Traffic is only a problem when the architecture was sized for average load. We engineer and remediate platforms for sustained concurrency and unpredictable peaks — the conditions under which most failures actually occur.
The failure mode is remarkably consistent. A platform is built and tuned against its current load, grows, and then fails at a peak. Every tuning decision was correct at the time. The problem is that average-load tuning produces a system that is comfortable most of the time and unavailable at the moments that matter most — which for a community, a marketplace or a publisher is precisely the wrong trade.
You have had incidents, or you can see one coming, and you need the architecture assessed on evidence rather than intuition.
Downtime is directly commercially damaging and you cannot afford the reputational cost of a failure during your busiest period.
Peak-time degradation is damaging the user experience at exactly the moment your most engaged users are present.
Engineering faults are rarely confined to the engineering layer. These are the commercial consequences we see most often.
On a marketplace or transactional platform an outage interrupts activity already in progress. On a community it breaks a daily habit that is expensive to rebuild. The direct traffic loss is the smallest part of the cost.
Recurring server errors cause search infrastructure to reduce how much it crawls, which degrades search visibility as a second-order effect of a reliability problem.
Provisioning for the worst case without addressing what causes the worst case produces infrastructure that is idle most of the time and still fails at the top of the curve.
Each of these is work we carry out, not an area we advise on.
Establishing which resource saturates first and at what concurrency, from evidence across the whole serving path. The visible symptom is frequently two tiers below the actual constraint.
The connection layer is the first thing to break under concurrency and the most commonly under-configured. Tuning for concurrent connections rather than average throughput is a different exercise from tuning for load.
Establishing what can be cached, for how long, and at which layer. Caching is the highest-leverage answer to high traffic, and it is a correctness question before it is a performance one — the failure mode of a wrong cache policy is serving something incorrect.
Moving what can be answered at the edge away from the application tier. This is frequently the single change with the largest effect on both capacity and latency.
Distributing demand across origins so that peak traffic is a throughput problem rather than a hard ceiling.
Contention, query cost and connection pooling under concurrency — the tier where scaling problems are most often hiding and hardest to see from outside.
Establishing the cause of failures that only occur under production conditions. These are the hardest bugs to reproduce and the ones with the largest consequences.
Instrumentation that answers 'what will break first' before it breaks, and capacity modelling grounded in observed load rather than guesswork.
The sequence is deliberate. The order is usually what determines whether the work holds or has to be repeated.
A fix applied without reproducing the failure is a guess. We establish the load profile that causes the problem, whether that is in production instrumentation or a controlled environment.
From connection layer to database, in order, because the constraint is rarely where the symptom appears. Solving the top of the path when the constraint is at the bottom produces no change and a lot of confusion.
On a platform that is currently failing, restoring stability takes precedence even at the cost of temporary inefficiency. Optimisation on an unstable system is not measurable.
Moving work off the critical path, caching what can be cached and eliminating what need not be done is nearly always cheaper and more durable than adding capacity to do it faster.
On a live platform there is no maintenance window. Every change is introduced into production, which constrains technique and pace and requires a disciplined approach to rollout and rollback.
Documentation is a deliverable, not an afterthought. On most of these engagements a large part of the value is a defect report precise enough for another team to act on.
Engagements where this capability was the substance of the work rather than a line item.
Since 2019, Soludome has managed infrastructure and platform operations for one of Spain's largest online communities. The work covers server and performance optimisation, CDN architecture, load balancing and site security under sustained traffic.
An established community platform had accumulated three separate problems that compounded each other: page loads that had degraded with growth, search visibility that had not kept pace with the community's authority, and a security posture that had not been revisited as the platform's profile grew. The engagement addressed all three concurrently.
A large marketplace platform in the United States was experiencing critical downtime from compounding traffic and application faults. Soludome analysed requests through the web application firewall, identified and blocked the DDoS patterns, and repaired a third-party payment flow that allowed links to be generated repeatedly without authentication. Server load fell and platform performance improved after the work.
These overlap at the edges. Sending you to the right page is more useful than having you work it out.
That is a different problem with a different remedy. See Website Performance Engineering.
Distinguishing those two is the first thing we do. See Infrastructure Engineering.
The number that matters is concurrent users, not monthly pageviews. A platform with a large but casual audience can be entirely comfortable where one with fewer users in long sessions is not. We size the question to your load profile rather than to a threshold, because the threshold would be arbitrary.
Almost exclusively, and it is a genuine constraint on the work. There is no maintenance window on a platform with users. Every change is introduced into production and validated there, which is slower and requires more discipline than a rebuild — and it is the only option that does not involve taking the service offline.
Hosting is where the capacity is, but most high-traffic failures are architectural rather than provisioned. A platform that does avoidable work on every request will fail at some load regardless of how much infrastructure is beneath it. We address both, but the architecture is usually the larger win and the cheaper one.
By instrumenting the moment it happens and reproducing the load that produces it. Production-only failures are the hardest class of problem in this work, and they are usually resolved by narrowing from a symptom to a specific resource that saturates at a specific concurrency — which requires evidence collected at the time, not afterwards.
Describe what is happening rather than what you think the cause is. If we are not the right people for it, we will say so.