What is AWS Cost Optimization?
AWS cost optimization is the practice of reducing unnecessary cloud spend by identifying idle, over-provisioned, or misconfigured AWS resources – prioritized by security and business context.
Does your AWS invoice feel like an unsolvable mystery? You're not alone. Cloud services deliver immense capabilities and scalability, but there’s often a hidden difficulty: managing ever-growing spend. When the monthly bill is higher than you budgeted for, it can kick off a panicked search to find the source of the overspending. This constant fire-fighting is not just stressful; it's also a highly inefficient way to manage your cloud expenses. Fortunately, there's a superior strategy—proactive AWS cost management.
Think of AWS cost optimization as an ongoing habit of trimming your cloud bill by using resources smarter, picking the right-sized services, and cutting out wasteful spending. For this to really work, it can't just be one team's job or something you only think about when you're over budget. It needs to be a core part of how your engineers work every single day, and it needs to incorporate three key ideas: having a clear view of your usage, deeply understanding your infrastructure's context and security, and using intelligent automation to find and fix waste on a large scale.
Ready to learn everything necessary to transform AWS cost management from a headache into a real business advantage? You’ll find everything you need in this guide.
First, let’s take a closer look at why cost optimization matters.
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The importance of AWS cost optimization
The massive growth in global public cloud spending shows why managing costs has never been more important. Forecasts indicate that cloud spending is expected to surge to $723.4 billion by the end of 2025, representing a substantial increase from the $595.7 billion spent in 2024. As your company leans more on the cloud, you can bet your bills will grow right along with it.
Other data paints a clear picture of the struggle to optimize cloud spending. A Gartner survey found that on average, companies are wasting 35% of their cloud budgets. In well-run environments, this number may be as low as 15%, but it can increase an incredible 55% in companies that lack a formal optimization strategy. This waste typically stems from a handful of common issues that can be addressed one by one:
Over-provisioned resources balloon your bill when you assign more computing power or storage—like bigger EC2 instances or more EBS throughput—than an application really uses.
Idle or "zombie" infrastructure are resources that are on and costing you money but aren't handling any traffic or doing any real work, like EBS volumes that aren't attached to anything or Elastic IPs you're not using.
Suboptimal pricing models can be a massive source of waste (for example, sticking with on-demand prices for steady, predictable work instead of taking advantage of cheaper, discounted models).
Orphaned environments are dev, test, or staging setups that were built for a project and were simply forgotten about after it ended.
Fundamental principles of AWS cost optimization
There's no single silver bullet for effective and lasting AWS cost optimization. Instead, it's all about consistently applying a set of engineering practices that, when used by everyone, lead to a cycle of constant improvement:
Visibility
You can't start optimizing your costs until you know where the money is going. It all begins with getting a complete picture of your entire AWS setup. A crucial first move is to implement a standard tagging system for everything you deploy and to assign clear owners, which makes it much simpler to track costs and keep teams accountable.
Right-sizing
Over-provisioning is one of the most common reasons for cloud waste. The idea behind right-sizing your computing and storage is to assess how your resources are being utilized and then adjust their capacity to meet those real-world demands. This involves selecting the correct instance types, choosing the appropriate storage tiers, and monitoring performance to make informed adjustments.
Automated cleanups
Idle resources, often referred to as zombie infrastructure, are another significant reason for unnecessary cloud spending. The trick is to identify and eliminate this idle equipment methodically. This involves deleting unattached EBS volumes, releasing unused Elastic IPs, and shutting down entire development or staging environments that are no longer in use.
Picking the right pricing model
AWS offers several pricing models that can provide significant discounts over the standard on-demand rates. To truly reduce your costs, select the appropriate pricing models for specific jobs. For work that is stable and predictable, utilizing Spot Instances, Reserved Instances (RIs), or Savings Plans can result in significant savings.
Shifting left
Finally, to make cost optimization a real part of your engineering culture, you must shift left and address it earlier in the process. This involves building cost awareness right into the way you develop and deploy software. By adding cost checks to your CI/CD pipelines and leveraging infrastructure as code (IaC), you can identify potential budget overruns before they reach production.
Best practices for optimizing AWS services
While the main ideas of cost optimization are the same everywhere, the actual methods you'll use will change based on which AWS services you count on. Here's a look at some of the best ways to optimize a few key AWS services:
EC2: The workhorse of AWS
For many companies, Amazon EC2 instances account for the most significant portion of their AWS bill, so follow these strategies for big savings:
Right-sizing is a great starting point, and AWS Cost Explorer provides solid recommendations about instances that are too large for their tasks.
After right-sizing, use a smart combination of Spot, Reserved, and On-Demand instances.
For any non-production setups, you can save a significant amount by setting up automatic start/stop times, so instances are only on when needed.
EBS: Persistent storage for your instances
Amazon Elastic Block Store (EBS) provides your EC2 instances with persistent storage, but these volumes can easily become a source of unexpected costs. Here’s how to keep them in check:
Switch your volumes to cheaper types, such as moving from gp2 to gp3. (This is an easy yet powerful tweak!)
Get into the habit of deleting EBS volumes that aren't attached to anything.
To automatically manage your EBS snapshots, set up snapshot lifecycle policies with the Amazon Data Lifecycle Manager.
Lambda: Serverless computing
AWS Lambda lets you execute code without setting up servers, but you can still find ways to optimize:
Focus on fine-tuning memory allocation and optimizing the runtime of your code.
While provisioned concurrency can reduce "cold starts," use it carefully because it incurs costs even when your function is inactive.
Boost performance and lower costs by making your deployment packages smaller and reusing execution contexts.
RDS: Managed relational databases
For your relational database requirements, Amazon RDS provides a managed service that makes operations much simpler. Follow these tips to keep that convenience cost‑efficient:
Think about using Amazon Aurora Serverless for workloads that are sporadic or unpredictable.
For databases that aren’t used in production, you can set up off-hours scaling to reduce costs when they're not in use.
S3: Scalable object storage
Amazon S3 provides incredibly stable and scalable storage, but as your data piles up, your costs will, too. To keep storage costs in check…
Turn on S3 lifecycle policies that move cold data into cheaper tiers—S3 Infrequent Access or S3 Glacier—long before it fattens your bill.
If you have data that's accessed unpredictably, S3 Intelligent-Tiering can automatically optimize your costs for you.
EKS: Managed Kubernetes service
Keeping your Amazon EKS cluster costs under control is vital, particularly as your container-based applications grow:
Right-size your node groups with a tool like AWS Compute Optimizer.
Find and fix clusters that are on extended support tiers since those can come with extra fees.
For your dev and test environments, schedule or scale down idle clusters after work hours.
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Operationalizing cost optimization across your teams
Make cost accountability part of every team’s workflow – not just finance’s.
Cost optimization isn't a job for one team. It’s a shared responsibility that spans engineering, platform, finance, and security teams. The most effective organizations embrace FinOps – a cross-functional operating model that promotes cloud cost visibility, accountability, and continuous improvement across the business.
To put FinOps into practice in AWS, adopt these habits:
Tag for ownership and context. Enforce tagging standards (e.g., team, env, project) at the point of provisioning to ensure that every resource is trackable and traceable.
Assign cost owners. Tie cloud spend to accountable individuals or teams. This enables direct follow-up and makes cost remediation fast and effective.
Enable showback or chargeback reporting. Let teams see their own cloud consumption in real time. Visibility into spend patterns encourages better day-to-day decisions.
Integrate cost signals into engineering workflows. Use IaC policies and CI/CD pipeline checks to flag inefficient configurations before they hit production.
Automate anomaly detection. Set real-time alerts for unusual spikes in cost or usage at the service, region, or tag level to enable faster triage.
Make cost a product decision. Build a culture where engineering teams treat cost as a product KPI – balancing performance, resilience, and spend in every release.
By integrating cost governance into every stage of the cloud lifecycle – from provisioning to deployment to decommissioning – you enable teams to take action quickly and confidently. It’s not just about cutting costs – it’s about making smart tradeoffs that support innovation while staying within budget.
Tools for AWS cost optimization
Finding your way through the maze of AWS cost optimization is much easier with the right tools. Luckily, AWS gives you a set of its own tools to get you going, and there's a growing market of third-party platforms that can give you even better insights and automation, leading to bigger cloud savings.
AWS native tools
| Tool | Primary function |
|---|---|
| AWS Cost Explorer | Lets you see, understand, and control your AWS costs and usage over a period of time; also suggests how to right-size your EC2 instances |
| AWS Compute Optimizer | Uses machine learning to look at your resource usage and suggest ways to right-size your EC2 instances, EBS volumes, and Lambda functions |
| AWS Trusted Advisor | Performs automated best‑practice audits, spotlighting idle or under‑utilized resources so you can trim unnecessary spend |
| AWS Budgets | Lets you create your own budgets to monitor costs and usage; sends you alerts if you go over your set limits |
| Savings Plans recommendations | Helps you find ways to save money by letting you commit to a certain level of usage in return for a lower price |
Engineering-led platforms
While the native AWS tools are a good place to start, platforms from other companies can often give you a fuller, more context-rich picture. These platforms layer precise cost insights onto your security and resource information, making it easier to spot and fix budget leaks quickly—and safely. This gives you a single view where you can see not only that a cluster is old but also the security dangers that come with it.
Wiz: Bringing context to AWS cost optimization
Raw cost figures by themselves don't tell you how to optimize AWS. A list of underused instances shows you the problem but not the reason it's happening or what effects fixing it might have. To go from basic reports to smart and safe optimization, you have to understand context. This is where graph-based tools really excel because they map out every resource, connection, and policy to give you a complete picture of your environment.
Armed with full context, you can trace cost spikes back to their source, like a security mistake that's causing surprise data transfer charges. Context also helps clarify who owns which service, assigning costs directly to the teams that are actually responsible.
When it comes to context and correlation, Wiz leads the way.
One stand-out feature? The Wiz Security Graph. It connects cost information with security threats and business ownership, letting you set your priorities based on both potential savings and risk reduction so that you can fix issues with confidence.
Looking to optimize your AWS environment with a full understanding of your security and infrastructure? Schedule a demo of Wiz to see how easy it can be to add context to your cloud cost-saving efforts!
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