5 AWS Services Draining Your Budget and How to Fix Them
If your AWS bill keeps climbing unexpectedly, you’re not alone. Many businesses waste up to 35% of their cloud budgets due to forgotten resources, inefficient configurations, and lack of oversight. Here are five AWS services that often drive up costs and actionable ways to reduce spending:
- Amazon EC2: Oversized instances and 24/7 usage inflate costs. Use tools like Compute Optimizer to choose the right EC2 instances, enable auto-scaling, and consider Savings Plans or Reserved Instances or Spot Instances for discounts.
- Amazon S3: Storage costs balloon with high-cost tiers and old data. Move data to lower-cost storage classes, clean up unused buckets, and reduce transfer fees with VPC Gateway Endpoints.
- Amazon RDS: Over-provisioned databases and unnecessary Multi-AZ setups add expenses. Downsize instances, disable automated backups for non-critical environments, and review read replica usage.
- AWS Lambda: Poorly configured memory, excessive invocations, and long execution times can spike costs. Optimize memory settings, batch events, and use tools like Lambda Power Tuning.
- EBS Volumes: Unused volumes and snapshots quietly drain budgets. Regularly delete unattached volumes, archive old snapshots, and switch to cost-efficient gp3 storage.
Key takeaway: Regular monitoring, right-sizing, and automation can cut AWS costs by up to 40%. Tools like AWS Cost Explorer, Compute Optimizer, and lifecycle policies can help you stay on top of spending.
5 AWS Services Draining Your Budget: Cost Optimization Strategies
Top 5 Cost Optimization Tips Every AWS User Should Know
Why AWS Costs Spiral Out of Control
AWS makes it easy to spin up resources, but without proper oversight, costs can skyrocket. When resources are created without careful monitoring or configurations are mismatched to actual needs, monthly bills can quickly spiral out of control. Here are three common culprits behind unexpected AWS expenses.
On-Demand Pricing and Oversized Resources
Many teams opt for on-demand pricing because it’s simple and doesn’t require long-term commitments. But this convenience comes at a price - it’s the most expensive pricing model. Running instances around the clock, especially those oversized for occasional traffic spikes, can lead to massive costs. The "lift and shift" migration strategy, where workloads are moved to the cloud without optimizing them, often results in paying for capacity that’s rarely used. AWS itself estimates that optimizing resources through rightsizing and automation can cut costs by up to 36%. But beyond compute, storage practices can add another layer of hidden expenses.
Storage and Data Transfer Fees
Storage costs, particularly with S3, can balloon quickly. Using high-cost storage tiers, holding onto outdated snapshots, or storing unnecessary file versions are all common pitfalls. By tailoring storage to match data type and usage patterns, costs can drop by as much as 50%. Another sneaky expense comes from data transfer fees. Moving data across AWS Regions, Availability Zones, or out to the public internet can rack up significant charges. Even something as simple as a misconfigured setup that routes traffic between regions instead of keeping it local can quietly drive up costs.
Serverless and Usage-Based Charges
Serverless services like AWS Lambda are marketed as cost-efficient because you only pay for what you use. But excessive invocations or over-allocated memory can cause sudden cost spikes. The pay-per-use model requires constant vigilance, as limited real-time visibility into metrics like invocation counts or execution duration often leaves teams unaware of rising costs until it’s too late.
With over 240 AWS products and the challenge of implementing proper tagging, many organizations struggle to track their actual spending. This lack of visibility means most teams don’t fully understand their costs until they see the bill.
Amazon EC2: Lowering Compute Expenses
Amazon EC2 often accounts for the largest portion of AWS bills, mainly due to over-provisioned capacity. The good news? There are simple and effective ways to cut these costs without sacrificing performance. Let’s dive into some practical strategies.
Finding Underused Instances
AWS provides tools like Compute Optimizer to identify idle instances by analyzing 14 days of performance data. Instances are flagged as idle if CPU usage stays below 5% and network I/O is under 5MB per day. To further optimize, you can use AWS Cost Explorer for rightsizing and AWS Trusted Advisor to spot stopped instances.
A useful rule of thumb is the "40% rule": if CPU and memory usage remain below 40% for four weeks, consider downsizing to a smaller instance type (e.g., from c4.8xlarge to c4.4xlarge). Installing the CloudWatch agent can also help track memory usage for better insights.
Using Savings Plans and Reserved Instances
If you're looking for big savings, commitment pricing can reduce compute costs by up to 72%. Savings Plans are often the easier choice, as they automatically apply discounts across instance families, sizes, regions, and even services like AWS Fargate and Lambda (though managed database commitments require a different approach).
For more specific needs, Reserved Instances offer similar discounts but require manual management. They’re ideal when you need guaranteed capacity in a specific Availability Zone. Tools like AWS Cost Explorer can provide automated recommendations for these purchases. However, make sure to right-size your instances using Compute Optimizer before committing to 1-year or 3-year terms to avoid paying for unused capacity.
Setting Up Auto Scaling and Schedules
Not all environments need to run 24/7, especially development and testing setups. By automatically stopping non-production instances during off-hours, you can save up to 70%. Use AWS Instance Scheduler to automate this with tags like Schedule=OfficeHours or Environment=Dev.
For production workloads with fluctuating demand, Auto Scaling Groups can dynamically adjust the number of instances based on actual usage. Use the describe-scaling-activity CLI command to review scaling activity and avoid over-scaling. Additionally, set DeleteOnTerminate in your launch templates to automatically remove unattached EBS volumes.
For even deeper savings, consider incorporating Spot Instances, which can reduce compute costs by up to 90%. These instances are a great option for workloads that can tolerate interruptions, offering significant cost reductions without impacting overall performance.
Amazon S3: Controlling Storage and Transfer Costs
Managing Amazon S3 effectively is key to keeping your AWS bills in check. Costs can quickly rise due to pricey storage classes, outdated data, and cross-region transfers. But with a few strategic tweaks, you can cut expenses without compromising data availability.
Moving Data to Lower-Cost Storage Classes
One of the easiest ways to save money is by shifting data to more cost-effective storage classes. For instance, S3 Standard-IA (Infrequent Access) can reduce storage costs by up to 40% for data accessed less than once a month. For data that’s only needed quarterly, S3 Glacier Instant Retrieval offers up to 68% savings. And if you’re storing compliance or archival data, S3 Glacier Deep Archive is the most affordable option, slashing costs by up to 95%.
Take Zalando as an example. By using S3 Intelligent-Tiering, the company cut its storage costs by 37% annually. Max Schultze, their Lead Data Engineer, explained:
"We are saving 37% annually in storage costs by using Amazon S3 Intelligent-Tiering to automatically move objects that have not been touched within 30 days to the infrequent-access tier."
To achieve similar results, use S3 Storage Class Analysis to track how often data is accessed. Then, set up lifecycle policies to automate transitions - like moving data to Standard-IA after 30 days and to Glacier after 90 days. Just keep in mind that Standard-IA, One Zone-IA, and Intelligent-Tiering require a minimum object size of 128 KB, so they’re not ideal for small files.
Beyond optimizing storage classes, it’s also important to clean up outdated and unused data.
Cleaning Up Unused Buckets and Old Versions
Old, forgotten data can quietly inflate your storage costs. If S3 Versioning is enabled, you’re charged for every version of an object. If noncurrent versions make up more than 10% of your total storage, you may be storing unnecessary duplicates.
To address this, use S3 Storage Lens to identify "cold" buckets - those with little to no retrieval activity. These buckets often hold data that’s no longer needed. The tool also highlights buckets with high percentages of noncurrent versions or incomplete multipart uploads, both of which can drive up costs.
Speaking of incomplete uploads, they’re another hidden expense. When large file uploads fail or are abandoned, the partial data lingers in S3 and racks up charges. To avoid this, set a lifecycle rule to automatically delete incomplete uploads after 7 days. You can also use lifecycle expiration rules to remove old object versions after a set period, saving you the hassle of manual deletions.
Once your storage is in order, you can turn your attention to reducing data transfer costs.
Reducing Data Transfer Charges
Data transfer fees can pile up, especially if you’re moving data between regions or out to the internet. The good news? There are ways to minimize these charges.
For starters, transfers within the same AWS Region - like between S3 and EC2 - are free. Additionally, data sent from S3 to Amazon CloudFront doesn’t incur any charges, making CloudFront a smart choice for cutting down on internet egress costs. AWS also gives you the first 100 GB of internet data transfer each month for free across all services.
If you’re using VPC-based resources, consider setting up a VPC Gateway Endpoint for S3. This keeps traffic on AWS’s private network and avoids NAT gateway processing fees. And if you often access data across multiple regions, replicating buckets to the most-used regions can save around 75% per GB compared to repeated cross-region downloads.
A great example of cost-saving measures comes from Pomelo, a fintech company in Latin America. By leveraging S3 Glacier storage classes for their data lake, they projected storage savings of 40-50%. On top of that, they reduced encryption costs by 95% by implementing S3 Bucket Keys. These kinds of adjustments can lead to long-term savings across your AWS environment.
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Amazon RDS: Reducing Database Expenses
Managing Amazon RDS costs effectively requires careful attention to resource usage. Over-provisioned features can inflate expenses, but with targeted adjustments, you can cut costs without sacrificing performance.
Adjusting Database Instance Sizes
To determine if your database instance is over-provisioned, monitor key CloudWatch metrics like CPUUtilization, DatabaseConnections, FreeableMemory, and I/O throughput over a four-week period. If your production instance shows less than 30% utilization, consider downsizing. As Samujjwal Roy, Principal Consultant at AWS, points out:
"Under-provisioning a resource has a direct impact on performance, so it gets escalated and corrected. However, over-provisioned resources don't have a direct functional impact that results in application disruption, so it's often missed."
For instances with zero connections over seven days, AWS Trusted Advisor can flag them for shutdown. While these native tools offer basic insights, they often lack the automation needed for complex environments.
Keep in mind that resizing often requires downtime, so plan these changes during maintenance windows. Since database storage and instance types are managed separately, scaling your instance won’t impact your allocated storage size. If you're using commercial engines like Oracle or SQL Server with a Bring Your Own License (BYOL) model, ensure your licensing aligns with any planned changes.
For databases that only operate during business hours, stopping them during off-hours can cut costs by as much as 70%. Automate this process with AWS Instance Scheduler, which manages start and stop times for tagged instances. Additionally, Reserved Instances (RIs) can significantly lower costs - offering up to a 72% discount compared to on-demand pricing. Even a one-year RI with no upfront payment provides a 42% discount.
After addressing instance sizes, evaluate your deployment configurations for more savings.
Reviewing Multi-AZ and Read Replicas
Multi-AZ deployments enhance availability by maintaining a standby replica in a separate Availability Zone. However, this setup can double your costs due to the added compute and storage resources. For workloads that aren’t critical, switching to a Single-AZ configuration can provide immediate savings.
Read Replicas, on the other hand, are billed as independent active instances. Adding replicas increases your costs, so before deploying one, check if your primary instance is underutilized. If its CPU and I/O usage consistently remain below 30%, you might be able to consolidate the workload back to the primary instance instead of creating a replica.
Standard RDS Multi-AZ setups limit the standby replica to failover purposes only - it cannot handle read or write operations. Amazon Aurora, however, allows the standby to function as a Read Replica, adding flexibility. Remember, while stopping an RDS instance halts instance-hour charges, storage and backup costs will still apply.
Once you've optimized your instance sizes and configurations, take a closer look at your backup policies to minimize additional expenses.
Setting Backup Retention Periods
Unmanaged backup storage can quickly add up. AWS offers free backup storage equal to 100% of your total provisioned database storage per region. However, any additional incremental data is billed per GB-month.
Automated backups are deleted based on your retention policy, which can range from 1 to 35 days. Manual snapshots, on the other hand, are never automatically removed - even if the database they were tied to has been deleted. These orphaned snapshots can result in unexpected charges, so make it a habit to regularly delete unused snapshots.
For non-critical environments like development or testing, you can disable automated backups by setting the retention period to zero. If only a few databases require extended retention, create tailored backup plans instead of applying a universal policy, which could lead to unnecessary costs. With Amazon Aurora, manual snapshots are free for up to 35 days, but charges apply beyond that as they are treated as full backups.
Before deleting an unused instance, always take a final manual snapshot to preserve the data and avoid ongoing costs.
AWS Lambda: Controlling Serverless Costs
AWS Lambda’s pay-as-you-go pricing can lead to savings of up to 57%, but poorly configured functions can quickly drive up expenses. With charges based on the number of invocations and execution duration (measured in GB-seconds), fine-tuning these parameters is crucial to managing costs effectively. As with other AWS services, careful configuration and monitoring are essential to make the most of its cost-saving potential.
Tuning Memory and Execution Time
Lambda’s pricing ties memory allocation to CPU performance - boosting memory also increases CPU power. While higher memory settings might seem more expensive, they can speed up execution times significantly, potentially reducing the overall cost per execution.
To determine the ideal memory configuration, try AWS Lambda Power Tuning, an open-source tool designed to test your function across various memory levels. For instance, increasing memory from 512 MB to 1,024 MB could cut execution time from 6 seconds to 2.5 seconds, lowering costs from $40.20 to $33.25 per million executions.
For production environments, AWS Compute Optimizer provides machine learning–driven recommendations once a function has been invoked at least 50 times over a 14-day period. Additionally, CloudWatch’s "Max Memory Used" metric can help you identify over-provisioned functions by comparing it to your allocated memory.
Switching to Graviton2 (ARM64) processors can improve performance by up to 19% and reduce costs by 20%. To further optimize, set realistic timeouts to avoid unnecessary costs from slow dependencies. Minimizing package size can also reduce initialization time, especially as Lambda will begin billing for initialization in August 2025.
Limiting Invocations and Batching Events
Reducing the number of function executions is one of the simplest ways to lower invocation costs. Event filtering at the source - using services like SQS, Kinesis, or DynamoDB - ensures that only relevant records trigger Lambda, avoiding unnecessary invocations.
Batching records can also help. By setting batching windows (up to 5 minutes), you can group multiple records into a single invocation, spreading initialization costs across more data points. For stream processing, enabling partial batch responses ensures only failed records are retried, rather than reprocessing the entire batch.
Where possible, eliminate proxy Lambdas by using direct integrations with services like API Gateway, Step Functions, SQS, S3, or DynamoDB. As FinOps Specialist German Eichemberger explains, Step Functions can help reduce costs by charging based on state transitions rather than idle wait time:
"The orchestrator [Lambda] pays for all the idle wait time between steps... Step Functions charges per state transition, not idle duration".
To prevent runaway costs from traffic spikes or infinite loops, set reserved concurrency limits.
Monitoring Costs by Function
AWS Cost Explorer allows you to track Lambda expenses by resource tags, while AWS Cost Anomaly Detection uses machine learning to flag unusual spending patterns before they escalate.
For deeper insights, tools like AWS X-Ray can identify performance bottlenecks and slow SDK calls that increase execution time. Amazon CodeGuru Profiler highlights the most expensive lines of code, and AWS Trusted Advisor can identify issues like over-provisioned memory, excessive timeouts, or high error rates.
For long-term savings, consider automated commitment management for Compute Savings Plans, which offer discounts of up to 17% for 1- or 3-year commitments. Provisioned Concurrency can also cut duration costs by 16%.
EBS Volumes and Snapshots: Removing Unused Storage
Unused EBS volumes and snapshots can quietly drain your budget. Even when volumes are unattached, they still rack up charges - typically $0.10 per GB-month for gp2 storage. For example, just 50 unused 100 GB volumes could cost you about $500 per month or $6,000 annually. Snapshots can also pile up over time, adding unnecessary expenses if not managed properly.
Finding Detached and Idle Volumes
To keep costs under control, it’s crucial to estimate your savings and manage EBS storage actively. Unattached EBS volumes appear as "available" in your AWS console. This status means they aren’t linked to any running EC2 instance but are still generating charges. To quickly find these volumes, you can use the AWS CLI command:
aws ec2 describe-volumes --filters Name=status,Values=available
For volumes that are still attached but barely used, AWS Compute Optimizer can help. It flags idle volumes based on their I/O activity - those with fewer than one read/write operation per day over a 14-day period. You can also check metrics like VolumeIdleTime, VolumeReadOps, and VolumeWriteOps in CloudWatch to identify underutilized volumes. AWS Trusted Advisor provides additional recommendations for spotting volumes that aren’t being used effectively.
Before deleting a volume, always create a snapshot. Snapshots are more cost-efficient, typically costing about 37.5% less than active gp3 storage - around $0.05 per GB-month versus $0.08 per GB-month. As Stephen Barr, Principal Architect at CloudFix, advises:
"EBS volumes attached to EC2 instances that have been stopped for more than one month should be detached and deleted".
To avoid orphaned volumes in the future, enable the DeleteOnTermination setting for data volumes when launching instances. This setting, enabled by default only for root volumes, ensures additional data volumes are deleted when the instance is terminated.
Deleting Old Snapshots
EBS snapshots are incremental, storing only the data that has changed since the last snapshot. AWS automatically merges unique blocks into future snapshots, making it easier to maintain data integrity.
To manage snapshot retention, consider using Amazon Data Lifecycle Manager (DLM). This tool automates the deletion of older snapshots based on tags and schedules, helping enforce retention policies. For snapshots needed for compliance but rarely accessed, move them to the Archive tier. At $0.0125 per GB-month, this option is up to 75% cheaper than the Standard tier. Additionally, when deregistering Amazon Machine Images (AMIs), remember to manually delete their associated snapshots, as AWS doesn’t do this automatically. A simple rule of thumb? Review snapshots older than 30 days to decide whether to delete or archive them.
Next, let’s look at how optimizing volume size and performance can further cut costs.
Adjusting Volume Size and Performance
Overprovisioned volumes often lead to wasted spending on capacity and performance you don’t actually need. Switching from gp2 to gp3 volumes can save you up to 20% while also improving baseline performance. Unlike gp2, gp3 allows you to separately configure IOPS and throughput, so you don’t have to overprovision storage just to get higher performance. AWS Compute Optimizer can provide recommendations based on actual usage over a 14-day period to help you right-size your volumes.
For workloads requiring high IOPS, consider whether gp3 can meet your needs instead of relying on more expensive provisioned IOPS (io1/io2), which cost $0.06 per IOPS.
| Storage Type | Cost per GB-Month | Best Use Case |
|---|---|---|
| gp2 Volume | $0.10 | Legacy workloads (consider migrating) |
| gp3 Volume | $0.08 | General-purpose |
| Snapshot (Standard) | $0.05 | Frequent recovery needs |
| Snapshot (Archive) | $0.0125 | Long-term retention (90+ days) |
Conclusion
Managing AWS costs effectively requires ongoing attention and proactive strategies. As we've seen with services like EC2, S3, RDS, Lambda, and EBS, specific actions - such as right-sizing, automated scheduling, and leveraging commitment-based pricing - can lead to savings of up to 36%.
To make the most of these opportunities, start by incorporating cost management into your regular workflows. Monitor performance metrics over a two- to four-week period to identify usage trends and peaks before implementing changes. Use AWS Budgets with proactive alerts to stay ahead of potential overspending. Additionally, enforce tagging standards across all resources to give teams clear visibility into the cost impact of their applications.
For a streamlined approach, automated tools can be a game-changer. Platforms like Opsima simplify cost management by providing real-time monitoring and intelligent handling of Savings Plans and Reserved Instances. With features like 15-minute onboarding and a pay-as-you-save pricing model, Opsima ensures you maximize discounts without the risk of overcommitting or missing savings opportunities.
FAQs
How can I identify and optimize oversized EC2 instances to save costs?
To keep your EC2 instances running efficiently and cost-effectively, start by evaluating their performance over a period of at least two weeks - ideally a full month. This timeframe helps you capture both typical usage patterns and peak demand. Use Amazon CloudWatch to track key metrics like CPU utilization, memory usage, and disk I/O. Instances showing average CPU or memory usage below 40% are strong candidates for downsizing.
After identifying potential instances for optimization, turn to AWS Cost Explorer for rightsizing recommendations. This tool provides suggestions for smaller instance types and estimates the savings you could achieve. Before switching to a smaller instance, double-check that it can handle your workload's peak resource demands. Once you're confident, update the instance type through the EC2 console and monitor its performance for at least a week to ensure it meets your needs. Regularly revisiting and adjusting your instance sizes can help you strike the right balance between performance and cost savings.
How can I reduce my Amazon S3 storage costs?
To cut down on Amazon S3 storage costs, start by choosing a storage class that matches your data access patterns. Options like Intelligent-Tiering, Standard-IA, Glacier, or Deep Archive can help you save based on how frequently you need to access your files. Next, use S3 Lifecycle rules to automate cost-saving tasks, such as moving data to lower-cost tiers, deleting outdated files, or clearing incomplete uploads and old file versions. Additionally, keep an eye on your storage habits with tools like S3 Storage Lens or AWS Cost Explorer to spot and remove unused or stale files. These strategies can help you manage expenses while still keeping your data available when needed.
How can I control AWS Lambda costs and avoid unexpected charges?
To keep AWS Lambda costs under control, start by optimizing memory and CPU allocations. Lambda charges depend on both memory usage and execution time. If you allocate too much memory, you’re likely overspending; if you allocate too little, execution time may drag, leading to higher costs. Tools like AWS Compute Optimizer can help you test and identify the ideal memory configuration that balances these factors, reducing overall expenses. Keep an eye on metrics like memory usage, duration, and billed duration in Amazon CloudWatch to fine-tune your settings as needed.
Another way to save is by cutting down on unnecessary invocations and execution time. For instance, enable event-source filtering in services like SQS or DynamoDB so that Lambda only triggers for relevant events. If your application is sensitive to latency, you might want to use Provisioned Concurrency to reduce cold-start delays, or schedule periodic “warming” to maintain performance. Switching to the ARM/Graviton architecture can also be a cost-effective move if your code supports it. Additionally, set the timeout to the shortest reasonable duration and use Lambda Layers to share libraries, which can help reduce deployment size and speed up startup times.
By combining these practices - adjusting memory, filtering events, managing concurrency, and monitoring metrics - you can keep AWS Lambda costs manageable and avoid unexpected spikes in your bill.
