We know AWS gives the user 200+ services, but it also gives them an endless bill. This article tells you how to stop those bills.

For many organizations, the monthly AWS bill has become one of the largest and most unpredictable items in their master budget. It’s not like the organizations have a spending problem; it’s mainly the visibility problem.

In 2025, almost every company is part of the Gen AI race, and they are trying to stay ahead, launching faster, building smarter, and scaling bigger. But in the urgency to compete, many overlook their AWS costs. With 82% of enterprises already overshooting their cloud budgets, competing hard without cost control doesn’t make sense anymore, it’s unsustainable.

As Werner Vogels, CTO of Amazon, famously said, "Costs will spiral, all the time... unless you are actively managing them."

Over the last couple of months, We have looked at more than 40 different AWS cost audits. A few common threads appeared again and again. Organizations aren't lazy or careless; they're just falling into some very common traps that are easy to overlook when you're busy building things. Let’s get straight into it!

What Are Different AWS Pricing Models?

Choosing the right pricing model can be tough, it can also be the one lever which would reduce your AWS bill. One of the common mistakes is to leave the majority of workloads on the default On-Demand is the pay-as-you-go model. You only pay per second on compute capacity, and no long-term contracts or advance payments.

On-Demand Instances

On- Demand model, which also happens to be the most expensive. A good thought-out strategy would involve layering different models to match your own workloads and its design.

When to Use It:

• New or Unproven Application: On-Demand is generally a safer and more flexible option when you have no prior data to project usage.
• Spiky Workloads: When there is an irregular workload pattern with a high cost of interruption.
• Short-Term Development and Testing: It is good when you need to just test something out for a couple of hours or days.

The Mistake You Have to Avoid: Complacency. On-Demand is the simplest AWS tool, and that is why companies use it as a default for everything. Any workload that continuously runs over months on-Demand will not give you the best saving opportunities.

Savings Plans

Savings Plans (SPs) are a price structure that achieves high savings in return for a commitment to a fixed level of compute usage (measured in $/hour) over 1 or 3 years. They have become the usual preferred choice for most users.

• How They Work: You promise, e.g., to spend a dollar per hour on compute time. Any use below that $10/hour limit will then automatically incur discounted SP rates by AWS. Any activity on top of the commitment is charged at normal On-Demand rates.
• Compute Savings Plans (Most Flexible): These plans automatically apply to EC2 instance usage independent of instance family, size, OS, tenancy and AWS Region. They also apply to the use of AWS Fargate and AWS Lambda. It is most suited to dynamic environments where you know you will modernize, change instance types (e.g. between Intel and Graviton), or move workloads across regions.
• EC2 Instance Savings Plans (Less Flexible, Higher Savings): The plans have the most significant savings but are specific to a certain instance family in a selected AWS Region (e.g., m6g instances in us-east-1). They get implemented automatically at the various sizes of that instance family (e.g. at m6g.large through m6g.4xlarge). This works well with workloads that are consistent and will stay in a particular instance family.
• The 1 vs. 3 year option: With a 3-year plan, your savings are much greater, however, you are bound longer with it. The best plan is to roll over your absolute, rock-solid bottom usage (the amount you know you would spend in three years) in a 3-year plan. Then, cover your more variable but still predictable usage with more flexible 1-year plans.

Reserved Instances (RIs): The Legacy Commitment Option

Before Savings Plans, Reserved Instances were the primary way to get a discount. They still exist and can be useful in specific scenarios.

• Standard RIs: With these you can actually get the biggest discount but they come with a lot of limitations. You would have to bind yourself to a certain instance family, size, OS, and Region. Companies are not big fans of this because they cannot modify most of the settings. But it is still good for truly static legacy workloads where you’re sure nothing is about to change.
• Convertible RIs: This feature actually offers a decent discount, but you can switch the instance family, operating system, or other features, which gives your team a bit of flexibility.
• The RI Marketplace: You can sell your Standard RIs on the RI Marketplace to other AWS customers in case you need them. This may assist you in recovering expenditure on a promise you no longer have to make, although not always.
• Why Bother with RIs? In the vast majority of applications, RIs have been replaced by Savings Plans because these are more flexible. But Convertible RIs may also come in handy in cases where you have to reserve capacity in a particular Availability Zone, which Savings Plans cannot do.

Spot Instances

Spot Instances let you use spare AWS compute capacity for up to 90% off the On-Demand price. The catch is that AWS can reclaim this capacity with a two-minute warning.

• Making Spot Work: Spot is not for every workload. It is targeted at fault-tolerant, stateless and gracefully interruptable applications.
• Ideal Use Cases: Big data analytics, data pipelines, image streaming, CI/CD pipelines and certain AI/ML training jobs.
• Best Practices: EC2 Auto Scaling Groups or EC2 Fleet is used to scale a heterogeneous mix of purchase options and instance types. This diversification is needed as it eliminates the effect of any one type of Spot Instance being reclaimed. You can use the Spot Placement Score feature in the EC2 console to determine what pools have the least likelihood of being disrupted. You should always implement a checkpoint for long running jobs so that when an instance is killed, you can restart where you were.

The AWS Cost Management Tools

AWS provides a powerful suite of native tools to help you analyze, control, and optimize your costs. Mastering them is non-negotiable. We have divided them into

1. Foundational Visibility

AWS Cost Explorer:

This is your primary interface for analyzing your spending.

Deep Analysis: Go beyond the default view. Use the grouping and filtering features to break down costs by linked account, region, service, and, most importantly, cost allocation tags. Create saved reports for your key projects to review them quickly each month.

Projections: The in-built forecasting can be used to project your monthly expenditure. One of the downsides is that it hasn't been updated recently and may not be very useful at predicting sudden spikes due to new projects.

AWS Budgets:  

This takes you off the passive analysis onto the active control. 

• Types of Budgets: Not only do you set a total cost budget. You get to associate granular budgets with particular projects (via tags), particular services (such as EC2 data transfer), or particular accounts. Another type of budget you can make is Usage Budgets (e.g. alert when I use 10 TB of S3 storage), which can be configured to give an earlier signal than a cost budget. 
• Coverage and Utilization Budgets: Use to track your Savings Plans and RI commitments. A Utilization Budget warns you when you are underusing your commitment (e.g. alert when utilization gets below 90%), whereas a Coverage Budget warns you when too much of your usage is going into costly On-Demand rates (e.g. alert when coverage gets below 80%). 
• AWS Budgets Actions: This is an effective automation capability. You may also set an action to automatically run when a budget threshold has been reached. As an example, you can enforce a restrictive IAM policy or an SCP to block the launch of new resources, or even invoke a Lambda function to terminate certain non-critical resources. 

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1. Proactive Analysis and Recommendations 

AWS Compute Optimizer:  

This is a free machine learning-based tool that can analyze the use of your resources and offer right-sizing recommendations. 

• How it Works: It analyzes at least 30 hours of CloudWatch metrics on your resources to discover patterns. 
• What it Covers: It represents a recommendation of EC2 instances, Auto Scaling groups, EBS volumes, Lambda functions and Fargate services on ECS. 
• Actionable Findings: It will define the resources as Over-provisioned (wasting money), Under-provisioned (risking poor performance), or Optimized. More importantly, it offers you three different types of instances at your choice and demonstrates the approximate difference in price and performance. It also actively suggests the migration to cost-effective ARM-based Graviton instances where possible. 

AWS Trusted Advisor:  

It is a kind of automated consultant, which gives real time advice according to AWS best practices. Although it includes checks on security and performance, its cost optimization checks are priceless. 

Key Cost Checks: 

• Idle Load Balancers (free until about $20/month). 
• Unassociated Elastic IP Addresses (a low but unnecessary fee). 
• Poorly utilized Amazon EC2 Instances. 
• Idle Amazon RDS DB Instances. 
• Amazon Route 53 Latency Resource Record Sets recommendations. 

3. Real-time Monitoring and Alerting 

Amazon CloudWatch Billing Alarms:  

As detailed in the AWS documentation, this is your first priority against budget overruns. It allows you to monitor your estimated charges and get notified when they cross a threshold you define. 

Step-by-Step Setup: 

1. Navigate to the CloudWatch console. Ensure you are in the us-east-1 Region, as billing metric data is stored there. 
2. In the navigation pane, choose Alarms, then All alarms. 
3. Choose Create alarm, then Select metric. 
4. Under the Metrics section, choose the Billing namespace, then Total Estimated Charge. 
5. Select the EstimatedCharges metric and choose the currency (USD). 
6. Define your threshold. For the condition, choose Greater/Equal and set a static value (e.g., $500). 
7. Configure an action. Choose an existing Amazon SNS topic or create a new one to send an email or SMS notification when the alarm state is reached. 
8. Name your alarm and create it. This simple alarm can save you from month-end surprises. 

Best Practices for AWS Cost Savings 

The best practices we’re about to discuss are quick wins. If your environment isn't gigantic, you could get most of this done within a week. 

1. Review, Categorize, and Act 

The first step is a full review of every resource in your AWS account, from production to dev environments. Look at their utilization over the last month. You can use AWS CloudWatch or your own monitoring tools to get the data you need. Group everything into three buckets: 

1. Idle Workloads: These are the zombies in your account. To find them, you need the right metrics. CPU and memory utilization don't always tell the whole story. For an RDS database, memory might look high because of the InnoDB buffer pool, even if it hasn't served a single query. A better metric is the number of database connections over the last two weeks. For a load balancer, it’s the request count. For EC2, it's network in/out. Once you've identified these idle resources, either stop them or terminate them. It’s a quick action that can have a big impact on your bill. 
2. Underutilized Workloads: This is where you find resources running at that 10-20% utilization mark. You should aim for a baseline of at least 40-50% utilization before you start thinking about scaling up. If you see both CPU and memory are low, it's a straightforward downgrade.  Don't jump down multiple sizes at once. If you're on an m5.xlarge, move to an m5.large. If only one metric is low (like CPU), consider switching to a different instance family, like moving from an m5.xlarge (general purpose) to an r5.large (memory-optimized). In non-production environments, you can also consolidate workloads. Use containerization to run multiple applications on a single box or merge several databases onto a single RDS instance. 
3. Well-Performing Workloads: For the resources that are doing their job effectively, check if they're running on the latest generation of instance types. Moving from an older generation like m3 to a newer one like m5 gives you better performance for a lower price.  It's like upgrading from an old iPhone to a new one, it's faster and often has better battery life, but in this case, it costs you less each month. We consistently find customers running a significant number of servers on previous-generation hardware. This is another easy way to move forward. 

2. Tackling Your Data Costs 

Once you've sorted your primary resources, it's time to look at your data. Data costs can pile up in unexpected places, particularly with snapshots and storage. 

The Snapshot Pile-Up 

Snapshot charges get out of control very easily. They are also used as a backup by people, commonly using home-written automation scripts. The issue here is that the script which generates the backups runs to completion flawlessly, and the one which deletes old-dirty ones does not run at all. Most of the SMEs see the monthly snapshot fees have become more significant than the cost of the underlying EC2 instances to which they were making a backup.  

Let’s take one hypothetical example, a bill would begin at zero and increase more than $5,000 in one month of snapshots alone. 

The key takeaway here is to offload this operational burden.  

To do this automatically, AWS Backup or Amazon Data Lifecycle Manager is used. These are services designed to handle all the lifecycle of your backups, such as deletion. And just come and check your snapshot charges immediately. 

Selecting the appropriate S3 Storage Class.

The majority of them fail to differentiate between the S3 Standard storage class and use it everywhere. This is the most costly alternative. There are 2 major factors on which S3 storage is charged: storage amount and retrieval frequency. 

This is a basic example: the storage of 50 terabytes of information. 

• S3 Standard: Maximum cost. 
• S3 Glacier Deep Archive: cheapest price. 

When you are backing up data that you will only access once or twice a month, then you have to back up using S3 Standard-Infrequent Access (Standard-IA). One Zone-IA can be used when you have temporary data to use. For long-term archival, 

S3 Glacier or Glacier Deep Archive are extremely inexpensive, however, they offer varying SLAs regarding both retrieval time and durability. You should have a good cost analysis before you move anything based on your pattern of retrieval. 

3) Cleverly Use Data Transfer Charges? 

One of the most complicated elements of an AWS bill is data transfer. Prices are determined by the origin, destination and the amount of traffic. 

First, a basic validation: when you have workloads in the same Availability Zone communicating using private IP addresses there is no cost. When they are using public or elastic IPs to communicate, you are paying. Go back and ensure that your applications are using IPs that are private when communicating between them. 

Second, turn compression everywhere. Turn it on your CloudFront distributions, on your web servers, and when you upload data to S3. We have discovered that CloudFront will normally disable compression on default, and enabling it can save you a lot of money in data transfer. 

Third, when traffic across your private subnets and other services, such as S3 or DynamoDB, is large, the traffic is typically routed over a NAT Gateway, which you pay. You can use 

VPC Gateway Endpoints to send this traffic over the AWS private network instead. This can help save you a substantial amount on your NAT Gateway and data transfer bill. 

4) Better Scaling and Scheduling 

It is time to stop correcting the mistakes that happened in the past and create a more intelligent and proactive system. 

Leverage Time-Based Scaling 

The majority of a business does not have a steady flow of traffic 24/7. When examining an e-commerce site, the number of visits could drop off a cliff after midnight and re-establish itself around 9 a.m. However, there are numerous individuals who operate as many servers as possible on a 24-hour basis. 

As an example, imagine a customer with half a dozen c5.4xlarge instances running for one month, at prices of almost $3,000. They would be saving more than half a yearly monthly by just reducing the number of servers by half, down to two, during the lean periods. This is the easiest trick that carries a big effect.  

You can do the same to all of your non-production environments-dev, UAT, staging. Switch them off during the weekends and holidays. This alone can save you 20-25%. This is true of EC2, RDS and currently even Redshift clusters can be put on hold. 

Adopt Spot Instance and Serverless. 

And this is where the largest savings are lurking. 

Spot Instances can reduce EC2 prices by 90%. The official AWS documentation may sound threatening, as there are warnings of interruptions. In practice, however, in the majority of workloads the interruption rate is not more than 5%.  

On average, 1 in every 100 companies experiences a host interruption each month. Any workload that is running on an Auto Scaling group qualifies as a good candidate for Spot. It can be nothing but a change of configuration. Test your non-production environments first to get used to it, and then in production. 

The other game-changer is serverless architecture. When you deploy services such as Lambda and S3 to a static site, you no longer pay to have idle capacity. Consider that you are doing a marketing microsite as part of a campaign. You don't know whether you will have a thousand or a million visitors. Previously users over-provision servers on a just in case basis. The serverless approach is to design it in such a way that it can infinitely scale and you only pay as you use it. It removes the hassle of dealing with servers and is cost effective. 

5) Long-Term Strategy 

The quick wins are awesome, but when you want to be cost effective in the long run, you have to implement some larger and more strategic changes. 

Get Smart About Regions and Processors. 

All AWS regions do not cost the same. North Virginia (us-east-1) is widely the least expensive region, not only of servers, but of data transfer, storage and most other services. By way of comparison, the Mumbai area can be as much as 40 percent higher. Although your data residency or latency needs may be stricter than you need, at least some of your non-production and internal-facing applications should be hosted in North Virginia. 

Also, look at your processors. Amazon provides examples of AMD processors as well as Intel. m5 (Intel) and m5a (AMD) will have the same vCPU and RAM, but the AMD one is cheaper. AMD is approximately 10 percent lower in North Virginia. It is a huge 35% less expensive in Mumbai. You save a lot of money with just a few flicks, if your application does not demand Intel. 

Think Again: You Have a Savings Plan. 

RIs are mostly no longer used. 

Savings Plans provide the same degree of discounts but are much more flexible and less of a hassle to manage. They automatically extend to the best-fit instance types and can even extend to Fargate and Lambda usage. 

One note of caution- the default RI utilization reports in the AWS Cost Explorer are deceptive. They tend to be latent and are not cost-based, but rather number based. An even more effective way is to keep a record of your coverage: what fraction of your bill is paid with Savings Plans compared to what you pay per-demand? Track this by creating custom reports. 

6) Building a Cost-Aware Culture 

Once you've cleaned up your environment, how do you keep it that way? You need to build cost awareness into your company's DNA. This comes down to two things: tagging and reporting. 

Tagging is a powerful tool for accountability. You should have a strict tagging policy for all resources, with tags for business unit, application, environment, and owner. Use these tags to generate reports and charge back the costs to the teams that are actually using the resources. No costs should be allocated to a generic "central IT" bucket. This forces every team to take ownership of their spending. 

Reporting needs to be real-time and accessible to everyone. Every application owner should be able to see their costs from yesterday and today and understand what caused any changes. Push this information out through email or Slack. Create simple dashboards showing the top 10 cost-incurring resources of each type (EC2, S3, RDS, etc.). When the information is readily available, people will find the time to act on it. 

Cost management needs to be a discipline, just like testing or agile development. It shouldn't be a periodic emergency drill you run once a quarter. When it becomes an ongoing practice for everyone, you'll find you don't have to "cut" costs anymore.  

To Sum Up 

The truth is simple AWS isn’t “expensive.” Unoptimized AWS usage is expensive. And that’s why cost optimization is no longer optional; it’s a core competency for every business scaling on AWS. 

This is where https://costimizer.ai/ becomes a differentiator. Unlike AWS native tools that simply show you “what you spent,” Costimizer is an Agentic AI, which analyzes usage patterns, predicts spend trajectories, and recommends precise actions to reduce costs without compromising performance & compliance. 

Here’s what you get with Costimizer: 

• Clarity across AWS services → instantly see where spend is leaking. 
• Real-time cost intelligence → prevent runaway bills before the month ends. 
• Predictive budgeting → know your likely bill before it arrives. 
• Actionable savings strategies → rightsizing, smarter storage, Reserved Instance planning. 
• Confidence to scale → grow without fearing any uncontrolled cost spikes. 

The Results You Can Expect with Us 

• Up to 20–30% savings on AWS bills. 
• We provide you seamless compliance, automation runs within your guardrails. 

How much does it cost? We don’t charge you anything extra, we only ask for a small percentage of what we save for you. We know every dollar wasted on AWS is a dollar not invested in growth. With Costimizer, you’re not paying for another dashboard, you’re paying for intelligent AI models trained on your own data enabling clarity, control, and continuous savings.

 Frequently Asked Questions