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Stop overspending on cloud

Expert cost optimization across AWS, Azure, and GCP. We find waste, map issues to owners and infrastructure code, right-size resources, and implement policy-aware remediation so savings stick.

Typical target: 20%–40% savings opportunities where the environment has enough waste, idle resources, lifecycle gaps, commitment gaps, or governance issues to address. Ongoing monitoring keeps costs under control.

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Deep cost assessment

Analyze workloads, usage patterns, and pricing to find concrete savings without sacrificing reliability.

Right-sizing and commitments

Identify over-provisioned resources and design reserved instance or savings plans that match reality.

Reviewable remediation workflow

Prioritized findings include owner context, expected savings, rollout notes, and rollback guidance before changes are made.

Service playbook

From problem to operating evidence

Main content is structured like a case study: context first, scoped work next, then the operating changes and evidence a team can use after handoff.

Service briefDecision supportPrerequisites and discovery inputsWhat we deliverOptimization Process

Cloud cost optimization is the consulting path for teams that need credible savings opportunities and a governance model that keeps spend under control. Assistance reviews AWS, Azure, and GCP usage, idle resources, rightsizing, lifecycle gaps, commitment coverage, tagging, showback, and ownership gaps, then turns approved fixes into reviewable work. Actual outcomes depend on workload mix, baseline waste, commitment coverage, governance maturity, and how much change your team can safely implement.

Case-study lens

Scoped

Problem, responsibility, and handoff boundaries before implementation.

Evidence

Dashboards, runbooks, reviews, and operating records over borrowed logos.

Outcomes

Conservative summaries focused on observable operational improvement.

EvidenceSection 01

Decision support

Runbooks, dashboards, reviews, and handoff material make the work auditable.

Cost optimization is not just deleting resources. We help decide which changes are safe, which require architecture work, and which should become FinOps governance.

DecisionHow we support it
Quick win or engineering projectSeparate low-risk cleanup from changes that need testing, rollout, or application owner approval
Rightsize or redesignIdentify when a smaller resource is enough and when architecture, caching, storage, or data flow should change
Commitment or flexibilityCompare savings plans, reservations, committed use discounts, and on-demand flexibility against real usage
Central action or team ownershipDecide which fixes can be implemented by platform teams and which require workload owner sign-off
One-time sprint or ongoing FinOpsDetermine whether the problem is waste cleanup, governance, forecasting, or financial operating model maturity
Operating modelSection 02

Prerequisites and discovery inputs

Responsibilities, response paths, and technical changes are made explicit before work starts.

We can begin with billing exports and read-only cloud access. The best findings come from combining cost, usage, ownership, and deployment context.

  • billing exports or cloud-native cost access for AWS, Azure, GCP, or other providers
  • account, subscription, project, environment, region, and service inventory
  • tags, labels, cost centers, team ownership, and application mapping
  • utilization metrics for compute, database, storage, Kubernetes, networking, and managed services
  • existing reservations, savings plans, committed use discounts, enterprise agreements, or credits
  • known reliability constraints, performance baselines, SLOs, and change windows
  • infrastructure code repositories and deployment workflows where remediation should be reviewed
  • current budgets, forecasts, anomaly alerts, and reporting cadence
ScopeSection 03

What we deliver

The work is broken into visible capabilities, acceptance points, and handoff artifacts.

What changes

Instance Rightsizing

  • Workload Analysis: Match instances to actual CPU/memory requirements
  • Instance Family Optimization: Choose the most cost-effective instance types
  • Burst vs. Steady State: Optimize for different workload patterns
  • Performance Monitoring: Continuously monitor and adjust sizing

What changes

Reserved & Spot Instances

  • RI Planning: Analyze usage patterns and plan optimal RI purchases
  • Blended Rate Optimization: Combine RIs, on-demand, and spot instances
  • Spot Instance Strategies: Implement fault-tolerant architectures for spot savings
  • Market Timing: Purchase RIs at optimal times for maximum discounts

What changes

Storage & Data Optimization

  • Storage Tiering: Implement intelligent storage class migration
  • Lifecycle Policies: Automate data archival and deletion
  • Database Optimization: Right-size databases and implement read replicas
  • Backup Strategy: Optimize backup retention and storage costs

What changes

Network & CDN Optimization

  • Data Transfer Costs: Minimize egress charges through architecture changes
  • CDN Implementation: Reduce bandwidth costs and improve performance
  • Region Optimization: Place resources in cost-effective regions
  • Peering Connections: Use direct connections for reduced network costs
Operating modelSection 04

Optimization Process

The section clarifies how production responsibilities change once the service is in place.

  1. Cost Discovery
  • Analyze current spending patterns
  • Identify waste and optimization opportunities
  • Map costs to teams and applications
  1. Optimization Planning
  • Develop comprehensive optimization strategy
  • Prioritize quick wins and long-term initiatives
  • Calculate potential savings and ROI
  • Map findings to owning teams, applications, and infrastructure code where possible
  1. Implementation
  • Execute optimization measures
  • Implement automation and policies
  • Configure monitoring and alerting
  • Package higher-risk fixes as reviewable changes with rollout steps and rollback guidance
  1. Continuous Improvement
  • Monthly cost reviews
  • Ongoing optimization
  • Governance updates
Operating modelSection 05

Example remediation runbooks

Responsibilities, response paths, and technical changes are made explicit before work starts.

Operating example

Idle resource cleanup

  1. Confirm the resource owner, environment, age, recent usage, and dependency signals.
  2. Snapshot or back up state when the service type requires it.
  3. Stop before delete where possible and monitor for impact during the agreed window.
  4. Delete only after owner approval or after the documented retention window expires.
  5. Record realized savings and update automation or tagging rules to prevent recurrence.

Operating example

Rightsizing production compute

  1. Review utilization across a representative period, including peak windows and release events.
  2. Confirm workload constraints with the owning team: latency, memory pressure, batch windows, and autoscaling behavior.
  3. Propose the target size, expected savings, rollout plan, and rollback trigger.
  4. Apply through the normal infrastructure review path.
  5. Monitor performance, error rate, saturation, and actual spend after rollout.

Operating example

Commitment purchase review

  1. Identify stable baseline usage after excluding experiments, migrations, and known decommissions.
  2. Compare commitment terms, coverage, utilization risk, cash flow, and flexibility needs.
  3. Confirm finance and engineering approval thresholds.
  4. Purchase only the portion backed by durable workloads.
  5. Review coverage monthly and before major migrations or architecture changes.
OutcomeSection 06

Governance cadence

Expected changes are framed as practical operating improvements, not unsupported guarantees.

CadenceFocusOutput
First 1-2 weeksDiscovery, baseline, owner mapping, and quick-win validationCost baseline and prioritized findings
First 30 daysLow-risk cleanup, rightsizing proposals, budget/anomaly setupEarly savings report and remediation backlog
MonthlySpend review, anomalies, owner follow-up, commitment coverage, and new wasteCost optimization report
QuarterlyArchitecture cost review, forecasting, commitment planning, and policy updatesCost roadmap and governance recommendations
ScopeSection 07

Deliverables

The work is broken into visible capabilities, acceptance points, and handoff artifacts.

  • current cost baseline with provider, account, service, region, environment, and owner breakdown where data allows
  • prioritized savings backlog with expected impact, risk, effort, owner, and approval path
  • remediation tickets or pull requests for approved changes where infrastructure code is available
  • budget, forecast, and anomaly alert recommendations
  • tagging, label, and owner-mapping improvement plan
  • commitment planning notes for reservations, savings plans, or committed use discounts
  • before-and-after savings summary for completed work
Operating modelSection 08

Boundaries

Responsibilities, response paths, and technical changes are made explicit before work starts.

We do not make risky production changes without owner approval, rollback guidance, and monitoring. Cost Optimization is focused on credible savings and governance improvements; broader financial accountability belongs in FinOps, while landing zone, network, or migration-heavy remediation belongs in Cloud Infrastructure.

OutcomeSection 09

Tools & Technologies

Expected changes are framed as practical operating improvements, not unsupported guarantees.

  • Cloud Native Tools: AWS Cost Explorer, GCP Cost Management, Azure Cost Management
  • Third-party Platforms: Cloudability, CloudHealth, Apptio, CloudZero
  • Monitoring Solutions: Custom cost dashboards, Prometheus exporters
  • Automation Scripts: Resource cleanup, rightsizing recommendations, policy enforcement
EvidenceSection 10

Common Savings Opportunities

Runbooks, dashboards, reviews, and handoff material make the work auditable.

What changes

Quick Wins (30 days)

  • Unused resource cleanup
  • Instance rightsizing
  • Storage tiering
  • Simple tagging improvements

What changes

Medium-term (90 days)

  • Reserved instance planning
  • Spot instance adoption
  • Storage lifecycle policies
  • Network architecture optimization

What changes

Long-term (6 months)

  • Automated cost governance
  • FinOps process implementation
  • Chargeback and showback systems
  • Reviewable remediation backlog tied to owners and infrastructure code
  • Multi-cloud cost optimization
EvidenceSection 11

Success metrics

Reliability signals are treated as decision evidence, not dashboards for their own sake.

MetricTypical result
Cost reduction opportunitiesTypical target of 20%–40% where current waste, commitment gaps, or governance issues justify it
Resource waste eliminated30%+
Cost anomalies caught90%+
Time to first savings30 days

We prioritize low-effort, high-impact changes—unused resources, rightsizing, storage tiering—so you see savings within 30 days. Then we tackle reserved instances and architectural changes for long-term gains.

Next stepSection 12

Getting started

Decision points and common questions are made explicit so follow-up work is scoped cleanly.

Wondering how much you could save? We'll analyze your cloud spend and provide a free assessment with concrete recommendations.

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Next stepSection 13

Related resources

Decision points and common questions are made explicit so follow-up work is scoped cleanly.

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Pricing

Flexible scopes available. if you need custom terms or bundled service pricing.

Hourly rate
€90/hr

Minimum engagement: 20 hours

Cloud cost analysis and optimization across AWS, Azure, and GCP. We identify waste, rightsize resources, and execute typical 20%–40% savings opportunities where the environment supports them.

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