Definition
The architectural capacity of a RAG pipeline or AI Agent framework to maintain performance standards—specifically sub-second retrieval latency and high inference throughput—as the underlying vector dataset grows to billions of records and concurrent user requests increase. It involves balancing horizontal scaling of vector databases with the computational demands of iterative agentic reasoning.
Focuses on system-wide throughput and data volume capacity, rather than the 'scaling' of an LLM's context window.
"A modular logistics hub where new loading docks (APIs) and storage aisles (Shards) can be added instantly to handle a holiday rush without slowing down the forklifts."
- Sharding(Component)
- Horizontal Scaling(Implementation Strategy)
- Throughput(Measurement)
- Vector Indexing(Prerequisite)
- Latency(Trade-off)
Conceptual Overview
The architectural capacity of a RAG pipeline or AI Agent framework to maintain performance standards—specifically sub-second retrieval latency and high inference throughput—as the underlying vector dataset grows to billions of records and concurrent user requests increase. It involves balancing horizontal scaling of vector databases with the computational demands of iterative agentic reasoning.
Disambiguation
Focuses on system-wide throughput and data volume capacity, rather than the 'scaling' of an LLM's context window.
Visual Analog
A modular logistics hub where new loading docks (APIs) and storage aisles (Shards) can be added instantly to handle a holiday rush without slowing down the forklifts.