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Serverless is a cloud computing model where the developer ships only code and the provider handles everything else: servers, scaling, availability. Servers very much exist, yet they are invisible to the developer. You pay only for actual execution, by the millisecond: an unused service costs zero, one hit by a traffic wave scales by itself.
In the Function as a Service (FaaS) paradigm, code is organized into functions triggered by events: an HTTP request, an uploaded file, a queued message. The provider spins up the needed instances on the fly and tears them down once the event is handled. The trade-off is the cold start, the latency of the first spin-up, plus limits on execution duration and memory.
In AI, serverless covers two fronts: functions orchestrate lightweight AI flows (a webhook receives a document, calls an LLM and stores the result) and serverless inference lets you serve models paying only for the GPU seconds actually consumed, ideal for bursty workloads. Beyond AI it powers APIs, mobile app backends and event-driven processing of every kind.
The term was already circulating around 2012 in essays on the future of the cloud, but the model exploded with AWS Lambda, unveiled by Amazon in November 2014, followed by Google Cloud Functions and Azure Functions. The name is deliberately paradoxical: serverless describes the developer's experience, freed from managing machines, while the underlying data centers are busier than ever.
Grace gives you a serverless learning experience: you open the daily gym and everything is ready, with no infrastructure to set up and an immediate 0-100 score. In the scenarios on cloud architectures, the term lets you ask the AI for genuinely modern pay-per-use solutions.
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