Serverless Architecture and Scale-to-Zero

Serverless inverts that. Your code sits dormant until a request arrives, runs, returns a response, and stops. Billing is based on execution time and the number of invocations. When nothing is happening, the cost approaches zero.

For a system with uneven traffic, that difference is substantial. An internal tool used during office hours, a seasonal store, a reporting job that runs twice a day, an API that gets ten thousand requests in an hour and then nothing until tomorrow. All of these spend most of their existence idle on a conventional server.

It works well when load is uneven or unpredictable, when the workload is naturally event-driven, and when you would otherwise be provisioning for a peak you rarely reach. It also removes a category of work entirely: there is no operating system to patch and no capacity planning to get wrong.

It works badly for steady, high-volume traffic. If your system serves consistent load around the clock, per-invocation pricing adds up to more than an instance you have already reserved. It is also a poor fit for long-running processes, anything needing large amounts of memory for a sustained period, and applications that depend on holding state between requests.

We look at your actual traffic before recommending it. Migrating a constantly busy application to serverless is a good way to increase both complexity and cost.

The first is cold starts. When a function has not been invoked recently, the platform has to load it before it can respond, and that adds latency to the first request. The size of that penalty depends on the runtime, the dependencies and the platform. For background processing nobody notices. For a page a customer is waiting on, it needs measuring and mitigating.

The second is database connections. A conventional application opens a pool of connections and reuses them. Serverless creates many short-lived execution environments, each wanting its own connection, and a database that was comfortable with fifty connections will not tolerate five hundred. This is solvable with a connection pooler or a database built for the pattern, but it has to be solved deliberately at the design stage.

Moving the whole thing at once is rarely the right answer. A more practical route is to pull out the parts with the most uneven load and run those serverless first, leaving the steady core where it is. Image processing, report generation, webhook handling, scheduled jobs and anything triggered by a queue tend to be good early candidates, because they are naturally event-shaped and usually already isolated from the rest of the code.

That gives you the cost benefit where it is largest and keeps the change contained. It also tells you quickly whether the model suits your team, which is worth knowing before committing the main application to it.

We set concurrency limits, execution timeouts and budget alerts as part of the build rather than after the first unpleasant invoice. The limits are chosen so that genuine growth is served while runaway behaviour is contained, and you are told when spend crosses a threshold you have agreed rather than at the end of the month.