Akarsh C

Learn how startups can avoid AI provider lock-in, switch between GPT-5.5, Claude, Gemini, DeepSeek, and Kimi without code changes, compare costs, test safely, and build more reliable AI products.

Direct SDK calls work until they don't. This post breaks down the architecture shift from calling providers directly to routing through an AI gateway — when it becomes necessary, what signals indicate it's time, and what the gateway layer actually does.

A deep technical breakdown of why Elixir and the Phoenix framework — with OTP supervision trees, Oban job queues, Phoenix Channels, ETS caching, and Cloak encryption — are uniquely suited for building production AI gateways and orchestration layers.

The technical decisions that separate AI agents that work in demos from AI agents that survive production — failover chains, prompt caching, request isolation, metered billing, and the infrastructure nobody talks about.

A practical comparison of the best LLM frameworks in 2026 — LangChain, LlamaIndex, Haystack, Semantic Kernel, and more. Learn which one fits your use case, and why most LLM frameworks struggle in production.

Learn how to build production-ready AI systems using event-driven architecture. Decouple AI generation from delivery, process webhooks, use callbacks, and stream results in real time.

Discover why we built ModelRiver: real-time AI infrastructure, auto-failover, structured outputs, client SDKs, CLI, and observability for developers shipping AI products.