The Top 5 Causes of Unexpected Cloud Expenses-and How to Avoid Them

For many small companies, cloud expenses can feel difficult to pin down.

The costs slowly accumulate away until-bam!-you're hit with an unexpected bill. But don't worry, you're not alone. These surprise charges often stem from common, preventable issues.

Let's unpack the top five culprits and what you can do to keep your cloud budget in check.

Idle Resources Lurking in the Shadows

You spin up an EC2 instance for testing, a temporary RDS database for a proof of concept, or extra storage to hold backup data "just for a little while." Fast forward a few weeks, and those resources are still running-or storing-and silently accumulating costs.

• Common examples:

  • Unattached EBS volumes (storage left behind when you terminate EC2 instances).
  • Elastic Load Balancers (ELBs) still active after their associated instances are shut down.
  • Old snapshots in S3 with no lifecycle policies.

• How to fix it:

  • Automate cleanup: Use AWS Config to set rules for idle resources and trigger notifications or remediation actions.
  • Enable tagging: Implement a tag-based lifecycle policy to auto-delete resources marked as temporary.
  • Regular audits: Schedule monthly resource reviews to catch stragglers.

Data Transfer Fees: The Silent Budget Killer

AWS charges for data transfer between regions, Availability Zones (AZs), and even services within the same account. These costs can escalate quickly if you're moving large datasets for analytics, backups, or cross-region disaster recovery.

• Where it happens:

  • Transferring logs or large datasets between regions for analysis.
  • APIs or applications with heavy outbound traffic to end users.
  • Hybrid architectures with frequent data exchanges between on-premises and the cloud.

• How to fix it:

  • Consolidate workloads: Where possible, run workloads in the same region and AZ to minimize transfer costs.
  • Leverage edge services: Use Amazon CloudFront to cache frequently accessed content closer to users, reducing outbound data transfer fees.
  • Optimize networking: Evaluate Direct Connect or AWS Global Accelerator for high-volume or hybrid setups, which can reduce data movement costs.

Over-Provisioned Resources

Overestimating your compute or database requirements is easy to do, but running resources larger than necessary inflates costs needlessly. For example, do you really need that m5.4xlarge EC2 instance running a low-traffic website?

• Common examples:

  • Using general-purpose EC2 instances for compute tasks that could run on burstable instances (like t3.micro).
  • Over-provisioned RDS databases with far more CPU or memory than your workload requires.
  • Scaling to peak demand levels manually instead of using auto-scaling to match demand dynamically.

• How to fix it:

  • Run load tests: Understand actual workload demands and scale resources accordingly.
  • Use AWS Compute Optimizer: Get specific recommendations for downsizing or switching instance types.
  • Explore serverless options: Services like AWS Lambda or Aurora Serverless scale automatically, eliminating the need for guesswork.

Serverless Overhead: Misjudging Lambda or Event-Driven Costs

Serverless services like AWS Lambda, DynamoDB, or Step Functions are cost-efficient at scale, but they come with their own pitfalls. The pay-per-use pricing model means that poorly optimized workloads or unexpected spikes in activity can lead to runaway costs.

• Common missteps:

  • High invocation counts: Small inefficiencies in Lambda functions (like unnecessarily frequent invocations or overly chatty APIs) add up over millions of executions.
  • Exceeding free tier limits: Many rely on AWS's free tier for Lambda and DynamoDB, but heavy usage can quickly outgrow these limits.
  • Overlooked payload sizes: Large response payloads or logs stored in CloudWatch can balloon costs without you noticing.

• How to fix it:

  • Optimize your code: Minimize invocation times by reducing function complexity and avoiding unnecessary calls.
  • Batch processing: Aggregate requests where possible to reduce invocation counts. For example, process 100 records in a single function run rather than invoking it 100 times.
  • Log retention policies: Configure shorter retention periods for CloudWatch logs unless longer storage is essential.

Unmonitored Reserved Instances or Savings Plans

Buying Reserved Instances (RIs) or committing to Savings Plans can save you up to 72%, but only if you're using them effectively. Misaligned purchases-like committing to an m5.large when you migrate workloads to an m6i.large-can leave savings on the table while still costing you for unused capacity.

• Common missteps:

  • Purchasing RIs for static workloads that later shift to different regions or instance families.
  • Overcommitting to Savings Plans without considering workload variability.
  • Failing to monitor actual utilization rates.

• How to fix it:

  • Track usage: Use AWS Cost Explorer's Savings Plan and RI Utilization reports to measure effectiveness.
  • Centralize management: For multi-account setups, use AWS Organizations to consolidate RI or Savings Plan purchases for maximum flexibility.
  • Combine with Spot Instances: For unpredictable workloads, blend reserved capacity with lower-cost Spot Instances to optimize spending.

Pro Tip: Proactively Manage Cloud Costs

Preventing unexpected charges requires a mix of automation, oversight, and cloud-savvy practices. Start by integrating cost management into your workflows-whether it's tagging policies, routine audits, or team training on cost awareness. Remember, the sooner you spot an issue, the easier it is to fix.