Definition
In the context of RAG pipelines and AI agents, load balancing is the distribution of inference requests, embedding generations, and vector search queries across multiple model instances or database shards to prevent rate-limiting and minimize latency. It involves architectural trade-offs between system reliability and the complexity of maintaining stateful context across different execution nodes.
Distributing LLM inference tokens and context state rather than generic web server traffic.
"A multi-lane toll plaza where a coordinator directs incoming cars (queries) to the fastest moving lane (available LLM instance) to prevent a single-point backup."
- Rate Limiting(Prerequisite)
- Model Fallback(Component)
- Horizontal Scaling(Prerequisite)
- Vector Sharding(Component)
Conceptual Overview
In the context of RAG pipelines and AI agents, load balancing is the distribution of inference requests, embedding generations, and vector search queries across multiple model instances or database shards to prevent rate-limiting and minimize latency. It involves architectural trade-offs between system reliability and the complexity of maintaining stateful context across different execution nodes.
Disambiguation
Distributing LLM inference tokens and context state rather than generic web server traffic.
Visual Analog
A multi-lane toll plaza where a coordinator directs incoming cars (queries) to the fastest moving lane (available LLM instance) to prevent a single-point backup.