“cost-optimization”
Cost Governance for Agent Fleets: Budget Caps, Spend Tracking, and Alerts
A practical guide to implementing cost governance for AI agent fleets, including budget caps, real-time spend tracking, alerting, and cost allocation across teams.
Model Routing for Multi-Agent Systems: Matching Tasks to the Right Model
Learn how to implement intelligent model routing in multi-agent systems using LiteLLM, matching each task to the optimal model for cost, speed, and quality.
Prompt Caching for Agentic Loops: Cutting Costs in Multi-Turn Systems
Reduce LLM API costs in multi-turn agent systems with prompt caching strategies. Learn to cache system instructions, tool definitions, and conversation context for agentic workflows.
Semantic Caching to Reduce Latency and Spend
Users ask the same question a hundred different ways. Semantic caching serves cached answers for similar queries — cutting LLM spend by up to 86% and latency from seconds to milliseconds.
Prompt Caching Strategies to Cut LLM API Costs
Cached input tokens are ~90% cheaper than fresh ones. Learn implicit vs explicit caching, TTL design, and how to structure prompts so your cache hits.
Sustainable AI: Strategies for Token Efficiency in Long-Running Gemini 3 Loops
Agent loops compound token costs linearly with every turn. Learn six concrete strategies to cut token usage by up to 70% in long-running Gemini 3 agents.
The "Context Caching" Revolution: Optimizing Costs for Gemini 3 Multi-Agent Clusters
Discover how Gemini 3’s context caching is fundamentally changing the economics of multi-agent systems by drastically reducing token costs and latency through the Google ADK.
Cost Optimization in AI Development: Managing API Bills and Resource Usage
Learn how to optimize AI development costs by managing token usage, choosing the right models, and implementing automated monitoring.