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How Levels.fyi Cut AWS Costs by 15% for 26M Users Without Performance Loss

Author

Govardhana M K
August 09, 2025

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🧠 DEEP DIVE USE CASE

How Levels.fyi Cut AWS Costs by 15% for 26M Users Without Performance Loss

It is so easier and theoritical to say, to cut cloud costs reduce usage, terminate idle resources, right size, reserve capacity, use spot instances, enable lifecycle policies etc., but in reality even a small s3 bucket life cycle change can trigger downstream processes and cause unexpected impact.

So, it is not only about removing what is unused, it is about understanding the workload, traffic pattern and dependency chain.

Today we go a deep dive on a real world use case of how Levels.fyi applied targeted AWS cost optimization strategies. Before we jump in, it is worth starting with the end result. This is what they achieved.

Data Source: Levels.fyi

Break down the bill

It all started with a deep dive into AWS Cost Explorer. Levels.fyi wanted to understand the exact sources of their cloud spend before acting. The data showed CloudFront taking the largest share, followed by EC2, ECS, and RDS. Even smaller categories like NAT, WAF, and ElastiCache were flagged for review. This clear picture became the foundation for their cost optimization plan.

Data Source: Levels.fyi

S3 Listeners Creating SQS Events

Levels.fyi had terabytes of historical job data in S3. They planned to move it to Glacier Deep Archive using a lifecycle policy for cost savings.

The catch was that S3 listeners were sending events to SQS for every object change. Moving data would have flooded consumers with millions of messages.
They paused the listeners, migrated the data, and turned them back on.

Learning:

  • Check all event triggers before bulk storage changes.

  • Simulate changes in a test environment.

  • Plan listener pauses or throttling during migration.

  • Re enable services only after validating impact.

Mysterious ElastiCache

An ElastiCache charge appeared with no reference from backend services. Levels.fyi validated usage by connecting and running DBSIZE and INFO to check metrics like connected_clients and total_commands_processed.

The activity seen on CPU and bytes I/O came from a backup job, not live traffic. They took a final backup and removed the instance.

Learning:

  • Search IaC, service configs, and env vars for any cache reference.

  • Verify with redis-cli using DBSIZE, INFO, and client list

  • Correlate CloudWatch metrics with backup windows before deciding.

  • Tag caches with owner and purpose to avoid orphans.

  • Snapshot before removal and set a quick rollback plan.

EC2 Instances CleanUp and Optimization

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