Cost Optimization

Cloud Spending Without Visibility Is Just Waste With Extra Steps

The promise of the cloud is that you only pay for what you use. The reality? Most companies pay for way more than they need — oversized instances running around the clock, storage tiers that don’t match access patterns, and commitments based on outdated usage data.

The bill climbs every month, but nobody can explain exactly what changed or whether the increase is even justified.

Cost optimization isn’t about cutting corners. It’s about making sure your AWS spend lines up with the value it actually delivers. Done well, it frees up budget for the projects that matter.

Common Problems We Solve

Cost issues in AWS environments follow predictable patterns. If any of these sound familiar, you’re leaving money on the table:

• No spending visibility. The monthly bill is a single number that finance receives and engineering shrugs at. Nobody can break down costs by team, project, environment, or feature.
• Oversized resources. Instances and databases were provisioned for peak load — or for a load estimate that never materialized — and nobody revisited the sizing afterward.
• Always-on non-production environments. Dev and staging environments run 24/7 even though they’re only used during business hours. Weekends and holidays cost the same as workdays.
• Unused resources piling up quietly. Unattached EBS volumes, idle load balancers, forgotten snapshots, and abandoned Elastic IPs add up. Individually they’re small. Collectively they’re significant.
• Commitment strategy mismatch. Reserved Instances or Savings Plans were purchased based on a point-in-time analysis that no longer reflects how you actually run. Some commitments go underutilized while on-demand spend stays high elsewhere.
• No tagging discipline. Resources aren’t tagged consistently, so cost allocation per team or project is impossible. Finance can’t do chargeback, and engineering can’t figure out who owns what.
• Data transfer costs flying under the radar. Cross-region and cross-AZ data transfer charges get overlooked during architecture decisions but can represent a real chunk of the bill.

Our Approach

We treat cost optimization as an ongoing discipline, not a one-and-done project. The first sprint delivers immediate savings, but the real value comes from building the visibility and processes that keep waste from creeping back in.

Spend Analysis and Baselining

We start by building a complete picture of your AWS spending. This goes way beyond the top-level bill — we break costs down by service, account, region, resource, and tag (where tagging exists). We spot trends, anomalies, and the specific resources driving the biggest portions of your spend.

We also set a cost baseline so you can measure the impact of every optimization we make. Without one, you can’t tell the difference between real savings and normal usage fluctuations.

Right-Sizing Assessment

Using AWS Compute Optimizer’s ML-driven recommendations and historical CloudWatch metrics, we evaluate every compute instance, database, and container task for right-sizing opportunities. We’re hunting for resources where actual utilization is consistently well below what’s provisioned.

Right-sizing isn’t just about picking smaller instances. It also means checking whether the instance family fits the workload — compute-optimized vs. memory-optimized vs. general-purpose — and whether Graviton-based instances give you better price-performance.

For databases, we look at whether RDS instance classes are appropriately sized, whether read replicas are justified by query patterns, and whether Aurora Serverless makes more sense for variable workloads.

Commitment Strategy

Reserved Instances and Savings Plans can save you 30-60% compared to on-demand pricing, but only if the commitments match how you actually use AWS. We analyze your historical usage data and growth projections to recommend the right mix of commitment types, terms, and coverage levels.

We weigh Compute Savings Plans vs. EC2 Instance Savings Plans, one-year vs. three-year terms, and all-upfront vs. no-upfront payment options. The goal is maximum discount with minimum lock-in — so you keep the flexibility to change instance types, regions, and architectures as your needs evolve.

Tagging Governance and Cost Allocation

If your resources aren’t tagged, you can’t allocate costs. Full stop.

We design and implement a tagging strategy that maps every resource to an owner, a project, an environment, and a cost center. We enforce it through AWS Config rules and SCPs so untagged resources get flagged — or blocked from being created in the first place.

With tagging in place, we set up Cost Explorer dashboards and AWS Budgets alerts so spending is visible to the right people in real time. Teams see their own costs, finance can run chargeback reports, and anomalies trigger notifications before they become surprises.

Scheduling and Lifecycle Policies

Non-production environments that don’t need to run around the clock get scheduled start/stop automation. Storage that doesn’t need to live in the highest-performance tier gets moved to the right class using S3 Intelligent-Tiering or lifecycle policies. Snapshots and backups get retention rules so they don’t pile up forever.

What You Get

• Spend analysis report — a detailed breakdown of your current AWS costs by service, account, region, and resource, with identified waste and optimization opportunities
• Right-sizing recommendations — specific instance and database changes with projected savings for each one
• Commitment strategy plan — a Savings Plans and Reserved Instance purchasing recommendation based on your usage patterns and growth forecast
• Tagging governance framework — a tagging taxonomy, enforcement policies, and implementation plan for consistent cost allocation
• Budget and alerting configuration — AWS Budgets set up for each team or project with anomaly detection and threshold notifications
• Cost optimization runbook — a documented monthly review process your team can follow to keep savings on track and catch new waste as it appears

AWS Services We Use

• AWS Cost Explorer — spend analysis, trend identification, and savings opportunity discovery
• AWS Compute Optimizer — machine learning-driven right-sizing recommendations for EC2, EBS, and Lambda
• AWS Budgets — cost and usage budgets with alerting and automated actions
• AWS Savings Plans — flexible commitment-based pricing for compute workloads
• AWS Cost Anomaly Detection — automated monitoring for unexpected spending increases
• AWS Config — tagging compliance enforcement and resource configuration auditing