Model Routing

Learn how to build resilient AI applications by implementing a multi-model routing layer that prevents vendor lock-in and optimizes performance.

Discover concrete engineering techniques to lower production LLM API spending by up to 80%. Learn about prompt prefix caching, intelligent multi-tier routing, context pruning, batch processing, and safeguard circuit breakers.

A technical guide on implementing a multi-model AI strategy using the OpenAI SDK and n1n.ai to optimize costs and performance across different LLM tasks.

DeepSeek V4 has officially launched with an MIT license, revealing a staggering 137x price difference compared to Western models. This guide explores the July 2026 LLM landscape and how to optimize your API costs.

Discover why open-source models now handle 65% of global token traffic and learn how to implement a Python-based multi-model router to optimize your AI infrastructure costs.

Guillermo Rauch highlights the critical need to separate large language models from agentic orchestration to optimize for price, performance, and production stability.

An in-depth look at GPT-5.5's performance metrics, hidden hallucination risks, and why a multi-model approach using Claude and OpenAI is essential for modern AI agents.

Learn how to slash your LLM API spend by implementing a tiered model routing architecture, moving simple tasks to local models while reserving frontier models for complex reasoning.

Using frontier models like GPT-4o for every task is a massive budget leak. Learn how to implement a 5-node decision tree to route tasks based on reasoning depth, latency, and cost, reducing expenses by up to 80% without sacrificing accuracy.

Learn how to slash your LLM API bills by 40% without sacrificing quality by implementing a tiered model routing strategy using DeepSeek, Gemini, and Claude.

A deep dive into cost-performance optimization for AI agents in 2026, comparing the Claude 4.6 family and GPT-5 with a focus on intelligent routing strategies.