Topic
Model Routing & Fallback Strategies
6 posts on model routing & fallback strategies — part of glossary on the n4n AI blog.
What is LLM model routing and why it matters
A practical definition of LLM model routing covering how it works, why engineers need it, a working code example, and the misconceptions that waste time.
How provider fallback chains work in practice
A practical guide to designing and implementing provider fallback chains for LLM inference, covering routing logic, health checks, latency budgets, and common failure modes.
How circuit breakers stop cascading LLM API failures
A practical guide to implementing circuit breakers for LLM API calls — states, thresholds, code patterns, and the tradeoffs that bite you in production.
How automatic fallback keeps apps online during outages
A practical guide to implementing automatic fallback for LLM outages, covering routing strategies, health checks, and common pitfalls that break production systems.
Cost-based routing: sending requests to the cheapest model
Cost-based LLM routing explained: how it works, why it matters for production systems, a concrete implementation example, and common misconceptions to avoid.
Building a fallback strategy for GPT-5 rate limits
A practical guide to implementing a robust fallback strategy for GPT-5 rate limits with code examples and verification steps.
More topics in glossary
- Structured Outputs & JSON Mode19
- AI Agents Fundamentals12
- Hallucination in LLMs11
- Sampling Parameters: Top-p, Top-k & Penalties11
- Context Window & Context Length10
- Fine-Tuning Fundamentals9
- Foundation Models: Base vs Instruct vs Chat9
- Model Families & Naming Conventions: GPT-5, Claude, Gemini 3, Llama 4, Mistral, DeepSeek, Qwen, Grok9
- Grounding & Fact-Checking in AI8
- LLM Benchmarks: MMLU, HumanEval, SWE-bench & GPQA8
- Max Tokens, Stop Sequences & Output Truncation8
- Quantization Formats: GGUF, GPTQ, AWQ & INT4/INT88