How do you handle scale?
We measure before optimising. From the start, we build the elements that are costly to add later: a sound data model, indexes that match real queries, a clear separation between request handling and background work, and stateless services. Caching, read replicas, sharding, queues and service separation are added when monitoring shows where they are needed.
Most scaling issues we are asked to resolve are not architectural. They are typically missing indexes, N+1 query patterns or slow third-party calls made during a request. These can usually be identified quickly with query logging and profiling, and fixed within days.
We avoid splitting applications into separate services before there is a clear need. A well-structured single application with clear internal boundaries scales further than many teams expect, and can be divided later along boundaries informed by real usage.
Observability is in place from the start: structured logging with request correlation, metrics on critical paths, tracing where services interact, and alerts based on user-facing symptoms.
We also define what scale means for each system before designing for it, as many short requests and many persistent connections require different approaches.
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