Autoscaling automatically adds or removes computing capacity, such as servers, containers or function instances, in response to demand. Applications keep up with traffic peaks without paying for idle resources during quiet periods.
FinOps is an operating practice that brings engineering, finance and business teams together to manage cloud spending. Teams share visibility of costs and make trade-offs between speed, quality and spend.
Kubernetes is an open-source platform for running containerised applications across a cluster of machines. It schedules containers, restarts failed ones, scales them with demand and routes traffic between them.
A load balancer is a network component that spreads incoming requests across several servers or containers. It keeps any single instance from being overloaded and steers traffic away from unhealthy ones.
Inference is running a trained model to produce an output, as opposed to training it. In production this is where most of the cost, latency and scaling work of an AI feature sits.
Serverless computing is a cloud model where the provider runs code or services on demand and manages the servers behind them. Teams pay for actual usage rather than for idle capacity.
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