cost-optimization

name	cost-optimization
description	Analyze and optimize costs for AI agent workloads on AWS — model selection, caching, memory pricing, scaling strategies. Use when the user asks about costs, pricing, or optimization.

Cost Optimization for AI Agents

Model Selection

• Claude Haiku for simple routing/classification (~10x cheaper than Opus)
• Claude Sonnet for most agent tasks (good balance)
• Claude Opus only for complex reasoning/code generation
• Amazon Nova Lite/Micro for high-volume, simple tasks
• Consider prompt caching for repeated system prompts (up to 90% savings)

AgentCore Pricing

• Runtime: per-second compute (scale-to-zero = no idle cost)
• Memory: per-event storage + per-retrieval calls
• Browser: per-session charges
• Network: standard EC2 data transfer rates

Optimization Strategies

• Prompt engineering: shorter prompts = fewer tokens = lower cost
• Lazy skill loading: only load full SKILL.md when needed (not in system prompt)
• Memory compaction: CompactionHook to summarize long conversations
• Session pooling: reuse agent instances across invocations
• Tiered model routing: use cheap model for triage, expensive for complex tasks
• Caching: cache tool results (e.g., API calls) to avoid redundant work

Memory Cost Control

• STM events: minimize event size (don't store large payloads)
• LTM strategies: 4 strategies × extraction cost per event
• Retrieval: semantic search has per-query cost
• Tip: use explicit save_memory for important facts only, don't over-index

Monitoring Costs

• CloudWatch metrics for invocation count, duration, token usage
• Set billing alarms for unexpected spikes
• Track per-user usage for chargeback models