What Is Horizontal Scaling?

What Is Horizontal Scaling? Horizontal scaling is a popular scalability model that involves adding additional machines to the pool of computing resources. This strategy is often recommended for applications that can handle large operations and high concurrency.

It distributes the load over a larger set of nodes, improving reliability and uptime while also offering redundancy. However, this approach can have its challenges and can be a costly option.


Scalability refers to the ability of an application or system to handle a certain number of simultaneous requests without causing any problems. As soon as this amount exceeds a particular threshold, the application or system runs out of available resources and can no longer process additional requests.

Horizontal scaling can be a great option when you need to distribute applications across multiple regions, reduce local latency or meet compliance rules. In this approach, each of your servers can handle a certain percentage of the overall load and the rest will be spread out across other machines in different locations.

While it is possible to scale a single server vertically (upgrading the machine to increase its capacity), horizontal scaling can be a better choice if your application does not require significant changes in the logic of the software. This is because the sequential piece of code will be broken into smaller parts, which can be distributed over the network via patterns such as MapReduce or Tuple Spaces.

For this to work, the software must be able to distribute the workload over multiple machines and use multi-threading to optimize the incoming requests. Moreover, it must be able to communicate with all of the servers in the cluster.

However, horizontal scaling can cause a few problems for your application. For example, it is difficult to ensure that a client’s session will be handled by one of the instances of your application every time they make a request.

In addition, horizontal scaling may not be suitable if your business relies on stateful applications. Any application that requires a lot of storage for data between sessions is not a good candidate for horizontal scaling as the user session will be inextricably tied to a specific instance.

Another important consideration is cost. Adding more servers through horizontal scaling costs more up front than vertically scaling. This can be problematic if you have a tight budget.

In some cases, horizontal scaling can even result in a reduction in the amount of storage or processing power that is available to your applications. For this reason, it is crucial to weigh the pros and cons of this model before making a decision.


In the cloud, horizontal scaling is a way to add capacity by spreading databases across a larger number of machines. This approach is a popular choice amongst many big companies that provide web services like Google, Yahoo, Facebook, EBay and Amazon.

Horizontal scaling is a reliable way to increase system capacity and improve performance, as it spreads load across multiple servers. This method also reduces the risk of hardware failure and provides increased reliability over time.

For example, a horizontally scaled database can use multiple read replicas to ensure that data is available across all servers, minimizing the chance of a single failure and improving overall performance. It is especially useful for large, cloud-based systems with high I/O concurrency.

This method is also a great way to decrease operational expenditures (OpEx) when you are deploying a cloud environment using leased or rented server infrastructure. You can remove some of the hardware when demand decreases and avoid paying for peak traffic resources.

One drawback to horizontal scaling is that it relies on network communication, which can be slow and unreliable. However, this can be mitigated with stateless scalability, which does not require any interaction between sessions on the server side.

Another disadvantage of horizontal scaling is that it can be costly to change hardware and software. When you need to add a new disk or increase the amount of memory, it can take some time for the system to recognize the change and process it correctly. This can also result in downtime as the application may need to be shut down for a while before it is ready to run again.

The best way to decide which scalability method is right for you is to work with a technology expert to clarify your application requirements. Once you have a clear understanding of what you need, it’s easy to see which methodology will suit your needs.

Vertical scaling is often preferred for smaller businesses or enterprises with fewer data throughput needs, as it can be easier to set up and manage. It also offers greater redundancy, which can help to prevent the loss of valuable data. It’s also cheaper to implement than a horizontally-scaled system, so it’s a good option for organizations with limited budgets.


Horizontal scaling is an excellent way to improve resilience and fault tolerance by distributing workloads among multiple servers. This allows you to scale up a system without worrying about running out of resources, as well as minimize the risk of data loss in case of a server failure.

In a nutshell, horizontal scaling is the process of adding new machines to your pool of existing infrastructure and spreading the load across them. This method of scalability allows you to increase the amount of CPU, RAM, and storage your infrastructure can handle.

The key to success with horizontal scaling is to build a system that is flexible enough to accommodate changes and upgrades. This means that you should consider decoupling your application as much as possible and using a tier-based architecture instead of a single-tier approach.

This strategy allows you to delegate various services or layers to different servers, which makes it easier to deploy and upgrade your system. Moreover, it also saves you the hassle of reworking your application’s architecture in order to accommodate the needs of a larger pool of machines.

Another reason to choose horizontal scaling is to maximize topographic distribution. For example, if you plan to deliver your application to a global audience, horizontal scaling is an ideal option. This will allow you to manage geo-latency, disasters, and downtimes better.

While horizontal scaling can be effective for many types of applications, it is important to understand the specifics of your business’s IT infrastructure before making a decision. This will ensure that you have the right hardware, software, and network infrastructure in place to support your scaling strategy effectively.

In contrast, vertical scaling is a more traditional approach to scalability. It involves adding more hardware to the existing pool of systems, such as a new CPU, RAM, and hard drive. This gives you more capacity to handle increased demands, but it also requires you to upgrade your existing hardware in order to get the most out of it.

In addition to this, vertical scaling can lead to downtime if you need to replace your entire infrastructure or move your application to a new machine. It is also difficult to maintain a single device when it runs out of storage space or is experiencing slow performance. Lastly, it can be expensive to add more hardware to your system.


Scalability is an important component of cloud computing. Demand for online services can vary dramatically based on several factors (sometimes uncontrollable). If your organization cannot meet these demand fluctuations, it can lose customers to a competitor. This can be especially problematic when you have a global audience.

One way to ensure that your cloud services are scalable is to use horizontal scaling. This technique involves adding new servers to your system as required.

Horizontal cloud scaling is a cost-effective and efficient method of enhancing your system’s capacity for processing, data storage, and networking capabilities. It is particularly useful for organizations that require high availability and near-zero downtime.

In this strategy, administrators divide tasks across multiple servers in the network using patterns like MapReduce and Tuple Spaces. They also deploy parallel processing to ensure that all requests are handled efficiently and quickly.

There are many advantages to this approach, including its ability to slash maintenance costs and minimize complexity. In addition, it can provide a better experience for end users.

However, it is not without its drawbacks. Horizontal scaling is often more complex to maintain than vertical scaling and requires more maintenance resources. Additionally, it can lead to compatibility issues, sub-optimal scaling, and unnecessarily high costs.

Another drawback is that it can only go so far before the capacity of your systems begins to exceed the capacity of your current infrastructure. This could result in your existing systems becoming outdated and no longer performing as expected.

This can cause problems in your business and affect the quality of your services. It is essential to plan for your capacity needs based on industry information, past trends, and future projections.

For example, if you are an eCommerce company that serves a global customer base, you will need to cater to local regulatory compliance requirements. The same is true for banking and insurance services.

In any case, scalability is a key factor to consider when you decide whether to move your business to the cloud or not. With proper planning and a dedicated team, you can ensure that your business has the bandwidth it needs to thrive in today’s competitive environment.

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