Horizontal Scaling

Horizontal scaling, also called scaling out, adds more servers to handle increased load. This approach works well for stateless services where any instance can handle any request. It provides natural fault tolerance because the failure of one instance does not affect others, and it can scale incrementally by adding one server at a time.

For AI product teams, horizontal scaling is the primary strategy for inference services because it allows matching capacity to demand precisely. Running ten smaller inference instances provides better availability than two large ones, since the system tolerates individual instance failures gracefully. However, horizontal scaling requires that the application be designed for it: session state must be externalized, data must be partitioned or replicated, and load balancing must distribute requests effectively. Growth teams benefit from horizontally scaled AI services because traffic from experiments and campaigns distributes naturally across instances. The key challenge is managing the total cost of many instances while maintaining fast enough scaling to handle sudden demand increases from viral moments or successful growth campaigns.