and error rates. I strictly separate measurements into cached and uncached so that I can obtain genuine Bottlenecks . Those who pursue this approach deliver fast experiences and create reliable performance – regardless of a nice TTFB figure.