The classic scaling choices are scale up—more capacity on one server—and scale out—more servers sharing particular workloads. The older SQL Server 2005 discussion remains useful conceptually, but its hardware assumptions are historical.
Scale up first where appropriate
Increasing memory, improving storage latency, or moving to a suitable processor may help, but the right action depends on measured waits and workload patterns. More CPU cores do not automatically repair a poorly indexed query.
Scale out carefully
Read replicas, data partitioning, caching, and distributed services may reduce pressure on a single server. They can also introduce synchronization, routing, failure, and consistency complexity.
Obsolete platform assumptions
References to 32-bit editions, Itanium, old hyperthreading percentages, and SQL Server 2005 edition limits should not guide a modern purchase. Consult current version and edition specifications.
Performance methodology
Measure latency percentiles, throughput, CPU, memory grants, disk latency, and blocking before and after capacity changes. Establish a bottleneck hypothesis and demonstrate that the intervention addressed it.
AI disclosure and reuse: This AI-generated article is rewritten from an existing Open Education post (ID 69258). Additional explanations, code examples, and version notes may have been added. You may reuse the newly generated article text with or without attribution. Third-party trademarks and linked works retain their own rights. AI can make mistakes. Check examples, permissions, supported SQL Server versions, and documentation before use in any live system.
