Skip to content

System Design Foundations

For any design decision in HLD use these parameters:

  • Fidelity (It is fulfilling the use case or purpose.)
  • Simplicity (Is it simple to implement and manage)
  • Cost effectiveness ( Is it cost effective for a given use case and scale)

For a given amount of money having one large instance will give lesser computation power as compared to many smaller instances.

Arguments for "Many Small Instances" (Why the statement holds true)

While raw pricing might be linear, "effective" power often favors horizontal scaling in specific scenarios:

  • Granular Auto-Scaling: With small instances, you can scale your fleet to match traffic demand very closely. If you have one massive instance, you are paying for 100% of capacity even if traffic drops to 10%. Smaller instances reduce idle waste.
  • Hardware Limits & Premium Pricing: Once you push into "extreme" vertical scaling (e.g., instances with terabytes of RAM or 100+ cores), the price-performance ratio often degrades. You begin paying a premium for the specialized engineering required to keep that much density on a single board (NUMA architectures, etc.).
  • Burst Allowances: Some cloud families (like AWS T series) offer burst credits. A fleet of smaller instances might collectively accumulate a larger pool of burstable CPU credits than a single large instance, offering higher peak performance for short durations.

Arguments for "One Large Instance" (Why the statement is false)

There are strong technical reasons why a single large instance often yields more actual computation power for the application than many small ones:

  • The "OS Tax" (Overhead): Every instance runs its own Operating System kernel, logging agents, monitoring sidecars, and networking stack.
  • Scenario A (1 Large): You pay the OS memory/CPU overhead once.
  • Scenario B (10 Small): You pay the OS overhead 10 times.

  • Result: The large instance leaves more resources available for your actual application code.

  • Inter-Node Latency: Communication between processes on the same machine (via shared memory or loopback) is nanoseconds/microseconds. Communication between distributed instances involves network hops (milliseconds). For tightly coupled workloads, distributed computing "power" is lost to network wait times.

  • Resource Fragmentation (Bin Packing): If a single task requires 6GB of RAM and you have 4GB instances, you cannot run the task at all. A large instance absorbs "lumpy" resource requirements much better than small fragmented ones.

Summary Table

Feature Many Small Instances (Scale Out) One Large Instance (Scale Up)
Availability Higher. If one fails, only a fraction of capacity is lost. Lower. Single point of failure (SPOF).
Cost Efficiency Higher for variable traffic (better auto-scaling). Higher for steady-state heavy processing (less OS overhead).
Complexity High. Requires load balancers, service discovery, distributed logging. Low. Monolithic management.
Ideal Workload Stateless web apps, microservices. Database primaries, in-memory caches, legacy monoliths.