Topic
Load Balancing for LLM APIs
4 posts on load balancing for llm apis — part of glossary on the n4n AI blog.
What is load balancing for LLM APIs
A precise technical definition of load balancing for LLM APIs, covering routing strategies, health checks, fallback behavior, and common misconceptions engineers encounter in production.
How token-aware load balancing improves LLM throughput
Learn how token-aware load balancing routes LLM requests by token count instead of request count, improving throughput and reducing tail latency across heterogeneous model fleets.
How n4n load balances across 240+ models and providers
A practical guide to load balancing across 240+ LLM models and providers, covering routing architecture, health checks, fallback strategies, and observability.
How load balancing reduces LLM API rate limit errors
Practical guide to using load balancing across LLM providers to eliminate rate limit errors, with implementation patterns and common pitfalls.
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