Horizontal vs Vertical Scaling: Which One Should You Use and When?

Horizontal vs Vertical Scaling: Which One Should You Use and When?

There are two basic ways to add system capacity:

• Vertical scaling: make one machine bigger.
• Horizontal scaling: add more machines/instances.

Neither is automatically better. The correct choice depends on the workload.

Vertical Scaling: Simpler First Step

Scaling up can mean moving from 4 vCPU / 16 GB RAM to 16 vCPU / 64 GB RAM.

It is attractive because application architecture may not need to change. Stateful systems such as relational databases also often benefit from vertical scaling before more complex distribution is justified.

The limits are clear: one host has a maximum size, maintenance can affect one large failure domain, and bigger instances can become expensive.

Horizontal Scaling: More Independent Capacity

Scaling out adds replicas behind a load balancer.

Load Balancer
  ├→ API 1
  ├→ API 2
  └→ API 3

AWS explicitly recommends multiple smaller resources to reduce the impact of a single failure where the workload supports it.

Horizontal scaling works best for stateless APIs, workers, and partitionable workloads.

State Makes Scale-Out Harder

If a user session or file exists only on one server, any request cannot safely reach any replica.

Move shared/durable state to appropriate databases, object storage, or shared session/cache systems before expecting clean horizontal scaling.

Horizontal Scaling Adds Distributed-System Cost

More nodes introduce:

• load balancing and service discovery
• replica coordination
• network failures
• cache/session consistency
• deployment and observability complexity

Do not distribute a workload just because horizontal scaling sounds more modern.

Most Production Systems Use Both

A common pattern is:

API → horizontal replicas
Database → vertically sized primary + replicas when useful
Workers → horizontal replicas
Cache → vertically sized and/or sharded as traffic grows

Use the simplest scaling dimension available for each component.

Quick Decision

Final Takeaway

Scale up when a larger machine solves the problem cheaply and cleanly. Scale out when the workload can be divided across independent replicas and you need more throughput or failure isolation. Mature systems usually combine both rather than treating them as competing philosophies.

References

• AWS Well-Architected Reliability Design Principles
• Azure: Design to Scale Out