How can service quotas on AWS become a barrier in scaling your application?

We live in a time where anyone can learn a few DevOps skills that enable them to provision infrastructure within minutes and scale applications with ease. Dynamic scaling is a pinnacle of cloud-native architecture and is critical to keeping applications highly available and reducing undesired latency.

This blog is written by Jeremy Rivera at KushoAI. We're building the fastest way to test your APIs. It's completely free and you can sign up here.

With great power though, comes an even greater responsibility. AWS stands as a pillar in the cloud computing ecosystem, holding almost a third of the market share for service providers. AWS enables any organization to adequately provision and scale their applications with relative ease on their platform.

Yet, despite its extensive capabilities, AWS's ability to scale your application's infrastructure is not without limits. Understanding these limitations is critical for engineers and organizations seeking to harness the full potential of the cloud while avoiding pitfalls.

The limits AWS imposes are under what they call service quotas and are defined by the provider as follows:"Quotas, also referred to as limits, are the maximum number of resources that you can create in an AWS account. AWS implements quotas to provide highly available and reliable service to all customers, and protect you from unintentional spend."

In other words, AWS imposes these limits a) to prevent one organization from eating into too many resources single-handedly &b) to protect an organization from scaling to the point where it leads to over consumption of resources and a very large bill.

[For those interested, see Apple's monthly AWS bill] 

How Service Quotas Can Become A Barrier to Scaling Applications

As applications grow and demand increases, service quotas can present significant challenges that hinder scaling efforts. Here are some of the key areas where these quotas can impact application performance and availability:

1. Resource Limits: Each AWS service has predefined limits on the resources it can provision. For instance, there are limits on the number of EC2 instances you can run in a region or the throughput of an S3 bucket. When these limits are reached, applications may experience failures or performance degradation, which can affect user experience.

2. IAM Roles and Policies: AWS Identity and Access Management (IAM) has its own limits, such as the number of IAM roles and policies you can create. If your application architecture requires more roles than permitted, it can impede the deployment of new services or applications that need specific permissions, thereby stalling development and growth.

3. Network Quotas: Quotas on networking components, such as Virtual Private Clouds (VPCs), subnets, and IP addresses, can restrict your ability to expand your network architecture. As your application scales, the inability to create new network resources can lead to bottlenecks and limit your overall capacity.

4. Service-Specific Quotas: Each AWS service has its unique quotas, which can vary widely. For example, API Gateway has limits on the number of requests per second. If an application experiences a sudden spike in traffic, it may be throttled, resulting in slow responses or downtime.

5. Scaling Challenges: Auto-scaling groups are designed to automatically adjust the number of instances in response to demand. However, if you reach a quota limit, new instances may not be launched, even in the face of high demand. This can lead to service outages or degraded performance during peak usage times.

6. Cost Implications: Scaling often involves requesting increases in service quotas. This process can take time, and delays in approval can hinder your ability to respond to increased demand. Additionally, scaling to higher service tiers may incur additional costs, impacting your budget.

Mitigating Service Quota Issues

To effectively manage and mitigate the challenges posed by service quotas, consider the following strategies:

1. Monitoring: Regularly monitor your service quotas using tools like AWS CloudWatch or the AWS Management Console. Keeping an eye on your limits allows you to anticipate potential issues before they impact your application.

2. Quota Increase Requests: AWS allows you to request increases in service quotas. It’s prudent to plan ahead and submit these requests before you hit your limits, especially during expected growth phases. This proactive approach can help ensure that you have the necessary resources when you need them.

3. Design for Limits: Architect your application with service limits in mind. Implement strategies like circuit breakers or fallback mechanisms to ensure that your application can gracefully handle situations where quotas are reached, minimizing the impact on users.

4. Use AWS Trusted Advisor: AWS Trusted Advisor is a valuable tool that can help identify areas where you are approaching service limits. It provides recommendations on best practices and actions to mitigate potential issues, allowing you to stay ahead of quota-related challenges.

Understanding the implication of service quotas and then implement strategies to monitor and manage them. Some of the above tools can provide assistance when determining the best path forward for your organization's applications. Requesting an increase for quotas are a solution but it would also be wise to see what is causing the application to quickly demand more resources. Perhaps there is unnecessary bloat or a way to optimize instance usage. Only with a dutiful eye and a respect for the infrastructure and prover can an organization truly take advantage of all that a cloud provider, such as AWS, can offer.

Though service quotas exist, they do in a large part to protect AWS's customers from unwanted fees and over-provisioning. As your organization continues to look forward and scale, view quotas as a friend, not a foe, and also try to manage the application's infrastructure to see if there are any pieces that need a refactor or further thought to truly determine if a request is 100% necessary.

This blog is written by Jeremy Rivera at KushoAI. We're building an AI agent that tests your APIs for you. Bring in API information and watch KushoAI turn it into fully functional and exhaustive test suites in minutes.