Cloud Cost Optimization: 5 Impactful Tactics For 2025

The elasticity of cloud resources is both a blessing and a curse. You can experiment with new ideas without worrying about getting another rack of servers, but there’s a price to pay for this comfort. Effective cloud cost optimization is essential to ensure that this flexibility does not lead to uncontrolled expenses.

Overprovisioning and cloud sprawl are real problems. Cloud cost increases make the FinOps team’s eye twitch at the end of the quarter, no matter the company size. A report from Gartner revealed that most IT leaders exceeded cloud budgets in 2023.

The only way to deal with the long-term cost implications of your cloud environment is by implementing cloud cost optimization measures. And if you don’t want optimization to become a drag on your engineering team, automating it is the only move that gets you there.

Why is cost optimization so tricky in the cloud? Let’s start with the most common hurdles teams encounter.

Biggest challenges of cloud cost optimization

Teams face several challenges when managing cloud costs:

• Lack of visibility into cloud costs – tracking cloud spend grows harder as adoption increases within the company. In an ideal world, finance teams can trace cloud costs to specific initiatives, teams, or projects.
• Cost attribution issues – it’s not uncommon for engineers to buy more capacity than necessary just to sleep well at night. But before you know it, an extra expense here and there snowballs into massive cloud waste. 
• Unexpected expenses – without guardrails to control and govern cloud spending, the autonomy around provisioning cloud resources can result in unexpected or unexplainable cost anomalies. 
• Lack of cost oversight – working across functions (technical, product, and business) without a centralized view of the actual spend makes understanding cost and usage trends very difficult. 
• Inaccurate forecasting – companies looking to save up on operating costs with committed use discounts (reserved instances and savings plans in AWS) often try to forecast their cloud resource requirements.

5 cloud cost optimization tactics for 2025

1. Understand your cloud bill

If you look at your cloud bill, you’re likely to get lost. 

Bills are long, complex, and hard to unpack because every service has a defined billing metric. Understanding your usage to the point where you can decide confidently is next to impossible.

And we’re talking about analyzing costs for only one cloud and one team. Try billing for multiple teams or clouds!

This is where cost allocation comes in and reveals who is using which resources. How else can you make anyone accountable for these costs? Cost allocation is especially challenging in dynamic infrastructures running on Kubernetes.

Why is it worth examining and allocating costs based on your cloud bill? Because it’s a treasure trove of data that will help you forecast your requirements better and secure the right amount of resources (and avoid the curse of overprovisioning!)

But estimating your future resource demands is no small feat. Here’s an example sequence you may follow:

1. Gain visibility and analyze your reports to identify any usage patterns in spending.
2. Detect peak resource usage scenarios with the help of periodic analytics and crunching your historical usage data.
3. Consider seasonal customer demand patterns and check if they correlate with peak resource usage. If you see that, identifying them in advance might become easier.
4. Monitor resource usage reports regularly and set up alerts to keep cloud costs in check.
5. Create an application-level cost plan by measuring application or workload-specific costs. This will also allow you to calculate the total cost of ownership of your cloud infrastructure. 
6. Examine your cloud providers’ pricing models and plan capacity requirements over time. Putting all of this data in one place makes understanding your costs easier.

The tasks listed above aren’t one-off jobs. You need to do that regularly to get results. 

2. Rightsize your cloud resources

Rightsize compute instances

Choosing the right virtual machine can be a huge game-changer if your application relies on compute. But AWS has 500+ different compute instances. Similar instance types deliver different performance across cloud providers;and even in the same cloud, a more expensive instance doesn’t equal higher performance.

Here are the steps you can take to pick the best instance for your workload:

Define your minimum requirements 

Make sure to do it across all compute dimensions, including CPU (architecture, count, choice of processor), Memory, SSD, and network connectivity. 

Select the right instance type 

You can choose from various combinations of CPU, memory, storage, and networking capacities that come packaged in instance types optimized for one such capability. 

Set the size of your instance 

Remember that the instance should have enough capacity to accommodate your workload’s requirements and include options like bursting if necessary.

Examine different pricing models 

The three major cloud providers offer different rates: on-demand (pay-as-you-go), reserved capacity, Spot instances, and dedicated hosts. Each of these options has its advantages and drawbacks. 

Rightsize Kubernetes requests and limits

If you run your application on Kubernetes, another excellent method for lowering your cluster costs is to define Pod requests and limits. They allow you to establish memory and CPU requirements so that cluster containers are aware of their constraints.

By consistently bin-packing Pods to shrink your cluster to the bare minimum of nodes, you may maximize the savings from your AKS cluster using an automated solution. Here’s how removing a node from the cluster once it is empty works.

3. Achieve greater savings with Spot instances

It’s smart to buy idle capacity from AWS and other large cloud service providers because Spot instances are up to 90% cheaper than on-demand ones. However, there is a catch: the vendor reserves the right to reclaim these resources at any moment. You need to ensure your application is prepared for that before jumping on the spot bandwagon.

Here’s how to use spot instances:

Check if your workload is Spot-ready Your Workload Is Spot-Ready

Can it withstand interruptions? How long will it take to complete the job? Is this a mission-critical workload? These and other questions help qualify a workload for Spot instances.

Examine the services of your cloud provider

It’s a good idea to look at less popular instances because they’re less likely to be interrupted and can operate for more extended periods of time. Check the frequency of interruption of an instance before settling on it.

Bid on Spot instances

Set the highest amount you’re prepared to pay for your chosen Spot instance. Note that it will only run if the market price meets your offer (or is lower). The rule of thumb here is to set the maximum price at the level of on-demand pricing.

Manage Spot instances in groups

That way, you can request numerous instance types simultaneously, increasing your chances of landing a Spot instance.

To make all of the above work well, prepare to spend a lot of time on configuration, setup, and maintenance tasks (unless you decide to automate it).

Want to learn more about Spot instances? Here’s a complete guide: Spot instances: How to reduce AWS, Azure, and GCP costs by 90%

4. Autoscale resources to match demand

Autoscaling is a great cloud cost optimization strategy for dynamic cloud-native technologies like Kubernetes. The tighter your scaling mechanisms are configured, the lower the waste in running the application (and the more valuable your cloud financial management efforts are!) 

Real-time autoscaling solutions use business metrics to generate the optimal number of required instances. They can scale up, down, or zero if there’s no more work. The mechanism ensures that resources always match the application’s requirements in real-time. 

Look at this graph, which shows the difference between resources requested vs. provisioned (CPU and memory). Notice how it shrank once the team used the CAST AI autoscaling feature (marked “Start of optimization”):

5. Pick the right tool for the job

To gain control over their cloud expenses, companies apply various cost management and optimization strategies and solutions in tandem:

1. Cost visibility and allocation – using a variety of cost allocation, monitoring, and reporting tools, you can figure out where the expenses are coming from and even do a break-even analysis. Real-time cost monitoring is especially useful here since it instantly alerts you when you exceed a set threshold.
2. Cost budgeting and forecasting – you can estimate how many resources your teams will need and plan your cloud budget if you crunch enough historical data and have a fair idea of your future requirements. Sounds simple? It’s anything but – Pinterest’s story shows that really well.
3. Legacy cloud cost management solutions – this is where you combine all of the information you got in the first two points to create a complete picture of your cloud spend and discover potential candidates for improvement. Many solutions on the market can assist with that. But most of the time, they give you static recommendations for engineers to implement manually.
4. Automated cloud-native cost optimization – this is the most powerful solution for reducing cloud costs you can use. This type of optimization doesn’t require any extra work from teams and results in round-the-clock savings of 50% and more, even if you’ve been doing a great job optimizing manually. A fully autonomous and automated solution that can react quickly to changes in resource demand or pricing is the best approach here. 

Wrap up

Poor resource utilization causes an average cloud overspend of 30%. Imagine how many precious cycles would need to be spent getting that 30% closer to 0%. 

Should we continue to rely on software engineers to perform all the management and optimization tasks manually? Not with so many automation options available!
CAST AI is here to help you. Check out how we reduced Amazon EKS costs by 66% for one of our clusters to see how automation helps cut costs without impacting performance or availability.