Cloud billing is opaque by design — providers present granular line items that are hard to connect to actual business value. A team provisioning a test environment forgets to shut it down. An application gets over-provisioned "to be safe" and stays that way. Data transfer charges accumulate unnoticed. Reserved Instance coverage drifts as the workload mix changes. None of these are disasters individually, but they compound.

Cloud cost optimization isn't a one-time exercise — it's an operational discipline called FinOps. The FinOps framework describes three phases: Inform (make costs visible), Optimize (act on what you see), and Operate (make optimization continuous). We help organizations build all three, not just generate a report of what to cut this quarter.

Where cloud waste comes from

Oversized instances

The most common source of waste. Instances provisioned at peak capacity or with a large safety margin that never get right-sized after initial deployment. CPU utilization below 20% on production workloads is common. We analyze actual utilization metrics over a representative period (at least 2-4 weeks) before recommending instance changes — not just current utilization, which may not capture workload peaks.

Idle and unattached resources

Stopped EC2 instances still incur EBS charges. Unattached EBS volumes, orphaned snapshots, unused Elastic IPs, and idle load balancers continue billing without doing anything useful. These accumulate over time in accounts where no one is regularly reviewing resource inventory. We identify and document every idle resource for review and cleanup — with owner identification so resources aren't deleted without checking with the team that created them.

On-demand pricing for stable workloads

On-demand pricing is the most expensive way to run workloads that run continuously. AWS Reserved Instances and Savings Plans, Azure Reserved VM Instances, and GCP Committed Use Discounts offer 30-72% discounts in exchange for 1-3 year commitments. Organizations that haven't purchased commitments for their stable baseline workloads are paying the full on-demand rate for things that will run regardless. We analyze utilization patterns, identify commitment opportunities, and model the breakeven and savings for each option.

Data transfer and egress charges

Data transfer is often the most surprising item on cloud bills. Egress from cloud to the internet, cross-region data transfer, and cross-AZ traffic within a region all generate charges. Architecture decisions — where you place your cache, how services communicate, where backups are stored — have direct cost implications that aren't always apparent when the architecture is designed. We identify high-transfer patterns and recommend changes that reduce them where feasible.

Non-production environments running 24/7

Development, staging, and test environments often don't need to run continuously. Scheduling them to stop outside business hours — typically nights and weekends — can reduce their cost by 60-70% without affecting anyone who actually uses them. We identify environments where scheduled shutdown is feasible and implement automation to start and stop them on schedule.

Missing tagging and cost allocation

Without consistent resource tagging, you can see what your cloud costs but not why. You can't allocate costs to specific teams, products, or projects. You can't identify which workloads are driving cost growth. Tagging standards — consistently applied and enforced — are the prerequisite for meaningful cost management. We define tagging standards, implement enforcement via policy (AWS Service Control Policies, Azure Policy), and build cost allocation reports from the tag structure.

How a cost optimization engagement works

1

Billing analysis and cost baseline

Pull 3-6 months of billing data and build a clear picture of cost by service, region, account, and tag. Identify the top cost drivers and where costs are growing fastest. This is the diagnostic phase — understanding the shape of spend before making any recommendations.

2

Resource inventory and utilization review

Inventory all running resources and pull utilization metrics. Identify oversized instances, idle resources, and unattached volumes. Cross-reference with tags (or absence of tags) to identify ownership before making any deletion recommendations.

3

Commitment coverage analysis

Analyze what proportion of baseline compute is covered by Reserved Instances or Savings Plans. Model the optimal commitment portfolio given your current utilization patterns and projected growth, with specific purchase recommendations and expected savings.

4

Governance implementation

Define and implement tagging standards, budget alerts, and cost allocation structure. This is the preventive layer — making it harder for waste to accumulate unnoticed going forward.

5

Remediation and reporting

Execute or guide execution of quick-win optimizations. Establish monthly cost reporting so the gains are visible and drift is caught early. The goal is a cost management process your team can run, not ongoing dependence on external review.

What you receive

Cost optimization report

Itemized findings with estimated savings for each optimization. Grouped into immediate actions (days), short-term (weeks), and governance improvements (ongoing).

Reserved Instance and Savings Plan recommendations

Specific commitment recommendations with modeled savings, break-even analysis, and guidance on commitment type (1yr vs 3yr, full upfront vs monthly).

Tagging standard and enforcement policy

Tag taxonomy for cost allocation, with enforcement via cloud-native policy. Required tags, optional tags, and how to handle legacy untagged resources.

Budget alerts configuration

Budget thresholds set at account, service, and tag level with alerting to the right people. Anomaly detection configured to flag unexpected cost spikes.

Cost allocation dashboard

Monthly cost view by team, project, or product using the implemented tag structure. Showback reporting so teams see what they're consuming.

Non-production scheduling automation

Automated start/stop schedules for dev and test environments with team-configurable overrides for when environments need to run outside normal hours.

Who this is for

→ Organizations whose cloud spend has grown faster than expected and leadership wants to understand why
→ Engineering teams that know they have waste but don't have the time or billing expertise to identify and address it systematically
→ Companies running multiple accounts or subscriptions with no consolidated view of total cloud spend
→ Businesses with mostly on-demand compute that want to model Reserved Instance or Savings Plan purchases
→ Organizations preparing for budget cycles that need defensible cloud cost projections
→ Any organization that wants cost allocation by team or project but doesn't currently have the tagging structure to enable it

Find out what you're actually spending and why

Start with a free consultation. Share your rough cloud spend and we'll tell you where we typically find the most waste for environments like yours.

Schedule Free Consultation

Cloud Cost Optimization