What is Horizontal vs Vertical Scaling?
Updated Apr 28, 2026
Short answer
Horizontal scaling means adding or removing multiple instances of an application to handle changing workloads, while vertical scaling means increasing or decreasing the resources of a single instance, such as CPU, memory, or storage. In Azure, horizontal scaling is commonly achieved with services like Azure Virtual Machine Scale Sets and App Service autoscale, while vertical scaling is done by changing the size of a VM or service tier. Horizontal scaling usually provides better availability and long-term growth, while vertical scaling is often simpler but has physical limits.
Deep explanation
Scaling is the process of adjusting computing resources to match application demand. Applications need scaling because traffic and workload are rarely constant: an online store may receive normal traffic most of the year but experience millions of requests during a sale.
The two main approaches are horizontal scaling and vertical scaling.
Horizontal Scaling (Scale Out / Scale In)
Horizontal scaling means changing the number of application instances running.
- Scale out: Add more instances when demand increases.
- Scale in: Remove instances when demand decreases.
For example, instead of running one large web server, an application can run five smaller web servers behind a load balancer. Incoming requests are distributed across all available servers.
In Azure, common horizontal scaling options include:
- Microsoft Azure Virtual Machine Scale Sets, which automatically add or remove VM instances.
- Azure App Service autoscale, which adjusts the number of application instances based on metrics such as CPU usage or request count.
- Azure Kubernetes Service (AKS), which can increase or decrease container replicas.
Example architecture:
Users | v Azure Load Balancer | ------------------------- | | | v v v App 1 App 2 App 3Advantages of horizontal scaling:
- Handles large increases in traffic.
- Improves availability because traffic can continue if one instance fails.
- Allows gradual growth by adding resources as needed.
- Works well with cloud-native applications.
Challenges:
- Requires applications to be designed to run across multiple instances.
- Data synchronization and session management can become more complex.
- Requires load balancing and monitoring.
Vertical Scaling (Scale Up / Scale Down)
Vertical scaling means increasing or decreasing the resources of a single machine or service instance.
Examples:
- Increasing a VM from 2 CPUs and 8 GB RAM to 8 CPUs and 32 GB RAM.
- Moving an Azure SQL Database to a higher performance tier.
Terms:
- Scale up: Increase resources.
- Scale down: Reduce resources.
Example:
Before:
Single Azure VM----------------CPU: 2 coresRAM: 8 GBStorage: 100 GBAfter scaling up:
Single Azure VM----------------CPU: 8 coresRAM: 32 GBStorage: 500 GBAdvantages of vertical scaling:
- Simple to implement.
- Does not usually require application changes.
- Useful for workloads that cannot easily be distributed.
Challenges:
- There is a maximum size limit for any single machine.
- Larger instances can become expensive.
- A single instance can still be a single point of failure.
- Scaling may require downtime for some systems.
Horizontal vs Vertical Scaling Comparison
| Feature | Horizontal Scaling | Vertical Scaling |
|---|---|---|
| Changes | Number of instances | Size of an instance |
| Also called | Scale out / scale in | Scale up / scale down |
| Example | Add more VMs | Increase VM CPU and RAM |
| Availability | Usually higher | Usually unchanged |
| Complexity | Higher | Lower |
| Limit | Depends on platform capacity | Limited by maximum instance size |
| Best for | Web apps, distributed systems | Databases, legacy applications |
In real cloud environments, companies often use both approaches together. For example, a web application may horizontally scale its web servers while vertically scaling a database to provide more processing power.
Real-world example
Imagine an online shopping website hosted on Azure.
During normal days, the application runs with two App Service instances:
Users | vAzure App Service | +-- Instance 1 | +-- Instance 2During a major sale, traffic increases significantly. Azure autoscale detects high CPU usage and creates additional instances:
Users | vAzure Load Balancer | +-- Instance 1 +-- Instance 2 +-- Instance 3 +-- Instance 4 +-- Instance 5A simplified Azure CLI example for changing a VM size (vertical scaling) might look like:
az vm resize \ --resource-group MyResourceGroup \ --name MyWebVM \ --size Standard_D4s_v5For horizontal scaling, an Azure App Service can be configured to increase instance count based on demand:
Minimum instances: 2Maximum instances: 10
Rule:If CPU usage > 70% for 10 minutes: Add another instanceThe website can handle the traffic spike without manually creating servers.
Common mistakes
- * Confusing horizontal scaling with simply upgrading a server's hardware
- upgrading hardware is vertical scaling.
- * Assuming vertical scaling can provide unlimited growth
- every machine has a maximum capacity.
- * Adding more application instances without considering whether the application supports distributed execution.
- * Storing user sessions only in local memory when horizontally scaling, causing users to lose sessions when requests move between instances.
- * Scaling resources without monitoring actual application bottlenecks.
- * Believing horizontal scaling always reduces costs
- running many unnecessary instances can increase expenses.
- * Forgetting that databases often require different scaling strategies than stateless application servers.
Follow-up questions
- Why is horizontal scaling usually preferred for cloud applications?
- How does Azure automatically perform horizontal scaling?
- What is the difference between scaling and load balancing?
- Why can vertical scaling be difficult for databases?
- What application design practices make horizontal scaling easier?