Auto-Scaling
What auto-scaling is, how it adjusts capacity dynamically, and how to configure scaling policies for cost-efficient AI workloads.
Cost-Optimization
Batch Inference Patterns for AI Workloads
Processing large volumes of AI inference requests efficiently. Queue design, throughput optimization, error handling, and cost management …
Capacity Planning for AI Inference
How to right-size GPU and TPU clusters, configure autoscaling for inference workloads, manage GPU memory, and plan capacity for variable AI …
Cost Estimation for AWS AI Services
How to estimate and manage costs for AI workloads on AWS, covering Bedrock, SageMaker, compute, storage, and strategies for cost …
GPU Pooling
Shared GPU infrastructure with intelligent scheduling: maximizing GPU utilization across teams, managing heterogeneous hardware, and …
Model Distillation Patterns for Production AI
Using large model outputs to train smaller, cheaper, faster models for specific tasks. When to distill, training approaches, and quality …
Model Tier Routing - Matching Request Complexity to Model Cost
Route AI requests to different model tiers based on complexity, cost sensitivity, and quality requirements. Reduce spend without sacrificing …
Multi-Model Routing Patterns
Strategies for routing requests to different AI models based on task complexity, cost constraints, and latency requirements. Router design, …
Plan-and-Execute Pattern - Separating Planning from Execution in AI Agents
A two-phase agent pattern where a capable planner model creates a step-by-step plan, then delegates each step to cheaper, faster executor …
Reducing LLM Inference Costs in Production
Practical strategies for reducing LLM API and hosting costs without sacrificing quality, from caching and routing to model selection and …
Semantic Caching for AI Applications
Caching AI model responses based on semantic similarity rather than exact match. Implementation patterns, cache invalidation, and …
Token Budget
The maximum number of tokens allocated for an LLM request or workflow, used to control costs, latency, and context window utilization.
Cost Optimization (Well-Architected Pillar)
The Well-Architected pillar covering right-sizing, reserved capacity, spot instances, and cost allocation - and how it applies to AI …
AI Cost Optimization Patterns
Model selection by task, caching strategies, batch vs real-time processing, and tiered inference with Haiku, Sonnet, and Opus.