Several factors, including price increases, a rush to adopt AI, inflation and ongoing digital modernization efforts, are driving IT leaders to revisit cloud costs. According to a recent survey of IT professionals, three out of every five organizations have seen an increase in cloud spending in the past year, with nearly four in 10, who experienced price hikes, saying their costs jumped by more than 25%.
Edward Viaene, Co-Founder of IN4IT, looks at how cloud costs can be lowered and cloud tooling be delivered at a lower cost.
For businesses running on Amazon Web Services (AWS), it is easy to get caught off guard by the escalating cloud costs. One minute you are taking advantage of AWS’s incredible scalability and diverse service offerings, and the next you are facing an unexpectedly high bill.
Be it a startup, a small business or an enterprise, controlling AWS costs is critical to maintaining the bottom line. In this blog, we will explore a few practical strategies that help you reduce AWS expenses while keeping your infrastructure efficient.
When you first sign up for AWS, you might receive credits through a startup accelerator or during a proof-of-concept (POC) phase. During this time, you are probably not paying close attention to the costs as those credits are covering everything. But as soon as the credits run out, reality hits hard — suddenly, what seemed like ‘free’ services have ballooned into a significant monthly expense.
It is common for businesses to overlook monitoring their AWS usage early on, only to find out later that they have been running services that are more expensive than anticipated. This is particularly true for services like Amazon Aurora, Redshift or serverless offerings that may be overkill for smaller companies or startups.
Monitor Your Cloud Costs
The first step in reducing your AWS bill is to monitor your costs from the beginning. Even if you are using credits, AWS provides cost dashboards that allow you to see how much each service is costing you in real-time. This visibility is crucial for identifying potential overspending during the early stages.
For example, some businesses opt for Amazon Aurora because it is often recommended as a top-tier relational database service (RDS). However, Aurora is designed for large-scale, high-performance needs, which may not be necessary for a smaller operation. Instead, Amazon RDS with PostgreSQL could be a much more cost-effective option, especially for those who do not need the full capabilities of Aurora.
Another common pitfall is choosing AWS services that are more suited for enterprise-level operations when a smaller, more affordable option would suffice.
One area where this frequently happens is with serverless technologies. While serverless databases like Aurora Serverless are powerful and scalable, they can be more expensive than traditional instance-based databases for most workloads.
If you are running a database with consistent demand, it might be more cost-effective to use a small instance rather than relying on serverless architecture, which charges per request.
Additionally, Redshift is a popular data warehousing service, but it is not always the best fit for startups. Unless your company is heavily focused on data analytics and can afford the high costs associated with Redshift, it may be wise to explore more economical data storage options like Amazon S3 or RDS.
Once you have a better understanding of which services you need, consider leveraging AWS Reserved Instances (RIs) or Savings Plans to lock in lower prices.
- RIs allow you to reserve EC2 instances for a one- or three-year period, providing significant discounts compared to on-demand pricing. These are especially useful if you know exactly what instance types you would be using for a long period.
- Savings Plans, on the other hand, offer more flexibility by allowing you to commit to a minimum amount of compute spend per hour without being tied to a specific instance type or region. This is a great option if you are still scaling or testing different services but want to reduce costs in the meantime.
The key is to review your current usage and forecast your future needs. If you are not ready to commit to a specific instance family, a Savings Plan gives you more wiggle room while still saving money.
Databases are often one of the biggest contributors to AWS bills, but there are ways to optimize them. For example, Amazon DynamoDB offers two pricing models: On-demand and provisioned capacity. On-demand pricing is convenient for workloads with unpredictable traffic, but it is far more expensive than provisioned capacity if your workload has consistent demand. By switching to provisioned capacity and reserving capacity for one or more years, you can significantly reduce costs.
Additionally, RDS instances running on ARM64 architecture can save you money. ARM-based instances are cheaper to run as compared to x86-based instances and they often provide better performance too. If your application is already running on a MacBook or other ARM-based machine during development, switching to ARM instances in production should be seamless.
One of the most common mistakes businesses make is duplicating services, which unnecessarily drives up costs. For instance, many organizations use both AWS CloudWatch and third-party tools like Datadog for logging and monitoring. While Datadog offers a user-friendly interface, it comes at a higher cost, especially when you are already paying for a similar functionality in CloudWatch. If you need Datadog or another third-party tool, consider sending logs and metrics directly to that service instead of duplicating them in CloudWatch. This approach can help reduce costs while still giving you access to the features you need.
AWS offers a wide range of services designed for different scales of operation. As a startup or smaller company, you do not need to immediately jump to enterprise-level solutions like Kubernetes or Amazon SageMaker. Kubernetes offers flexibility and control, but it can quickly become expensive when you factor in the costs of managing multiple nodes, load balancers and data storage.
Instead, services like AWS Fargate or elastic container service (ECS) provide managed container solutions that are easier to maintain and cheaper to run at smaller scales. Similarly, while SageMaker is great for large-scale machine learning (ML) models, you can often build and train models locally using a smaller instance and move to a managed service when necessary.
Review Your Architecture
Finally, one of the most important ways to keep your AWS costs down is to regularly review your architecture. As your business grows, your AWS infrastructure will evolve and once essential services may no longer be necessary. It is easy to let unused resources pile up, leading to higher bills. Consider having an external architect review your infrastructure periodically. AWS offers free architectural reviews through programs like the Well-Architected Framework, which can help identify areas where you can reduce costs or improve performance.
Reducing AWS costs does not require a complete overhaul of your infrastructure. Many cost-saving strategies are about simplifying what you already have. By choosing the right services, leveraging savings plans and RIs, avoiding service duplication and regularly reviewing your architecture, you can significantly lower AWS expenses while maintaining the performance and reliability of the business.
For DevOps teams, keeping AWS costs under control is about being mindful of the services you use and making sure you only pay for what you need. By making a few simple adjustments, you can reduce cloud expenses and free up resources to focus on business growth.