The real price of "serverless" at scale
Cheap at low volume, worth re-checking at high volume.
Serverless is genuinely excellent for the problem it's usually sold on: unpredictable or low traffic, where paying only for what you use beats running an idle server. The economics quietly flip at sustained high volume, where a right-sized always-on instance is often meaningfully cheaper than paying the per-invocation premium at scale.
The invisible costs beyond the bill
Cold starts, execution time limits, and debugging a distributed web of functions instead of a service with a clear boundary are real engineering costs that don't show up until the system is complex enough that they're expensive to unwind.
The crossover point is measurable
For any given workload, there's a volume level where always-on compute becomes cheaper than per-invocation pricing — and it's usually lower than teams expect. Modeling that crossover with real numbers, rather than assuming serverless stays cheap forever, prevents an unpleasant bill surprise later.
One tool, not a default architecture
The pragmatic approach is treating serverless as one tool among several rather than a default architecture — genuinely excellent for spiky or infrequent workloads, worth re-evaluating against traditional compute once a workload becomes sustained and predictable.
- Serverless economics are best for unpredictable or low-volume workloads.
- At sustained high volume, right-sized always-on compute is often cheaper.
- Cold starts and distributed debugging are real costs that compound with complexity.
CDN strategy: the invisible infrastructure decision
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