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Hosting comparison review shows how superficial tests produce false winners: one-off measurements without load, outdated key figures and a lack of safety tests distort results. I explain why these tests are of little technical value and how I set up reliable measurements with TTFBs, load profiles and safety checks.
Key points
I summarize the most important weaknesses and practical countermeasures in a compact way so that you can classify test reports more quickly. Many portals emphasize marketing information but neglect technical details. core values. With a few clear tests, you can recognize real performance instead of advertising promises. Pay attention to measurement quality, measurement frequency and realistic Load profiles. Keep a written record of your results so that you can compare tariffs accurately.
- Methodology: One-off checks are deceptive; continuous measurements count.
- PerformanceTTFB and E2E instead of mere uptime quota.
- SecurityPentest simulation instead of feature lists.
- ScalingLoad tests with user scenarios, not just ping.
- SupportMeasure response time, standardize cases.
This is how I filter out marketing noise and collect hard values. Each measurement follows a previously defined Scenario, each result remains reproducible. I compensate for deviations with second runs and check globally. At the end, I compare like an auditor: same basis, same load, clear Metrics.
Why many hosting tests fail technically
Many portals install WordPress, click on a theme, and then evaluate the Speed using individual screenshots. Such a procedure ignores caching warm-up, network scattering and daily load. One provider works quickly because the test happened to run in a quiet minute. Another slips because backups are running in parallel in the shared cluster. I therefore measure with a time delay, repeatedly and from several Regions, so that outliers do not determine the judgment.
I also make a strict distinction between „cold“ and „warm“ runs: The first retrieval without a cache shows the raw Origin performance, other retrievals measure cache hit rates and their stability. Both perspectives are important - showing only warm values masks server latency, while showing only cold values ignores real user paths with repeat requests. I choose measurement windows over 24 hours and on at least two days of the week so as not to overlook shift operation, backups and batch jobs.
Another mistake: identical themes, but different Configurations. I version my test environment (themes, plugins, PHP version, WP cache settings) and freeze it for all providers. Changes to the stack are synchronized and noted in the log. This is the only way to clearly assign regressions and improvements instead of attributing them to the wrong factor.
Missing load and scaling tests
Without a realistic load, any performance evaluation remains incomplete, as shared environments react sensitively to parallel loads. User. I simulate waves of visitors with increasing requests per second and observe error rates, TTFB jumps and CPU throttling. Many tests evaluate „fast“ after the first call and ignore how the platform collapses with ten times more accesses. I also check whether limits such as PHP workers, I/O or RAM throttle early. If you know such limits, you protect yourself from expensive Failures. A good overview of the pitfalls of portals can be found in the article Comparison portals critical.
I model load profiles as real User scenariosOpen category page, set filter, load product details, add to shopping cart, start checkout. I measure error classes (5xx, 4xx), queue times in the backend, cache bypasses and session locks. As soon as waiting times suddenly increase, I identify the limiting component: too few PHP workers, slow database, file locks in the cache or rate limits for external services. I document the volume (e.g. 20 simultaneous users, 150 RPS) at which stability starts to deteriorate - a hard, comparable Break-even for every offer.
It is also important to ResilienceHow does the system recover after a load peak? I stop the load abruptly and check whether queues flow off, caches remain consistent and whether error rates quickly fall to normal levels. A robust setup shows short recovery times and no data inconsistencies (e.g. orphaned sessions, duplicate orders). These behavior patterns often say more than a peak throughput value.
Outdated metrics distort results
A naked uptime quota says almost nothing about Speed when the first byte contact is lame. I evaluate TTFB separately and aim for values under 300 ms, measured over several locations and time windows. Single shots from Frankfurt are not enough for me, as routing and peering fluctuate. I also check waterfall diagrams to isolate bottlenecks in DNS, TLS handshake or backend. This allows me to recognize whether a great front end is just a weak Backend concealed.
I also make a clear distinction between synthetic measurements (controlled clients, defined bandwidths) and real user data from E2E flows. Synthetic covers regression and trend analyses, E2E shows production proximity and uncovers sporadic latency peaks. Both measurement worlds have their own dashboards and are not mixed. Server timing headers and detailed timings (DNS, TCP, TLS, TTFB, TTI) help to assign the responsibility layer: Network, web server, app, database or third party.
I only use Core Web Vitals as a supplement. They reflect rendering and interaction, but are highly customizable. front-end heavy. For host comparisons, what primarily counts is the origin latency, stability under load and the ability to deliver dynamic content quickly. A score of 100 does not conceal anything if the first byte contact remains sluggish or the checkout collapses under load.
Security checks that hardly anyone checks
Many tests list free SSL certificates without analyzing the configuration. check. I test headers such as HSTS, check OCSP stapling and simulate XSS and SQL injection against demos. Error pages often reveal paths, versions or debug notes, which I consider a risk. I also evaluate backup options: Distance, encryption and recovery time. Only these components add up to a complete Security image instead of whitewashing.
I also look at Account hardening2FA availability, IP restrictions for the control panel, API keys with scope limits, separate production and staging access. On the server side, I pay attention to SSH/SFTP options, file permissions (no 777), isolated PHP pools and logging without plain text passwords. A clean default configuration already prevents many trivial attacks.
WAF, rate limits and Brute force protection are only a plus if they work in a comprehensible way: clear threshold values, customizable rules, meaningful error messages without information leaks. I check whether false alarms are documented and whether support responds to security incidents in a structured manner (ticket classification, forensic data, time to mitigation). I check GDPR aspects via data locations, ADV contract, deletion concepts and retention periods for logs - security is more than just a lock symbol in the browser.
Support assessment: How I measure fair
I never evaluate support based on my emotional state, but with identical Tickets. Each scenario receives the same text, the same logs and a clear expectation. I stop the response time until the first qualified answer and evaluate the technical depth. General phrases without a solution cost points, reliable steps including reference numbers earn points. If you offer live chat, you need to offer a similar service at peak times. fast deliver as at night.
I also evaluate ContinuityAre tickets handed over cleanly or do they „reset“ at shift changes? Are there summaries, checklists, clear queries? I rate positively when support teams proactively explain causes, name workarounds and suggest retests - not just report „ticket closed“. I also record availability via channels (chat, phone, email), SLAs and the availability of escalation paths for critical incidents.
Correct test methodology at a glance
To ensure that results remain reliable, I set up anonymous test accounts, install WordPress without demo ballast and launch automated Measurement series. GTmetrix, continuous TTFB checks and simple E2E flows cover the day-to-day business. Global calls show whether a CDN is sitting correctly or just hiding latency. After updates, I repeat core runs to find regressions. If you want to deepen measurement quality, take a look at the PageSpeed scores as a supplement to the TTFB; they do not replace load tests, but complete the picture.
I use an identical one for all providers BaselineSame PHP version, same WP configuration, identical themes and plugins, same caching settings. I document changes with a timestamp, commit hash and brief justification. Measuring points (locations, bandwidth profiles) remain consistent. I record results in a standardized template: test window, median/95th percentile, error rate, anomalies and notes. I mark outliers visibly and check them with a second run.
I minimize confusion factors by DecouplingKeep DNS providers constant, identical TTLs, no traffic shaping in the browser, identical headers (Accept-Encoding, Cache-Control), no parallel deployments during the runs. This makes it clear whether differences originate from the hoster or from the test environment.
| Criterion | Frequent test error | Correct method |
|---|---|---|
| Performance | One-time ping without context | Weekly GTmetrix runs plus TTFB < 300 ms |
| Security | Feature lists instead of testing | XSS/SQLi simulation and header analysis |
| Support | Subjective mail judgments | Standardized ticket time measurement |
| Scalability | No load profiles | E2E with user simulation and error rate |
Recognize price traps and bait offers
Many tariffs shine with entry-level discounts, but conceal expensive Extensions. I always calculate total costs per year including SSL, backups, domains and any add-ons required. A „free“ backup doesn't help if restore fees are incurred. I also cover contract periods; long commitments often hide later price jumps. If you calculate properly, you can compare effectively and protect your Budget.
The full costs also include Soft limitsEmail sending quotas, I/O throttling, CPU minutes, inodes, API limits. Exceeding these limits leads to throttled performance or additional costs - both must be included in the evaluation. I check whether upgrades are fairly priced and whether downgrades are possible without risking new fees or data loss. Hidden fees (setup, migration, restore per case, additional IPs) are added to a separate cost line and included in the annual assessment.
Technology stack: interpreting NVMe, PHP and CDN correctly
I check whether the provider has genuine NVMe-SSDs, how many PHP workers are running and whether HTTP/2 or HTTP/3 is active. NVMe brings low latencies, but is of little help if I/O is limited or caching is configured incorrectly. A CDN reduces global latency, but must not conceal the server weakness in Origin. I therefore separate static and dynamic tests and measure both paths separately. This allows me to see where optimization is effective and where hard Boundaries lie.
I go into depth with Server tuningOPcache hit rates, JIT effects, Brotli/Gzip, TLS 1.3, ALPN, IPv6, HTTP keep-alive and connection reuse. On the database side, I check the engine (InnoDB), buffer pool sizes, slow query logs and connection limits. Virtualization (KVM, LXC) and container isolation are relevant when it comes to „noisy neighbors“. A strong marketing label is of little use if the isolation is weak and neighbors eat up the resources.
Ranking example without embellishment
I show a sample ranking that provides clear Criteria and hides marketing screens. The rating is based on TTFB, stability under load, security configuration and support response time. Prices take into account additional costs such as SSL and backups. Technology is rated first, convenience second. This creates a picture that reflects real Performance rewarded.
| Place | Provider | Strengths | Weaknesses |
|---|---|---|---|
| 1 | webhoster.de | NVMe, fast support, GDPR | None |
| 2 | 1blu | Good speed values | Slower reactions |
| 3 | webgo | High uptime | Older interface |
How to test yourself - in 60 minutes
Start with a fresh WordPress instance without Pagebuilder and without demo import so that the Base remains clean. Create three identical subpages and measure TTFB from two regions, three times each, so that outliers do not dominate. Perform a simple load run with increasing requests and observe error rates from five parallel users. Check the security header, TLS version and the restoration of a backup. Afterwards, read your measurement logs crosswise and correct obvious errors. Error with a second run; why measurements often go wrong is shown in the article on incorrect speed tests.
If there is time: Test emails (SPF, DKIM, DMARC configured?), DNS lookup times (authoritative name server, TTL strategy) and the upload of larger files. This will help you recognize throttling that is not mentioned in brochures. Document each step briefly - just a few key points per test run increase the Traceability enormous.
Practical evaluation: from figures to decisions
I give more weight to TTFB and stability than comfort functions because reliable Performance protects sales. Uptime below 99.99% lowers the score noticeably, especially if outages become more frequent. Fast support is a lifesaver in an emergency, but should not compensate for weak technology. In the end, I add up the costs in an annual analysis, including add-ons. In this way, I make a choice that saves stress and creates real value. Transparency supplies.
For the evaluation, I work with clear Weightse.g. performance 40%, stability under load 25%, security 20%, support 10%, cost clarity 5%. Each criterion has measurable thresholds (TTFB < 300 ms, 95th percentile < 500 ms, 0% 5xx under moderate load, recovery < 60 s after load peak, complete header protection, restore < 15 min). This results in a score that is not based on gut feeling, but on real signals. If the results are close, I decide on Robustness (percentile, recovery time) instead of peak values.
Transparency, conflicts of interest and ethics
I document whether a provider provides test access, whether affiliate relationships exist and whether support teams know about the test. Transparency prevents skewed perceptions. Tests run on my accounts, not on third-party production sites. Load tests are deliberately limited so that no third-party systems are affected. I publish results with methodology, date and version status - this is the only way they can be replicable.
Recognizing noisy neighbors and insulation quality
Shared hosting stands and falls with Insulation. I check hourly TTFB drifts over several days: regular sawtooth patterns indicate backup/cron windows, irregular peaks indicate neighboring loads. I also measure under my own constant load: If the latency increases without any action on my part, this indicates external influences. Providers with stable isolation deliver tightly clustered percentiles, even at peak times.
Staging, deployments and recoveries
Good hosting support is evident in the Life cycle of a site: Create staging, mask data, deploy back to production, restore backup, test rollback. I assess whether these steps are documented, transactionally secure and possible without long downtimes. RPO/RTO key figures are just as much a part of the assessment as uptime - because data loss is more serious than a short outage.
Specific tips for your next comparison
Before buying, place three hard Goals fixed: TTFB under 300 ms, 99.99% availability and support responses within five minutes in live chat. Order the smallest package as a test only and cancel immediately if the core values are not met. Repeat measurements on two days, during the day and in the evening. Actively ask for pentest reports or at least header checks. If you apply these steps consistently, you won't need glossy lists and won't get caught up in pretty Advertising promise.
Add to your checklist:
- DNSAuthoritative response times, simple records, meaningful TTLs.
- E-mailSPF/DKIM/DMARC available, reputation, limitation of outgoing mails.
- ResourcesPHP worker, I/O, CPU minutes, inodes - ask for written limits.
- SLADefinitions of failures, credit mechanics, measurement methods of the provider.
- MigrationCosts, downtime window, who does what, test restore in advance.
Conclusion: Real performance instead of brochure values
Anyone who seriously compares hosting needs Consistency, not click rates. Repeated, cross-location measurements, clear load scenarios, clean security checks and standardized support tests expose quick fixes. I separate marketing from measured values, keep meticulous records and compensate for outliers with second runs. The result is a comparison that is easy on the budget, avoids failures and gives you the certainty that you have chosen the right platform - based on hard figures, not nice promises.
