Cost Optimization
The ongoing practice of reducing infrastructure spending while maintaining required performance and reliability levels. Cost optimization involves right-sizing resources, leveraging pricing models, eliminating waste, and aligning spending with business value.
Cloud cost optimization starts with visibility: understanding what you are spending, where, and why. Common strategies include right-sizing instances to match actual utilization, using reserved instances or savings plans for steady-state workloads, leveraging spot instances for fault-tolerant tasks, implementing auto-scaling to avoid over-provisioning, and cleaning up unused resources like idle instances and orphaned storage volumes.
For AI product teams, cost optimization is critical because AI infrastructure costs can grow rapidly and unpredictably. GPU instances cost significantly more than CPU instances, and model inference costs scale with user traffic. Growth teams should track the unit economics of AI features: the cost per AI-generated recommendation, per inference request, or per user served. This enables informed decisions about which AI features justify their infrastructure cost through business impact. Strategies specific to AI cost optimization include batching inference requests, caching frequently requested predictions, using smaller distilled models where full model accuracy is not required, and implementing tiered inference that routes simple queries to lightweight models while reserving expensive models for complex cases.
Related Terms
Content Delivery Network
A geographically distributed network of proxy servers that caches and delivers content from locations closest to end users. CDNs reduce latency, improve load times, and absorb traffic spikes by serving content from edge nodes rather than a single origin server.
Edge Computing
A distributed computing paradigm that processes data closer to the source of generation rather than in a centralized data center. Edge computing reduces latency, conserves bandwidth, and enables real-time processing for latency-sensitive applications.
Serverless Computing
A cloud execution model where the provider dynamically manages server allocation and scaling. Developers deploy functions or containers without provisioning infrastructure, paying only for actual compute time consumed rather than reserved capacity.
Function as a Service
A serverless computing category where developers deploy individual functions that execute in response to events. FaaS platforms like AWS Lambda, Google Cloud Functions, and Azure Functions handle all infrastructure management, scaling each function independently.
Platform as a Service
A cloud computing model that provides a complete development and deployment environment without managing underlying infrastructure. PaaS offerings like Heroku, Vercel, and Google App Engine handle servers, storage, networking, and runtime configuration.
Infrastructure as a Service
A cloud computing model that provides virtualized computing resources over the internet. IaaS offerings like AWS EC2, Google Compute Engine, and Azure Virtual Machines give teams full control over servers, storage, and networking without owning physical hardware.