Horizontal vs Vertical Scaling in AI Systems

When facing the limits of individual LLMs, hallucination, context window constraints, auto-regressive bottlenecks, the field is increasingly exploring horizontal scaling (multiple smaller agents collaborating) rather than vertical scaling (bigger, more capable single models).

This parallels distributed systems architecture: instead of one powerful server, many coordinated workers. The shift reflects a recognition that some problems are better solved by division of labor than by raw capability.

The tradeoff: horizontal scaling introduces coordination overhead and communication complexity, but enables specialization, fault tolerance, and potentially better resource utilization.

Related:, 05-atom—collaboration-channel-definition, 05-atom—agentic-ai