Performance and reliability

Scale is performance and reliability engineering: I find what makes a web application slow, fragile or expensive to run, measure it, and fix it, from database queries and caching to infrastructure, deployments and Core Web Vitals. It is for teams in the US, UK, EU and the Gulf whose system already has real users and real traffic.

The work is the same whether the pressure is a slow checkout, a launch date or a cloud bill: measure first, then change the thing the numbers point at, then measure again.

It suits SaaS and e-commerce teams whose pages or APIs feel slow, sites failing Core Web Vitals in Search Console, media and commerce platforms facing a broadcast, a vote or a sale, and teams still deploying by hand or paying for servers nobody has right-sized.

It does not suit a site with no real traffic yet, because there is nothing real to measure, or a team that already runs a mature SRE function. For an event less than a week away with no staging environment, I can still help, but that is emergency work, not readiness.

"The site is slow and every team blames a different layer." "We have a launch in a few weeks and we've never tested at that load." "The bill doubled and traffic didn't." "Deploys happen on Friday nights because they scare us." "We have backups, we think."

Behind most of these sit a handful of causes I see again and again: queries without the right indexes, data fetched once per row instead of once per page, no cache in front of the expensive reads, slow work done inside the request instead of a queue, and images sent at full size. None of them needs a rewrite to fix.

You get measurements before you get opinions: where the time goes, page by page and endpoint by endpoint, down to individual queries. Then a ranked list of fixes with expected impact, effort and risk, and, if you want them, the fixes themselves, re-measured after each one.

The performance and Core Web Vitals audit is the measured report. Launch-day readiness adds a load model, load tests until something breaks, the changes that move the breaking point, and on-call cover during the event. Cloud, DevOps and cost covers infrastructure as code, CI/CD, monitoring, restore-tested backups and a cost report. The database design and query optimization offer and the SEO, AIO and GEO offer below go deeper on queries and on what search engines and AI crawlers receive.

With the fixed-price performance audit. It needs read access to the code, the database's slow-query log or a read replica, your analytics or Search Console, and your monitoring tool if you have one. It ends with a written report and a walkthrough with your team.

The results this approach has produced are on the case pages: a magazine's main pages went from 1–3 min → 3 s after a rebuild with fixed queries, caching and right-sized images, and a voting platform served 1.6M+ users over its campaign with API responses of <1 s at peak.

For UK and EU teams, slow pages cost search traffic as well as conversions, and most of the working day overlaps with mine, so audits and fixes run on short, same-day loops. Where the work touches personal data, it runs inside your infrastructure under a data processing agreement. For UAE teams, infrastructure work often starts with where data must stay: I build in a UAE or Saudi cloud region and keep backups, logs and monitoring data there too, so residency holds end to end, not only for the main database.