When serverless is the right call (and when it isn't)
Serverless is a sharp tool, not a default. A practical framework for deciding where it fits — and where it'll bite you.
Serverless gets pitched as either the future of everything or an expensive trap, depending on who's talking. The truth is narrower and more useful: it's excellent for a specific shape of problem and awkward for others.
Where it shines
Spiky, event-driven, and unpredictable workloads are where serverless earns its keep — you pay for what runs and nothing for idle. Glue code, scheduled jobs, webhook handlers, and APIs with uneven traffic all fit naturally. If your load looks like a city skyline, serverless smooths the bill.
Where it fights you
Steady, high-throughput workloads that run hot around the clock are often cheaper and simpler on provisioned compute. Long-running jobs, heavy in-memory state, and chatty low-latency call graphs all rub against the model. And cold starts, while much improved, still matter for some user-facing paths.
Count the whole cost, not just the invoice
The compute line is only part of the story. Serverless shifts complexity into observability, local development, and the operational model. That trade is worth it when it buys you elasticity you'd otherwise build by hand — and a poor trade when you're forcing a stateful, steady workload into a stateless, bursty tool.
Decide per workload, not per company
The healthiest architectures are usually mixed: serverless at the edges and for the bursty work, provisioned compute for the steady core. 'Are we a serverless shop?' is the wrong question. 'Is this workload a good fit?' is the right one, and you answer it one service at a time.
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