Scalable IT infrastructure is an important feature offered by cloud services for small and medium businesses. Using the cloud, your business can upscale or downscale your resources on demand, while controlling unnecessary costs.

Scalable data infrastructure is particularly beneficial for businesses dealing with seasonal or cyclical demand. Retail companies, for example, can easily manage server demand during the holidays.

Below, Dev.Pro’s experts weigh in on best practices for scalability in cloud computing and how these approaches benefit consumers.

How Is Cloud Scalability Helpful to You as a Consumer?

A scalable cloud computing infrastructure allows companies to quickly adjust their use of on-demand servers, depending on the number of users and transactions they need to accommodate.

However, not all companies use cloud services to their full potential. 

For instance, you can use a pay-as-you-go model to minimize your peak loading costs. When your product experiences loading changes, peaks during promo campaigns or goes overcapacity during the nighttime, your cloud pricing model can adapt accordingly. At the same time, the scalability of the even traffic can be served with more affordable fixed models that fit better for such cases.

The sections below highlight different situations and options to serve them in more affordable and sustainable ways.

Best Practice #1: Cloud Computing Scalability for REST API

Representational state transfer application programming interface (REST API), or call them API Gateway, allows you to design APIs with various scalability options, but without the need to manage servers. API Gateway can deal with traffic management and its extensibility. But is it better to use AWS Lambda or AWS Elastic Container Service (ECS)? Let’s dive deeper.

Cloud Computing Scalability with AWS Lambda

In a nutshell, AWS Lambda is a serverless cloud computing platform provided by Amazon Web Services and is an excellent solution to address scalability challenges for small, stateless API services. 

When to use:

  • Short running tasks (< 29 sec)
  • Small payloads (< 10 MB)
  • Limited parallel requests (< 1000)
Example of scaling implementation on a project using AWS Lambda

AWS Lambda cloud computing scalability benefits:

  • Minimizes costs with a pay-as-you-go model: this can save you up to 90% on cloud expenses.
  • Automates scalability: serverless solutions can adjust to sudden jumps in usage.
  • Accelerates iterative development: no need to use continuous delivery tools;  just send the code from the developer console.
  • Optimizes team workflow: automation scalability frees time for your developers to work on new features and decreases administrative costs. 

This solution is best used at the initial, low-traffic stage of the project, when fast setup is required.

Cloud Computing Scalability with AWS ECS

Amazon ECS enables AWS production workload. In addition to managing assignments like task placement, integration with the AWS platform and VPC networking, ECS provides users with auto-scaling features.

When to use:

  • Big payloads
  • Long-running tasks
  • Managed scaling 
  • Pay per EC2 usage
  • More complex app setup
Example of scaling implementation on a project using AWS ECS

Auto-scaling is powered by CloudWatch metrics available for ECS containers, such as central processing unit and memory usage. With AWS auto-scaling, you can automatically increase or reduce the task capacity of your ECS container. 

With CloudWatch metrics, you can handle an enormous volume of requests by adding additional tasks as needed or removing them when the volume decreases.

ECS therefore offers cloud scalability if you expect your project to deal with significant traffic and numerous requests. This solution is well suited for infinite scale and cost-efficiency.

Best Practice #2: Cloud Computing Scalability for Background Tasks

What are background tasks? Let’s consider the checkout process for a typical online store. When a user completes a transaction, this data is to be stored according to a traditional design pattern responsible for data integrity. After data processing, the user will either receive a message that the transaction was successful or that there was an error. 

Since multiple steps in the save-logic are executed on non-cloud platforms, the process will take some time due to a delay between the cloud and the back-end sync and a delay between a user and the cloud sync. To avoid these delays, your company may want to consider running these activities in the background and informing customers about the successful receiving of their requests.

To handle this task, you can use either AWS Simple Queue Service (SQS) and Lambda or AWS and ECS.

AWS SQS and Lambda

AWS SQS assures programmatic sending of messages via web apps and allows for separate microservices for pipeline optimization. 

SQS can be used as an event source for Lambda. The process is constructed as follows: a message is added to a queue, then a Lambda function is called on with an event that contains this message.

When to use:

  • Short running tasks (< 15 min)
  • Limited parallel requests (< 1000)
  • Configured execution concurrency
Example of scaling implementation on a project using AWS SQS and Lambda

SQS and Lambda are commonly used together  because it improves Lambda’s call and error functions. That’s because the default concurrency cap for function calls is 1,000. If a function reaches this limit, all further calls will fail with a throttling error. By adding SQS to the process, these errors are automatically retried based on set configurations. As a result, you can avoid failures connected with sudden spikes in use.

For low-traffic projects, use  SQS and Lambda together for small tasks in order to reduce costs and ensure a fast setup. 


If you have a service processing queue dealing with peak load moments, you may need to scale ECS. In this situation, handling additional simultaneous transactions helps process this queue faster. For this purpose, you need to use Simple Queue Service from Amazon to scale your pipeline.

When to use:

  • Long-running tasks
  • Managed scaling
  • Complex application setup
Example of scaling implementation on a project using AWS SQS and ECS

To scale your system following this best practice, you need to use CloudWatch metric alarms again. CloudWatch alarms will continuously monitor the number of items in the queue and trigger a warning if it exceeds the threshold you specified.

The SQS and ECS combination is a perfect match for large projects due to its cost efficiency for high traffic solutions.

Best Practice #3: Horizontal Cloud Computing Scalability with MongoDB 

Another option to guarantee scalability is to balance database load by distributing simultaneous client requests to various database servers. This approach reduces the burden on any specific server. By default, MongoDB can accommodate several client requests at the same time. In addition, MongoDB employs specific parallel management mechanisms and locking protocols to maintain data integrity at all times.

Horizontal scale in MongoDB splits your database into separate pieces and stores them on multiple servers. You can succeed with it by using sharding and replica sets.

MongoDB sharding provides additional options for load balancing across multiple servers called shards. In this way, each shard becomes an independent database, while the whole collection transforms into one logical database.

When to use:

  • Big and complex systems
  • Vertical scaling isn’t cost-efficient enough

With  horizontal scale-out, you have the ability to add several new servers directly at runtime. This will reduce downtime to zero, which will positively influence database performance.

Even though horizontal scaling is cheaper than vertical scaling, there are a few things to consider. It’s necessary to ensure an efficient key distribution between shards. This approach requires a more complex configuration and is also applicable to its use. 

What Approach Should You Adopt for Scaling Your Cloud Services?

There is no universal answer to this question, as it depends on your initial tasks, product size, load expectations, and approved budget.

If you’re not sure what solution you should choose or have any additional concerns, drop us a line. Our Cloud and DevOps experts have proven expertise on these projects and are here to guide you.


What Is Scaling in Cloud Computing?

Scaling in cloud computing is the ability to increase or decrease IT infrastructure in accordance with demand. It helps cope with unpredictable load peaks and scale your business while avoiding associated risks.

What Are the Major Benefits of Scalable Cloud Computing ?

The major benefits are time savings, cost reduction, and increased flexibility and speed. Since you don’t need to waste resources setting up physical hardware, you can organize your time more wisely.

How Is Cloud Scalability Helpful to You as a Consumer?

Scalability in cloud computing gives small and midsize businesses unlimited data storage capacity, adds processing power and provides the ability to avoid and tolerate failures during peak load periods. Cloud scalability helps your business stay agile and competitive.