LLM Pipelines Explained: How to Choose Between Chains, Flows, and Orchestrators

As AI developers increasingly string multiple LLM calls together, choosing the right tool to manage these workflows has become a common source of confusion. Two broad categories exist: LLM-native libraries like LangChain and LlamaIndex, which handle prompt templating and context passing, and general orchestrators like Airflow and Prefect, which treat LLM calls as standard tasks requiring reliability. A practical rule of thumb suggests using a chaining library for simple, mostly LLM-driven sequences, a full orchestrator for multi-step processes that cannot afford failures, and an agent framework when the model itself must decide the execution path. The author built a small tool called PromptKit to explore these trade-offs firsthand, finding that the choice often comes down to how deterministic the underlying process truly is. The core advice is to avoid reaching for heavy frameworks reflexively and instead pick the lightest tool that can handle retries and failures when they inevitably occur.