8 Pros and Cons of Aurora Serverless for Cloud Native Apps

In today’s fast-paced data-driven world, businesses of all sizes are focused on scaling their operations to meet evolving demands. Auto-scaling is the primary solution for serving fluctuating traffic while keeping costs as low as possible. 

As the leading provider in the global cloud market, with an impressive share of 32% as of 2022 and the recent announcement of Aurora I/O optimized in 2023, Amazon affords customers a wide selection of cost-saving solutions.

So what is Aurora Serverless all about? If you are new to Amazon Aurora and this Serverless configuration, it’s critical to understand its best use cases and whether they suit your goals. This article reviews the benefits and downsides of Aurora Serverless and I/O optimized, enabling you to make the best decision when choosing an auto-scaling solution.

An Overview of Amazon Aurora Serverless

Amazon Aurora is a proprietary relational database developed by Amazon to meet the growing demand for auto-scaling database architecture by cloud-native or cloud-hybrid businesses. Aurora offers a low-configuration solution that scales based on traffic and demand and is compatible with MySQL and PostgreSQL.

As of April 2022, Amazon Aurora Serverless v2 is available to the public, offering a more granular and efficient auto-scaling solution with a wide range of added features such as multi-AZ (Availability Zone) support and the ability to scale down 15 times faster than in version 1. It’s worth noting that one version doesn’t replace the other, so you can use both side by side. 

I/O Optimized is another version of Aurora with a higher base cost but unaffected by heavy I/O workloads, making for an appealing solution under the right circumstances.

Components of Amazon Aurora Serverless

Firstly, there’s the storage layer, which duplicates data across multiple availability zones. This layer has a scalable storage capacity, ranging from 10 GiB to 64 TiB, and its I/O throughput can scale impressively.

Secondly, the compute layer can automatically scale vertically anywhere from 1 ACU (ACU stands for Aurora Capacity Unit, approximately equivalent to 1 vCPU and 2 GiB of memory) to 256 ACU (roughly equal to 64 vCPU and 488 GiB of memory) based on the current workload demands. The compute layer also has the capability to scale down to a complete stop.

This dynamically scalable compute layer introduces a revolutionary approach for unpredictable workloads or situations where the database goes extended periods without queries – making it ideal for DevOps automation.

Aurora Serverless Pricing 

The pricing structure for Aurora is quite complex, but the essential thing to note is the difference between serverless and provisioning. When considering the costs of Aurora Serverless v2, the minimum price is twice that of provisioned Aurora, setting the baseline monthly cost at approximately $350, considering its minimum capacity of 4 ACUs. Despite this higher upfront cost, the dynamic auto-scaling feature of Aurora Serverless v2 provides cost-effectiveness for applications with fluctuating workloads. 

According to Amazon, I/O optimization can save up to 40% on I/O-intensive applications, with 25% of the costs due to I/O operations. AWS may reduce these costs in the future. Yet, a minimum price will likely persist due to the inherent challenges related to cold start issues in process-based architectures. 

What does the new Aurora version mean?

In the blog post announcing the new version, AWS acknowledged that the new version is a more expensive product than the standard Aurora database. The cost-saving value provided by this update depends on the type of workload. AWS’s Principal Developer Advocate, Channy Yun, states: “You can now confidently predict costs for your most I/O-intensive workloads, with up to 40 percent cost savings when your I/O spend exceeds 25 percent of your current Aurora database spend. If you use Reserved Instances, you will see even greater cost savings.” 

The devil will be in the details of your particular workload requirements. Ray Wang, founder and principal analyst at Constellation Research, said it’s a win for customers with big workloads. “Normally, every time you read data that’s not cached and then write data back to your MySQL or Postgres data, you incur an I/O charge,” he stated. “This is designed to drop your pricing because they have found a more efficient way internally to handle this, and they’ve passed on the cost savings to customers as we enter an age of AI.”

In summary, the new Aurora can reduce storage costs, but to know if it is right for you, you need to understand the underlying pros and cons of AWS Aurora.

8 Pros and Cons of Aurora Serverless

Let’s review the benefits and drawbacks of Aurora Serverless and how you may mitigate some of the downsides.

The 4 Pros of Aurora Serverless

1. Improved performance

Amazon claims that Aurora Serverless delivers up to five times the throughput of standard MySQL and up to three times the throughput of standard PostgreSQL. However, Amazon also notes that you are responsible for query optimization to ensure the full benefit of better performance.

2. Simplified management

Aurora Serverless’s ability to auto-scale in response to application demand eliminates manual intervention to adjust capacity, significantly simplifying database management and freeing developers from capacity planning and infrastructure management.

In addition, Aurora Serverless provides a highly available and durable storage infrastructure, replicating data across multiple Availability Zones by default. This automatic replication enhances data security and reduces the need for manual backup management. 

3. Cost-effectiveness

At the forethought of every business is cost saving, and database costs are a significant money sink if not carefully managed. When used correctly, Aurora Serverless has the potential to reduce operational costs significantly. The key to cost-saving is scaling to a minimum when not in use.

In this way, Aurora Serverless is best suited for databases with high variability in access requirements. A prime example may be a test database only used during work hours, or a database accessed primarily by an audience in a narrow time zone.

4. High availability

Sensitive and high-value data are common assets that must be secure and available on demand. Aurora safeguards your data by storing copies across multiple Availability Zones within a single AWS Region. Whenever data is written to the primary DB instance, it’s synchronously replicated to six associated storage nodes, irrespective of their zone locations. This process ensures data redundancy, mitigates I/O freezes, and reduces latency during system backups.

Control Plane can help safeguard against service interruptions by serving the nearest healthy cluster to each request. Furthermore, Control Plane’s multi-cloud support reduces reliance on a single provider, further bolstering your workload’s survivability.

The 4 Cons of Aurora Serverless

1. Cost for steady workloads

The flip side of the scalability of Aurora Serverless is the steep costs of steady workloads. You will find that when the need for a steady stream of data arises, a scalable solution will cost you twice as much as provisioned Aurora. Continuously monitoring storage needs to prevent sinking your capital into the wrong tool. This is the same approach for computing resources.

2. Read operations

The auto-scaling of Aurora Serverless is cost-effective for dynamic data size and computing requirements but not for read-heavy operations. It makes sense if the data is relatively constant, then read operations are primarily data traffic, and Aurora Serverless doesn’t scale data transfers in any meaningfully different way.

3. Lack of control 

The downside of an easy-to-configure and ready-to-use solution like Aurora Serverless is the lack of control. You will not have control over when and how Aurora scales your resources, which means the way it operates may not meet your requirements.

A Scale Up operation is triggered if there have been at least three minutes since the last scaling event and the CPU usage is above 70% or the connections exceed 90%. A Scale Down operation occurs if at least fifteen minutes have passed since the previous scaling event, CPU usage drops below 30%, or connections fall under 40%.

Control Plane can give you enhanced control over auto-scaling operations to meet your applications’ requirements.

4. Potential high latency

One potential downside of Aurora Serverless is the possibility of high latency in the event of cold starts and scaling operations:

Cold starts mean that a paused database needs to start up to handle a new request. The startup process can cause a delay or high latency, impacting performance. However, AWS has continuously improved this aspect in Aurora Serverless v2.

Scaling operations may cause high latency when Aurora Serverless must scale up to handle the increased load. The process of new resources being allocated and brought online causes brief interruptions in existing connections.

Uses cases and best practices when using Aurora Serverless for Cloud Native Apps

Aurora Serverless is primarily suitable when the scaling is unknown. Some everyday use cases include:

• eCommerce – Traffic to digital storefronts may have high spikes during sales or when an exclusive item becomes available.
• New Applications – During the initial phases of application development, you may not clearly understand your requirements.
• Testing Environments – Normally accessed during work hours, testing environments don’t need 100% uptime. The ability to scale down during off hours can be a cost saver.

Even if you can anticipate spikes in traffic, a scalable solution may still offer the best pricing structure for your needs.

Scalability is for variability

Aurora Serverless provides a cost-effective solution for dynamic environments. You must be aware of the platform’s limitations regarding high latency issues and the cost of steady workloads, but if the task is right, Aurora Serverless will save you time and money. The ease of use and shutoff functionality is a boon for any service with varying requirements.

To get the full benefit of an auto-scaling operation, consider the Control Plane as a flexible solution that reduces costs and increases availability while mitigating latency issues. Plus, you are not restricted to AWS services and can mix and match resources from GCP and Azure, too – ensuring you get the best out of all the cloud worlds.

Our team will happily demonstrate how Control Plane can help you or provide advice on scaling your cloud environment.