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Hosting comparison portals provide ratings and rankings, but their technical significance often suffers from short test periods, inconsistent setups and a lack of measurement details. I show which key figures really count, how TTFB, P95 and I/O are measured cleanly and why real load profiles separate the wheat from the chaff.
Key points
I will summarize the most important points of criticism and recommendations so that you can classify ratings correctly and plan your own tests. Many portals test too briefly, mix setups or confuse frontend scores with server performance. It only becomes meaningful when measurement series are large enough, conditions remain constant and error rates become visible. Then you will recognize real bottlenecks in CPU, RAM, I/O, database and network. This allows you to make a decision based on Data instead of gut feeling.
- MethodologyTest duration, setup clarity, repeatability
- BenchmarksP95/P99, error rates, I/O profiles
- Load imagesSmoke, Load, Stress, Soak clean separation
- Measuring locationsCompare regions, specify cache status
- TransparencyDisclose raw data, metric weights, test plans
How portals measure - and where the statement falls down
Many portals evaluate performance, availability, support and value for money, but the technical depth often remains thin. I often see series of measurements over a few weeks that ignore seasonal fluctuations, backups or cron jobs and therefore Tips disguise. Without a clear baseline setup - such as the same PHP version, identical CMS including plugins, the same themes, the same cache behavior - results can hardly be compared. Rankings then appear objective, although setup differences are the deciding factor. Such contrasts explain why one provider comes out on top with 99.97 % uptime despite higher costs, while another with a good frontend load time collapses in the load test. weighting differ.
Test duration, setup and noisy neighbors
Short test periods eliminate maintenance windows, seasonal effects and fluctuating neighboring systems in shared environments. I plan series of measurements over at least six weeks, document maintenance events, set identical Software-stacks and keep plugin versions constant. Without this discipline, noisy neighbor effects, backup windows and virus scanners play into the data. It is also important to count error pages and not just average loading times; HTTP 5xx rates often show bottlenecks before total failure. If you ignore these points, you are measuring coincidence and calling it Performance.
Front end is not back end: TTFB, I/O and database
Frontend scores via Lighthouse, GTmetrix or PageSpeed provide impulses, but do not replace server profiling. I separate TTFB into server time and network latency and also measure I/O, query duration and lock wait times so that CPU, RAM and storage bottlenecks become visible. A clean TTFB analysis without a cache cloak shows whether the machine responds efficiently. I also check NVMe vs. SATA, random vs. sequential accesses and database latencies under constant queries. Only the combination of these perspectives separates cosmetic front-end optimization from real Server power.
Read load profiles correctly: Smoke, Load, Stress, Soak
I differentiate between four load patterns: Smoke tests check basic functions, load tests simulate typical traffic, stress tests show the limit and soak tests expose memory leaks over hours. Each stage requires enough requests, parallel users and P95/P99 evaluation so that outliers do not disappear. Pure average values appear friendly, but ignore tough tails and incorrect responses. Without defined error thresholds - for example P95 over 800 ms or 1 % 5xx - the interpretation is misleading. This is how I can recognize whether a host is slowly fraying under continuous load or abruptly with Errors tilts.
Regions, caches and cold runs
Measurement locations shape results: European measuring points conceal delays for users in America or Asia. I therefore measure from several regions and mark cold and warm cache runs separately, because warm cache glosses over time-to-first byte and transfer times. A single location and only warm cache produce nice charts, but tell little about real User paths. CDN transparency also counts: If CDN is active, the note belongs in the legend. Those who are too strongly PageSpeed scores oriented, confuses front-end tricks with real Server performance.
Which key figures really matter?
I weight metrics according to their influence on experience and operation: P95 load time, error rate, uptime including MTTR, I/O performance and query latency are at the top. I only evaluate TTFB in the context of latency and cache status, otherwise the figure leads to false conclusions. Uptime needs longer measurement periods so that failures and their resolution time become visible. For storage, I check random reads/writes and queue depth because web workloads rarely run sequentially. The following table shows typical weaknesses of portals and a better Practice.
| Criterion | Frequent shortage in portals | Better practice |
|---|---|---|
| TTFB | Single measurement, no latency split | P95 from several regions, server time separated |
| Uptime | Short period, no MTTR | 6+ weeks, downtime and repair time documented |
| Load test | No parallelism, only mean values | Smoke/Load/Stress/Soak, P95/P99 and 5xx quota |
| Storage | No I/O type, only sequential | SSD/NVMe, random and sequentially separated |
| Cache | Without cold/warm cache separation | Separate barrels, condition in the legend |
Such guard rails transform pretty graphics into robust evidence. I therefore log the setup, measurement locations, runs, confidence intervals and outlier treatment in a Test plan. This allows results to be reproduced and compared fairly. If this transparency is lacking, a ranking remains a snapshot without context. If you base your purchasing decisions on this, you run the risk of making the wrong choice and later Migration costs.
WordPress real tests: Journey instead of start page
Pure start page checks underestimate expensive processes such as search, shopping cart or checkout. I measure real user journeys: entry, product list, product detail, add-to-cart, checkout and confirmation. I count queries, transferred bytes, CPU peaks, PHP worker utilization and blocking times in the database. NVMe SSDs, 2+ vCPUs, PHP 8.x, OPcache, HTTP/2 or HTTP/3 and a clean cache strategy bring measurable benefits. If you check these factors, you will recognize early on whether the host is suitable for your own Load curve fits or throws errors during traffic peaks and sales costs.
Own measurement design: How to test before signing a contract
I start with a small staging setup and let it monitor for a week before I migrate. At the same time, I load it with realistic user scenarios and stop P95/P99, 5xx rate, error logs, CPU steal and I/O wait times. I also check backup windows, cronjob times, limits for processes and open connections so that hidden throttling becomes visible. I compare result diagrams against weekdays, peak times and maintenance events. If you are interested in charts incorrect speed tests pays later with Failures and additional work that a week of preliminary testing would have saved.
Weighting data fairly and understanding scores
Many portals combine metrics via weighted scores, such as 40 % performance, 20 % stability, 15 % technology and the rest for support and price. I first check whether the weighting fits the project: A store needs different priorities than a portfolio. Then I assess whether the measured values support the weightings - short uptime windows should not result in a high score for Availability bring. Without disclosure of the raw data, every figure remains speculative. A score only becomes meaningful when the measurement duration, setups, percentiles and error rates become visible and I can use the weighting for my own purposes. Usecase can adapt.
Classify frontend scores correctly
Good PageSpeed values without a clean server base look like make-up: pretty, but quickly disappear under load. That's why I first check the server key figures and only then apply frontend tuning. A fast TTFB up close does not conceal sluggish database queries or blocked I/O queues. CDN should also not be an excuse to use weak backends to hide. Those who celebrate front-end scores in isolation are ignoring causes and merely combating them Symptoms.
Transparency requirements for comparison portals
I expect portals to have clear test plans, open raw data, identical setups, marked measurement locations and a clean separation of cold and warm runs. This includes logs for failures, MTTR, limits, backup times and cron jobs. It would also be fair to display error rates and P95/P99 instead of just average values. Anyone using affiliate models should make evaluation logic and potential conflicts of interest visible. Only then will hosting comparison portals gain real Credibility and serve users as a sustainable basis for their Basis for decision-making.
Clearly distinguish between SLI, SLO and SLA
I separate three levels: Service Level Indicators (SLI) are measured values such as P95 latency, error rate or TTFB server time. Service Level Objectives (SLO) define target values, e.g. P95 < 800 ms and error rate < 0.5 %. Service Level Agreements (SLA) are contractual commitments with compensation. Many portals mix it up: they quote a 99.9 % SLA, but don't measure SLI at all, which counts for experience and operation. I first define SLI, derive SLO from it and then check whether the provider SLA is realistic. The important thing is Error BudgetWith 99.9 % uptime, just under 43 minutes of downtime are „allowed“ per month. If you use up this budget at peak times, you jeopardize sales despite SLA conformity. That's why I weight SLI according to the time of day and evaluate outages in the context of peak phases.
Statistics without traps: Sample, confidence, outliers
I make sure I have enough measuring points per scenario: for stable P95 values, I plan at least thousands of requests over several time windows. Confidence intervals belong in every chart, otherwise minimally different bars feign relevance. I treat outliers transparently: I winsorize in exceptional cases, but I remove none Error answers. Instead, I separate „Fast, but incorrect“ from „Slow, but correct“. Temporal aggregation is also critical: 1-minute buckets show spikes, 1-hour averages hide them. I check both. For comparability, I synchronize clocks (time servers), note time zones and coordinate aggregation across hosts so that backups do not „wander“ statistically.
Making limits and throttling visible
Many hosters cap resources in shared and managed environments: PHP FPM workers, CPU cores, RAM, inodes, open files, process and connection limits, SQL connections, network shaping. I deliberately provoke these limits until error messages or timeouts occur. Important indicators are CPU steal (shows hypervisor pressure), run queue lengths, FPM queues and database semaphores. Burst models (briefly high CPU, then throttle) also falsify short tests: a provider appears fast with a 5-minute load, but collapses after 20 minutes. Therefore Soak tests and the log of limit hits are decisive.
Network and TLS under control
I break TTFB down into network and server components: DNS lookup, TCP/TLS handshakes, H2/H3 multiplexing and packet loss all add up to the overall experience. A provider with good server time can still appear slow due to high RTT or loss rates. I measure RTT and jitter from several regions, note the TLS version and compression level (e.g. Brotli/gzip) per resource and observe whether retransmits increase under load. HTTP/2 brings advantages with many objects, HTTP/3 helps with high RTT and losses. Consistency is crucial: I keep protocol, cipher and certificate lengths constant in the tests in order to separate network variables from server time.
Specifying caching strategies
I separate the full-page cache (FPC), object cache and CDN edge cache. I measure the hit rate, invalidations and warmup duration for each layer. A host that serves FPC well can still be slowed down by a lack of object cache (e.g. transient queries). I document which paths are deliberately not are cached (shopping cart, checkout, personalized pages) and how these affect P95. Test scripts mark cache conditions (cold/warm) and Vary headers. This allows me to see whether a provider only shines in the warm cache or also remains performant with cold paths. It is important to warm up the OPcache and JIT properly so that initial requests do not perform artificially worse.
Making security, isolation and recovery measurable
Performance without security is worthless. I check patch cadence (operating system, PHP, database), isolation mechanisms (cgroups, containers, jails), backup strategy and recovery times. Two key figures are operationally central: RPO (Recovery Point Objective) and RTO (Recovery Time Objective). I test restore times in practice: how long does a complete restore of a realistic amount of data take, what is the success rate and what downtime is incurred? I also measure whether security scanners or malware sweeps run predictably and how much load they place on I/O and CPU. Such jobs belong in the test calendar, otherwise they do not explain nightly spikes and lead to false conclusions.
Costs, contract details and scaling
I calculate total cost of ownership: hosting, backups, staging environments, additional IPs, SSL variants, egress traffic and support levels. Fair valuations consider upgrade paths: Can you scale vertically (more vCPU/RAM) or horizontally (more instances), and how quickly? I check whether limits are under the radar (fair use rules, throttling after X GB, cron limits). In load tests, I simulate bursts and observe the response time of auto-scaling (where available): How many minutes until additional workers are active? Costs that only become apparent under load are part of the picture - otherwise a cheap tariff looks attractive until the bill explodes with traffic.
Toolbox and automation
I rely on reproducible measurements: Load generators for HTTP(S), tools for I/O profiles (random vs. sequential), system metrics (CPU, RAM, steal, run queue), network analysis (RTT, jitter, retransmits) and database profilers (slow queries, locks). It is important to automate the setup so that every test round starts identically - including identical PHP and DB configuration, identical plugins, identical seed data and deterministic cache states. Infrastructure as code, seed scripts and reusable journeys minimize variance and make results reliable. I archive raw data, parsers and diagram templates so that later comparisons do not fail due to format changes.
Interpretation according to use case: store, publishing, SaaS
I adapt the weighting to the purpose: A content portal needs globally good latency and caching hit rate, a store prioritizes low P95 under personalization and transaction load, a SaaS application needs stable database locks and low 5xx rate for long sessions. The test plan varies accordingly: For stores I focus on shopping cart/checkout, for publishing I use more region tests and CDN transparency, for SaaS I extend soak tests and session longevity. A one-size-fits-all score doesn't do justice to any of these profiles, which is why I document the priorities per project before the first measurement point.
Recognize error patterns quickly
Typical patterns can be systematically assigned: If P95 increases at a constant error rate, queue formations indicate CPU or I/O bottlenecks. If the 5xx rate jumps at the same time, limits have been reached (FPM, connections, memory). Wavy peaks on the hour are cron indicators, nightly sawtooths indicate backups. If TTFB server time remains stable but latency increases, the network is the suspect (RTT, loss). I correlate metrics in time series and tag events - so no interpretations arise without context. With this discipline, I separate chance from cause and avoid expensive wrong decisions.
Briefly summarized
Comparison portals provide an introduction, but real conclusions can only be drawn with long series of measurements, consistent setups and clear percentiles. I test TTFB separately, measure I/O and database, analyze P95/P99 and error rates and test several regions including cache status. For WordPress, I rebuild journeys, pay attention to NVMe, vCPUs, PHP 8.x, OPcache, HTTP/2 or HTTP/3 and limits. I evaluate frontend scores carefully and avoid jumping to conclusions without context. If you follow these guidelines and, if necessary, have a short Pagespeed classification combined with technical measurement data, makes decisions on the basis of reliable Measured values instead of pretty Rankings.
