Horizontal vs Vertical Scaling: How to Choose in 2026

In horizontal vs vertical scaling, vertical scaling (scaling up) gives one server more CPU, memory or storage, while horizontal scaling (scaling out) adds more servers and spreads the load across them. Scale up when your workload is steady and hard to split, such as a single database. Scale out when traffic swings, when you need to survive a server failure, or when one machine is no longer enough. Most growing systems end up using both.

This guide explains how each approach works on today's cloud platforms, what each one costs you in downtime and complexity, and how to decide. Platform details are current as of October 2026.

Horizontal vs vertical scaling at a glance

The short version: scaling up is the quick fix that needs no code changes. Scaling out is the long-term fix that needs your application to be built for it.

What vertical scaling means in practice

Vertical scaling moves your application to a bigger machine. On a cloud platform that usually means picking a larger instance or virtual machine size. Microsoft's Azure autoscaling guidance describes it as changing the capacity of a resource, for example moving an app to a larger VM size, and notes that it often makes the system temporarily unavailable while it is redeployed.

AWS is just as direct. To change the type of an EBS-backed EC2 instance, its documentation says you must stop the instance first and plan for downtime. A public IPv4 address that is not an Elastic IP is also released and replaced when the instance restarts.

Why teams still scale up

• No code changes. The application runs exactly as before, just with more headroom.
• Simple operations. One machine to patch, monitor and back up.
• Good for hard-to-split workloads. Relational databases, in-memory caches and older monolithic apps often run best on one large node.

Where scaling up runs out

• A hard ceiling. Even the biggest machines stop somewhere. The largest AWS U7i high memory instances reach 32,768 GiB of memory, which few businesses will ever need, but the limit exists and the price climbs steeply on the way.
• Downtime on each resize. Every step up usually means a restart window.
• One point of failure. If that machine or its zone goes down, so does your service.
• Paying for peak all day. A server sized for your busiest hour sits mostly idle the rest of the time.

What horizontal scaling means in practice

Horizontal scaling runs several copies of your application and puts a load balancer in front of them. When demand rises you add copies, and when it falls you remove them. Azure's guidance calls this scaling out and in, and points out that existing instances keep serving requests while new ones start, as long as the application is designed for it.

Every major cloud automates this. On AWS, EC2 Auto Scaling groups let you set a minimum, desired and maximum number of instances, replace unhealthy instances automatically, spread them across Availability Zones and register them with a load balancer. AWS charges nothing extra for Auto Scaling itself; you pay for the instances and related resources it runs. Azure does the same with Virtual Machine Scale Sets and App Service autoscale, and Google Cloud with managed instance groups.

What your application needs before it can scale out

• Stateless app servers. Sessions, carts and uploads must live in a shared store such as a database, Redis or object storage, not on one server's disk or memory.
• A load balancer and health checks. Traffic should reach only healthy instances.
• Fast, repeatable startup. New instances come from an image or container, not from manual setup.
• Safe shutdown. When an instance is removed, it should finish or hand off its work first.
• Dependencies that keep up. Adding app servers does not help if the database is the bottleneck. Azure's guidance makes the same point: find the bottleneck before adding compute.

Where scaling out gets harder

The cost is complexity. You now run a distributed system, so you need centralised logs, metrics across many instances, a deployment process that updates them all, and an answer for data that more than one server writes to. Autoscaling rules also need tuning. Azure warns about "flapping", where rules scale in and out over and over because the thresholds sit too close together.

Scaling the database: the hardest part

App servers are usually easy to scale out once they are stateless. The database is where most teams hit the wall, and it is where the two approaches are most often combined.

• Scale up first. A bigger database instance is the simplest step and needs no code changes.
• Add read replicas for read-heavy apps. Amazon RDS read replicas are read-only copies that let you scale out beyond a single instance for read traffic. Writes still go to one primary, replication is asynchronous so replicas can lag slightly, and RDS does not add or remove replicas automatically.
• Cache hot data. A cache in front of the database often removes more load than any resize.
• Partition only when you must. Splitting data across several databases (sharding) scales writes, but it changes how your application queries data. Treat it as a last step, not a first one.

Horizontal and vertical scaling in Kubernetes

Containers use the same two ideas. The Kubernetes autoscaling documentation defines horizontal scaling as changing the number of replicas of a workload and vertical scaling as changing the resources given to each replica.

• Horizontal Pod Autoscaler. Built into Kubernetes. It adds or removes pod replicas based on metrics such as CPU or memory use.
• Vertical Pod Autoscaler. An add-on that you install separately. It adjusts the CPU and memory requests of pods.
• In-place pod resize. Stable since Kubernetes 1.35 in December 2025. You can change CPU and memory on a running pod, often without restarting the container. The Kubernetes docs note that the Vertical Pod Autoscaler does not use in-place resizing yet.
• Node autoscaling. Adds or removes the machines underneath when pods cannot be scheduled or nodes sit empty.

In practice, most teams run the Horizontal Pod Autoscaler for their app tier and size pods vertically from measured usage.

How to choose between horizontal and vertical scaling

A few questions settle most cases:

1. Where is the bottleneck? Measure CPU, memory, disk and database load before changing anything. Scaling the wrong tier wastes money.
2. How spiky is demand? Flat demand suits a right-sized server. Peaks that are several times your average suit autoscaling.
3. What does an outage cost you? If one machine failing takes the business offline, you need at least two instances in different zones, whatever your traffic.
4. Can the app run as several copies? If not, scaling up buys time while you make it stateless.

The hybrid approach most teams use

The realistic answer is rarely one or the other. A typical setup looks like this: stateless web and API servers that scale out automatically behind a load balancer, each one sized vertically to a sensible instance type; a database scaled up to a strong primary, with read replicas and a cache to take read load; and background workers that scale out on queue depth.

Revisit the sizing regularly. Instances that never pass 20% CPU can be scaled down, and scaling rules that fire every few minutes need wider thresholds. Azure also suggests scheduled scaling for predictable peaks, such as adding capacity before a known busy period, with metric-based rules on top for surprises.

How Codeeo can help

Codeeo designs and builds web applications and platforms, and is trusted by 200+ companies. If your app slows down under load or cannot run as more than one copy, our custom software development team can find the bottleneck and plan the move to a scalable architecture. For new builds, our web development team designs stateless, autoscaling-ready apps from the start, and dedicated developers can join your team for a refactor. Contact us to talk it through.