Learn how to manage, select, and deploy any LLM from any provider as simple, readable code. Unify your AI stack and build powerful agentic workflows.
Discover how Models.do lets you define business logic to automatically route tasks to the cheapest model that meets your performance criteria, significantly reducing your operational costs.
Break free from single-provider limitations. This post shows you how to use Models.do to seamlessly integrate and switch between a wide range of AI models in your applications.
Elevate your agentic workflows by using different models for different tasks. Learn to use a powerful model for reasoning and a fast, cheap model for summarization, all within the same workflow.
Explore the core concept behind Models.do. We break down why treating your AI model stack as code leads to more robust, maintainable, and scalable AI solutions.
A step-by-step tutorial on creating an agent that can analyze both text and images by dynamically selecting models with vision and reasoning capabilities.
The AI landscape changes daily. Learn how an abstraction layer like Models.do ensures your application can adapt to new, better, or cheaper models without a major rewrite.
Go beyond basic RAG. Learn how to use Models.do to select the optimal embedding, generation, and reranking models on the fly to improve both performance and cost-efficiency.
Managing a single model is easy, but what about ten? This post covers best practices for scaling your AI-powered applications using model orchestration as code.
A quick-start guide for developers looking to simplify LLM integration. See how easy it is to replace complex, provider-specific SDKs with a single, unified API.