Your AWS bill arrives and the number keeps climbing. You’ve heard about reserved instances, spot pricing, and savings plans, but actually implementing them feels overwhelming. Here’s what actually works.
Start With Visibility
You can’t optimize what you can’t see. AWS Cost Explorer is free and immediately useful. Enable it, set up a cost allocation strategy with tags, and spend a week just watching where money goes.
Most organizations find 20-30% of their spend is waste – idle instances, oversized databases, forgotten resources from projects that ended months ago. This low-hanging fruit requires no architectural changes.
Right-Size Before You Commit
Reserved instances lock you into a capacity commitment. Buying them before you understand your actual usage patterns is expensive. Run your workloads for at least a month with detailed monitoring before making any commitments.
AWS Compute Optimizer provides right-sizing recommendations based on actual utilization. That m5.xlarge might really need to be an m5.large. The savings compound when you’re running hundreds of instances.
Savings Plans vs Reserved Instances
Savings Plans offer more flexibility than traditional reserved instances. You commit to a dollar amount per hour rather than specific instance types. This works better for organizations whose workloads evolve.
For stable, predictable workloads, RIs still offer slightly better discounts. The math depends on your specific situation. Run the numbers before committing.
Spot Instances for the Right Workloads
Spot pricing can cut compute costs by 60-90%, but only for fault-tolerant workloads. Batch processing, CI/CD pipelines, and stateless containers handle interruptions well. Your production database does not.
The key is designing for interruption from the start. If your application can’t gracefully handle a two-minute termination warning, spot isn’t for you.
Storage Tiers Matter
S3 Intelligent-Tiering automatically moves objects between access tiers based on usage patterns. For data with unpredictable access, this is nearly always the right choice.
For predictable patterns, lifecycle policies offer more control. Move logs to Glacier after 30 days, delete them after a year. These policies run automatically once configured.
The Human Factor
Technology changes don’t matter if developers keep spinning up oversized resources because it’s easier than checking requirements. Cost awareness needs to be part of your engineering culture.
Some organizations implement internal chargebacks, billing teams for their cloud usage. Others create budget alerts that notify engineers when their projects approach limits. Both approaches work – pick the one that fits your culture.
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