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In this article, I explain how TTFB influences the perceived performance - and why the measurement of static and dynamic pages reveals different things. I show when TTFB, Server Response Time is a strong indicator, where the pitfalls lie and which measures really count in practice.
Key points
- TTFBmeasures the moment up to the first byte and is made up of DNS, TCP, TLS and server work.
- Static: Very informative, infrastructure and distance dominate.
- DynamicDatabase, PHP and cache characterize the key figure.
- CDN: brings significant effects with full-page cache.
- Measurement: The choice of location determines the interpretation.
TTFB explains: What the first byte really reveals
I see TTFB as the time span from the request to the first response byte, divided into DNS lookup, TCP handshake, optional TLS and the actual server processing. These components add up, which is why even a single slow link pulls the entire key figure upwards. Less than 200 ms is considered very good, 300-500 ms is considered mediocre and above 600 ms there is pressure because core web vitals suffer. However, a fast first byte does not guarantee fast rendering, because large images, blocking JavaScript or layout shifts cost visible time. I therefore always evaluate TTFB in the context of other metrics in order to clearly separate cause and effect and avoid misinterpretations.
Static vs. dynamic websites: How meaningful is TTFB?
At static pages, the server retrieves pre-rendered HTML files and sends them directly - here TTFB primarily reflects the network path, DNS performance and I/O of the platform. The key figure correlates strongly with the total load time because there is little application logic in between. More happens with dynamic pages: PHP renders templates, the database delivers content, object cache and OPcache intervene. This is where TTFB often marks the real bottlenecks: lame queries, too many plugins, missing full-page cache or weak CPU. I therefore classify the value according to the page type before drawing conclusions or allocating budgets.
Classify measurement correctly: Location, DNS, TLS
The geographical Distance clearly characterizes TTFB because each additional hop introduces latency. If you only measure in one place, you only see a section of reality. I check values from several regions, for example with tools that offer global probes, and compare them with the target audience. I also pay attention to DNS times, as slow resolvers delay the start, and to TLS, as handshakes and certificate checks vary. Only with this classification do I recognize whether the server is slowing down or the network is eating up the time.
WordPress: Reducing server response time in practice
I start with Hosting, because CPU, RAM and NVMe I/O directly fuel the PHP stack. Modern PHP versions (from 8.0), OPcache and a persistent object cache (Redis/Memcached) significantly reduce the rendering time. Full page caching can dramatically reduce TTFB, as HTML then comes directly from the cache and the database and PHP are suspended. LiteSpeed Enterprise further reduces the response time in many setups, especially in conjunction with its cache plugin. For the root cause analysis I use a TTFB analysis, to make queries, hooks and slow end points visible.
Caching and CDN: When TTFB counts and when it counts less
A CDN accelerates images, CSS and JS reliably, but the pure TTFB refers to the HTML document. Without a full-page cache, the key figure is therefore influenced by the origin server. With edge HTML cache (e.g. APO), the document is delivered worldwide and TTFB decreases because the path is shorter and no backend is working. Conversely, TTFB loses weight with perfectly cached pages, as users are served immediately from the edge cache anyway. This is exactly why I have looked at the relation of TTFB at Cache and reclassified the measured values.
Technique checklist: Quick wins against high TTFB
I reduce Latency first by choosing a data center close to the target group or using edge locations via full-page cache. Then I eliminate backend brakes: identify slow queries, set indexes, streamline autoload options, clock cron jobs. Activating HTTP/3 brings noticeable start-up advantages because connection establishment and loss handling run more efficiently. I optimize the TLS handshake duration using the latest cipher suites and session resumption, which is particularly helpful for many first-time visits. I also filter aggressive bot traffic and block unnecessary endpoints such as XML-RPC so that real users benefit from the freed-up capacity.
Comparison table: TTFB factors and effects
The following Table summarizes which adjusting screws have what effect on static and dynamic pages and what I pay attention to.
| factor | Static pages: Effect | Dynamic pages: Effect | Notes |
|---|---|---|---|
| Geographical distance | High - network dominates | Medium - Network + Backend | Select edge locations via full-page cache |
| DNS provider | Medium - Start delay | Means - added to the total path | Fast resolvers, low TTLs for A/AAAA/CNAME |
| TLS handshake | Medium - First contact | Medium - especially for cold starts | HTTP/3, session resumption, current cipher |
| CPU/RAM/Storage | Low - File serving | High - PHP, DB, Cache | NVMe, sufficient RAM, high single-core performance |
| Full-page cache | High - direct delivery | Very high - backend not applicable | Cache HTML at the Edge, high cache hit rate |
| Database optimization | Low | Very high | Indexes, query review, object cache |
| PHP version/OPcache | Low | High | PHP ≥ 8.0, configure OPcache sensibly |
Measurement tools and interpretation: How to read values
I combine Individual tests with multi-location checks to separate network paths and server times. A test from just one city can show top values, while remote regions weaken; the combination makes the picture complete. For recurring audits, I document the time, location, cache status and protocol version so that I can interpret changes correctly later. I also check waterfall charts to see if DNS/TLS or the app is taking up the first milliseconds. For global reach, I plan CDN hosting so that the first response starts at the Edge and not at the origin.
HTTP/3, TLS and DNS: Network makes the difference
Activate HTTP/3, TTFB often decreases noticeably because connections are established more quickly and losses are better compensated. Choosing a high-performance DNS provider removes additional waiting time at the start and makes measurements more reproducible. For TLS, I rely on current ciphers, 1.2 or 1.3, and session resumption to speed up handshakes. Together, these network advantages add up to give the server more scope for rendering. I look at these steps as a baseline before going deeper into database or PHP tuning.
Cold vs. warm cache: hit rate, TTL and invalidation
I make a strict distinction between Cold and Warm cache. A cold cache shows the true server time without help, while a warm cache represents real repeat visits. For reliable statements, I log the Cache hit rate, TTLs and purge events. Low hit rates indicate TTLs that are too short, aggressive purges or variant-rich responses (cookies, query strings). I normalize HTML, remove unnecessary Vary headers, set consistent cache keys and plan soft purges so that the edge cache does not run empty. This keeps TTFB stable - not just in individual sessions, but throughout the day.
Forwarding, HSTS and Early Hints: Save milliseconds at the start
Any Forwarding adds an RTT and drives up TTFB. That's why I set up the target URL so that users land directly on the host, protocol and path (no http→https→www→non-www cascades). HSTS eliminates the http→https detour on subsequent visits. Where possible, I send Early Hints (103) and use server-side Early Flush, so that browsers request critical resources earlier and rendering starts while the backend continues to render. The first byte remains a number - but the perceived speed improves significantly if the browser can work early.
RUM vs. synthetic: Which TTFB really counts?
Laboratory values from synthetic tests are reproducible, but not representative for mobile networks, weak devices or remote regions. In RUM-data (Real User Monitoring), I look at distributions and percentiles: P50 shows the middle, P75 and P95 make problems with peak times visible. I segment by country, network type (4G/5G/WLAN), device and cache status. Only the combination of synthetics (finding causes) and RUM (impact on the audience) provides a robust basis for decision-making.
Server architecture and concurrency: avoid queues
High TTFB is often caused by Queuestoo few PHP FPM workers, an exhausted database connection pool or a blocking I/O. I adjust the process manager (static/dynamic), max-children and request queues to the real load and ensure that there are sufficient Single-core performance, because many PHP workloads are single-threaded. Keep-Alive and Connection-Reuse reduce handshakes, while a reverse proxy (e.g. before Apache) conceals idle times. Important: Compression blocks the first byte if it occurs before the flush - I stream HTML and compress in blocks so that the browser can get started early.
Headless, SSR and SPA: influence on TTFB and perception
At SPAs TTFB for HTML is usually low, but the time to interactivity suffers. With SSR and streaming HTML, I lower FCP and LCP even if the TTFB increases slightly because the server is doing more work. In headless setups, I separate API and HTML TTFB: slow CMS endpoints increase the overall experience even if the shell document is fast. I rely on island architectures and delayed hydration to avoid long main thread blocks - measurable in RUM, noticeable for users.
Protection and load peaks: WAF, bot traffic and rate limiting
Missed TTFB tips are common Bot-driven. A WAF, rate limits and clean robots rules protect backend resources. I prioritize HTML and block costly secondary paths (XML-RPC, wp-admin-AJAX) for anonymous users. I smooth out queue overflows at peak times with burst buffers and predictive cache warming before campaigns or TV commercials. The aim is to Origin capacity and feed the edge cache with hits.
Deepen diagnostics: server timing, logs and waterfalls
I annotate responses with Server timing-headers (e.g. dns, tls, app, db, cache) so that waterfalls show more than estimated values. In logs, I correlate slow requests with query logs, cache misses and CPU spikes. This is how I recognize patterns: cold OPcache starts after deploys, expire storms after purges, individual N+1 queries under certain routes. I set budgets for recurring SLOs (e.g. TTFB P75 ≤ 300 ms for DE) and link them to alarms - performance thus becomes a continuous process, not a one-off project.
Limits of TTFB: perception vs. measured value
A low TTFB only feels fast when render path and media build smaller hurdles afterwards. LCP increases immediately when hero images are large or fonts load late. CLS spoils the impression as soon as layout jumps occur, even if the first byte comes quickly. Interactivity also counts: blocking scripts lengthen the path to the first click. I therefore weight TTFB together with LCP, CLS and interaction metrics so that technology and perception go together.
Cost-benefit: What pays off first
I start with Cache and PHP update, because the effort remains low and the effect is high. I then check hosting resources: more single-core power and NVMe often significantly reduce backend time; an upgrade often costs €5-15 per month and pays for itself faster than tuning individual plugins. I then optimize the database and queries before activating the CDN HTML cache for global reach. This roadmap minimizes risk and creates measurable progress after each stage. In this way, performance grows steadily without burning budget.
Brief summary: Priorities for static and dynamic pages
At static pages, it's all about the path: fast DNS, a short network path, edge delivery and sensible cache TTLs. Dynamic projects also need strong servers, a modern PHP stack, database hygiene and a full-page cache so that HTML is available quickly. I always evaluate TTFB in the context of the page type and measure from different regions to draw fair conclusions. Only then do I define measures to reduce latency, shorten computing time and reduce the load on rendering. This results in a performance strategy that harmonizes measured values and user experience - for a noticeably fast start and a responsive experience.
