LLM integration
7 articles
Dedicated Python vs Node.js Developer for LLM Integration
Hire Node.js for LLM features inside a TypeScript product. Hire Python for RAG, evals, ingestion, and background AI workflows.
When Should You Use Background Jobs Instead of a Live LLM API Request?
Use a live LLM request for short interactive work. Use background jobs for long-running, document-heavy, or retry-sensitive AI workflows.
LLM Integration for Business: Direct API or LLM Gateway First?
Start with a direct provider API for a first AI feature. Add an LLM gateway when routing, fallback, spend controls, or audit logs matter.
LLM Integration Hiring: Backend Engineer, ML Engineer, or a Dedicated Team?
Hire a backend engineer for a narrow LLM feature, an ML engineer when model behavior is the bottleneck, and a dedicated team when product, retrieval, and reliability must ship together.
LLM Integration for Business: Should You Use MCP or Direct API Calls?
Use direct API calls for one in-product LLM workflow. Use MCP when the same tools must work across multiple AI clients with shared auth and connectors.
LLM Integration Cost in 2026: API Spend vs Engineering Time
LLM integration cost is usually driven more by engineering time than token pricing. Budget API spend, tool overhead, evals, logging, approvals, and human fallback.
LLM Integration Guide for Businesses: How to Add AI to Your Product in 2026
A practical guide to integrating large language models (GPT-4o, Claude, Gemini) into business applications, covering architecture patterns, cost control, evaluation, and the mistakes to avoid.