Jon Price

AWS Consultant & DevOps Specialist. 15+ years enterprise infrastructure and cloud consulting.

16 minute read

FinOps implementation on AWS delivers 40-60% cost reductions through systematic optimization, automated governance, and engineering culture transformation. This comprehensive guide explores a real case study where we reduced a SaaS company’s AWS infrastructure costs from $4M to $1.7M annually—a $2.3M savings achieved through proven FinOps methodologies.

For CFOs, VPs of Engineering, and cloud practitioners, uncontrolled AWS costs represent both a financial drain and a symptom of deeper organizational challenges around resource efficiency, accountability, and governance. FinOps provides the framework to transform cloud spend from an unmanageable cost center into an optimized, predictable business investment.

The $2.3M Cost Optimization Case Study

Client Background and Initial State

Company Profile:

  • Series C SaaS company with 200 employees
  • 500K+ monthly active users across multi-tenant platform
  • $4.0M annual AWS infrastructure spend (pre-optimization)
  • 3-person DevOps team managing 500+ EC2 instances, RDS databases, and supporting services
  • Experiencing 30% month-over-month AWS bill increases without corresponding revenue growth

Pain Points Driving Engagement:

  • Unpredictable costs: Monthly AWS bills varying by $100K-150K without clear drivers
  • Lack of visibility: No cost allocation by customer, team, or environment
  • Engineering disconnect: Developers unaware of cost implications of architectural decisions
  • CFO pressure: Board demanding explanation for cloud cost overruns
  • Competitive threat: Lower-margin pricing pressure from competitors with better unit economics

Discovery Phase: Finding the $2.3M Opportunity

Week 1-2: Cost Analysis and Baseline Establishment

Our comprehensive AWS cost audit revealed these findings:

Compute Waste (45% of total spend, $1.8M annually):

  • EC2 right-sizing opportunity: 60% of instances over-provisioned by 2-4x
    • Average CPU utilization: 15-25% across production workloads
    • Memory utilization: 30-40% average across application tiers
    • Potential savings: $720K annually through right-sizing
  • Reserved Instance gap: 70% of steady-state compute running at on-demand rates
    • 3-year RI commitment could save 40-50% on stable workloads
    • Potential savings: $380K annually through RI/Savings Plans
  • Zombie resources: 80+ stopped EC2 instances still incurring EBS volume costs
    • Old development environments never decommissioned
    • Immediate savings: $45K annually through cleanup

Storage Inefficiency (20% of spend, $800K annually):

  • EBS over-provisioning: Storage volumes sized at peak but rarely utilized
    • Average storage utilization: 40% across all volumes
    • Potential savings: $180K annually through volume optimization
  • S3 lifecycle management: No lifecycle policies for any S3 buckets
    • 3+ years of log data in S3 Standard storage class
    • Potential savings: $240K annually through Intelligent-Tiering and Glacier
  • RDS storage waste: Provisioned IOPS allocated but not utilized
    • Potential savings: $95K annually through GP3 migration and right-sizing

Data Transfer Costs (15% of spend, $600K annually):

  • Inefficient inter-AZ traffic: Application architecture causing excessive cross-AZ data transfer
    • Potential savings: $180K annually through architecture optimization
  • NAT Gateway overuse: Single NAT Gateway per AZ without VPC endpoints
    • Potential savings: $120K annually through VPC endpoints and optimization
  • CloudFront underutilization: Static assets served directly from EC2/S3
    • Potential savings: $85K annually through CDN optimization

Database Costs (15% of spend, $600K annually):

  • RDS right-sizing: Database instances sized for peak load 24/7
    • Potential savings: $150K annually through Aurora Serverless for dev/staging
  • Read replica overprovisioning: 12 read replicas when 4-6 sufficient
    • Potential savings: $180K annually through replica optimization

Other Services (5% of spend, $200K annually):

  • Load balancer consolidation: 40+ Application Load Balancers for 15 applications
    • Potential savings: $65K annually through consolidation
  • Development environment waste: Dev/staging at 50% production capacity but used 10% of time
    • Potential savings: $85K annually through automated start/stop schedules

Total Identified Opportunity: $2.58M annually (64.5% of current spend)

Implementation Timeline and Methodology

Phase 1: Quick Wins (Months 1-2)

Goal: Generate $400K-500K annual savings to fund remaining optimization work

Quick Win #1: Zombie Resource Cleanup

  • Identified 80+ stopped EC2 instances with attached EBS volumes
  • Snapshot for disaster recovery, terminate unused resources
  • Immediate monthly savings: $3,750 ($45K annually)
  • Implementation time: 2 days

Quick Win #2: Reserved Instance Initial Purchase

  • Analyzed 12-month usage patterns for steady-state workloads
  • Purchased 1-year Convertible RIs for 30% of on-demand compute
  • Monthly savings: $15,000 ($180K annually)
  • Implementation time: 3 days (analysis + purchase)

Quick Win #3: S3 Lifecycle Policies

  • Implemented Intelligent-Tiering for active data
  • Transitioned logs older than 90 days to Glacier
  • Deleted logs older than 7 years (compliance retention met)
  • Monthly savings: $20,000 ($240K annually)
  • Implementation time: 1 week

Quick Win #4: Development Environment Scheduling

  • Automated start/stop for dev/staging environments (8am-8pm weekdays only)
  • 72% reduction in dev/staging runtime hours
  • Monthly savings: $7,100 ($85K annually)
  • Implementation time: 3 days (Lambda scheduler deployment)

Phase 1 Results: $550K annual run-rate savings in 8 weeks

Phase 2: Structural Optimization (Months 3-6)

Compute Right-Sizing Project:

  • Deployed AWS Compute Optimizer across all accounts
  • Analyzed 30-day performance metrics for all EC2 instances
  • Implemented phased right-sizing: staging → production (A/B test)
  • Result: Average instance size reduction from m5.2xlarge to m5.xlarge
  • Monthly savings: $60,000 ($720K annually)
  • Implementation time: 12 weeks with rigorous testing

Reserved Instance Strategy Expansion:

  • Extended RI coverage to 70% of steady-state workloads
  • Balanced 1-year and 3-year commitments (60% / 40%)
  • Implemented monthly RI utilization reviews
  • Incremental monthly savings: $16,700 ($200K annually, $380K total with Phase 1)
  • Implementation time: 8 weeks

Database Optimization Initiative:

  • Migrated dev/staging RDS to Aurora Serverless v2 (auto-pause enabled)
  • Right-sized production RDS instances based on performance metrics
  • Optimized read replica count (12 → 6 with connection pooling)
  • Monthly savings: $27,500 ($330K annually)
  • Implementation time: 10 weeks

Network Architecture Redesign:

  • Deployed VPC endpoints for S3, DynamoDB, and other AWS services
  • Consolidated NAT Gateways (3 per VPC → 1 per VPC with high availability)
  • Optimized data flow to reduce inter-AZ traffic by 60%
  • Monthly savings: $31,700 ($380K annually)
  • Implementation time: 14 weeks (application architecture changes required)

Phase 2 Results: Additional $1.63M annual savings (Total: $2.18M)

Phase 3: Governance and Continuous Optimization (Months 7-12)

FinOps Culture Transformation:

  • Implemented cost allocation tagging strategy (environment, team, application, customer)
  • Deployed real-time cost dashboards visible to all engineering teams
  • Established monthly cost review meetings with engineering leadership
  • Created cost-conscious architectural decision framework
  • Result: 15% additional optimization from team-driven initiatives
  • Monthly savings: $10,000 ($120K annually from culture change)

Automated Cost Anomaly Detection:

  • Deployed AWS Cost Anomaly Detection with custom thresholds
  • Implemented automated alerting and investigation workflows
  • Created playbooks for common cost spike scenarios
  • Result: Prevented $150K+ in unexpected costs in first 6 months

Total Optimization Achievement: $2.3M annual savings (58% reduction from $4.0M baseline)

FinOps Implementation Framework for AWS

The FinOps Operating Model

Core Principles:

  1. Collaboration: Finance, engineering, and business teams working together on cost optimization
  2. Business Value: Optimize costs without sacrificing performance or customer experience
  3. Accountability: Engineers make cost-aware decisions with real-time feedback
  4. Continuous Improvement: Cost optimization as ongoing practice, not one-time project

Organizational Structure and Responsibilities

Centralized FinOps Team (Small to Mid-Market):

  • FinOps Lead: Senior engineer with financial acumen and executive communication skills
  • Cloud Cost Analyst: Data analysis, cost modeling, and optimization recommendations
  • Platform Engineer: Automation, tooling, and governance implementation
  • Reporting: Typically to VP Engineering or CFO depending on organization

Federated FinOps Model (Enterprise):

  • Central FinOps Center of Excellence: Standards, tools, best practices, reporting
  • Embedded FinOps Practitioners: Part of each product or platform team
  • Executive Steering Committee: Monthly cost review and strategic decision-making
  • Cloud Business Office: Cross-functional team with finance, engineering, procurement

Key Roles and Responsibilities:

Engineers:

  • Design cost-efficient architectures and select appropriate instance types
  • Implement automated scaling policies and resource lifecycle management
  • Tag resources according to cost allocation taxonomy
  • Participate in monthly cost reviews and optimization initiatives

Engineering Managers:

  • Set team-level cost budgets and track against targets
  • Prioritize cost optimization work alongside feature development
  • Foster cost-conscious culture and architectural decision-making
  • Escalate cost anomalies and architectural inefficiencies

FinOps Team:

  • Provide cost visibility through dashboards and reports
  • Identify optimization opportunities and calculate business impact
  • Develop automation for cost governance and optimization
  • Facilitate cross-functional collaboration on cost initiatives

Finance Team:

  • Establish cloud cost budgets and forecasting methodologies
  • Implement chargeback or showback models for business units
  • Support procurement of Reserved Instances and Savings Plans
  • Report cloud financial metrics to executive leadership and board

Cost Allocation and Chargeback Strategies

Tagging Strategy Implementation:

Required Tags (Enforced via AWS Config Rules):

  • Environment: production, staging, development, test
  • Application: Application or service name
  • Team: Owning engineering team or business unit
  • CostCenter: Finance cost center for chargeback
  • Customer: For multi-tenant architectures (when applicable)

Enforcement Mechanisms:

  • AWS Config rules deny resource creation without required tags
  • Daily compliance reports of untagged resources
  • Automated remediation for common resource types
  • Quarterly tag coverage reviews (target: 95%+ coverage)

Chargeback vs. Showback Models:

Showback (Informational, No Budget Transfer):

  • Use case: Early FinOps maturity, building cost awareness
  • Implementation: Monthly cost reports by team with trends
  • Benefit: Increases cost visibility without complex financial processes
  • Drawback: Limited accountability without budget responsibility

Chargeback (Budget Transfers from Business Units):

  • Use case: Mature FinOps organizations with clear cost centers
  • Implementation: Monthly AWS costs allocated to business unit budgets
  • Benefit: Strong accountability and cost-conscious decision-making
  • Drawback: Requires sophisticated cost allocation logic and finance processes

Hybrid Approach (Recommended):

  • Infrastructure platform costs: Showback (shared services)
  • Application workload costs: Chargeback (direct business unit costs)
  • Central FinOps team: Overhead allocation across all business units

Automated Cost Monitoring and Alerting

Real-Time Cost Visibility:

  • AWS Cost Explorer: Daily cost and usage analysis with filtering and grouping
  • AWS Cost and Usage Reports (CUR): Detailed billing data exported to S3 for custom analytics
  • Third-party tools: CloudHealth, Cloudability, or Vantage for enhanced visualization
  • Custom dashboards: CloudWatch dashboards, Grafana, or Tableau for stakeholder-specific views

Multi-Level Alerting Strategy:

Budget Alerts (Monthly Thresholds):

  • 80% of monthly budget: Warning to engineering team
  • 90% of monthly budget: Alert to engineering leadership
  • 100% of monthly budget: Escalation to VP Engineering and CFO
  • Implementation: AWS Budgets with SNS notifications and email

Anomaly Detection (Unusual Spend Patterns):

  • AWS Cost Anomaly Detection with machine learning-based alerts
  • Custom thresholds by service (e.g., EC2 >$5K daily increase, S3 >$2K)
  • Automated incident creation in ticketing system for investigation
  • Slack/Teams integration for real-time team notifications

Proactive Monitoring (Leading Indicators):

  • EC2 instance count changes (new instances created)
  • Large EBS volume provisioning (>1TB)
  • Data transfer spikes (>100GB increase)
  • New service adoption without approval

Cost Governance Automation:

  • AWS Service Control Policies (SCPs) limiting expensive instance types
  • Lambda functions terminating untagged resources after 7 days
  • Automated right-sizing recommendations from AWS Compute Optimizer
  • Scheduled reports on optimization opportunities (weekly to FinOps team)

Technical Implementation Strategies

Right-Sizing Methodologies and Automation

Right-Sizing Assessment Process:

Step 1: Performance Data Collection

  • Enable AWS Compute Optimizer for automated recommendations
  • Collect 30 days of CloudWatch metrics minimum (90 days preferred)
  • Metrics focus: CPU, memory, network, disk I/O, IOPS
  • Application-level metrics: Request latency, error rates, throughput

Step 2: Recommendation Analysis

  • Compute Optimizer recommendations: Hourly savings estimates
  • Account for headroom: Target 60-70% utilization, not 90%+
  • Consider workload patterns: Steady-state vs. burst requirements
  • Test in non-production first: Validate performance before production change

Step 3: Implementation and Validation

  • Staging environment: Right-size and load test
  • Production rollout: Canary deployment pattern (10% → 50% → 100%)
  • Performance monitoring: Track P50, P95, P99 latency metrics
  • Rollback plan: Previous instance type available for quick revert

Automated Right-Sizing Workflow:

CloudWatch Metrics → Compute Optimizer → Lambda Function
  → SNS Notification to Team → JIRA Ticket Creation
  → Manual Approval → Terraform/CloudFormation Update
  → CI/CD Pipeline → Gradual Rollout → Validation

Average Savings from Right-Sizing: 25-40% of compute costs

Reserved Instance and Savings Plan Optimization

RI vs. Savings Plans Decision Framework:

Use Standard Reserved Instances When:

  • Highly predictable, stable workloads (database servers, always-on services)
  • Specific instance type and size requirements unlikely to change
  • Maximum discount priority (up to 72% savings vs. on-demand)
  • Regional commitment acceptable (not multi-region flexibility needed)

Use Compute Savings Plans When:

  • Flexibility desired across instance families, sizes, and regions
  • Workload patterns stable but architecture may evolve
  • Balance of savings (up to 66%) and flexibility
  • Serverless workloads included (Lambda, Fargate)

Use EC2 Instance Savings Plans When:

  • Commitment to specific instance family but size flexibility needed
  • Regional flexibility desired
  • Higher discount than Compute Savings Plans (up to 72%)
  • No need for Lambda/Fargate coverage

RI/Savings Plan Procurement Strategy:

Coverage Target: 60-80% of Steady-State Workload

  • Analyze 12-month usage patterns for baseline demand
  • Reserve 70% of baseline with commitments
  • Use on-demand or Spot for burst capacity above baseline
  • Leave 20-30% on-demand for flexibility and growth

Commitment Term Mix:

  • 1-Year Commitments: 60% of total coverage (flexibility for growth/change)
  • 3-Year Commitments: 40% of total coverage (maximum savings for stable workload)
  • Convertible RIs: Recommended for 1-year commitments (instance type flexibility)
  • Standard RIs: Consider for 3-year if infrastructure stable (higher discount)

Monthly Optimization Review:

  • RI utilization tracking: Target 95%+ utilization
  • RI coverage analysis: Expand coverage as workload grows
  • Unused RI investigation: Workload decommissioned or right-sized below RI
  • Savings Plan recommendations: New services or workload patterns

ROI Calculation:

  • 1-year Convertible RI: ~40% savings, break-even at 7-8 months
  • 3-year Convertible RI: ~55% savings, break-even at 13-15 months
  • Investment risk: Mitigated by Convertible exchange and RI Marketplace

Spot Instance Strategies for Non-Critical Workloads

Spot Instance Use Cases:

Excellent Spot Candidates (70-90% cost savings):

  • Batch processing jobs (ETL, data processing, video encoding)
  • CI/CD build agents (stateless, ephemeral)
  • Development and testing environments
  • Machine learning training jobs (checkpointed)
  • Web application auto-scaling groups (with on-demand baseline)

Poor Spot Candidates (Avoid Spot):

  • Stateful applications without persistence strategy
  • Real-time user-facing services requiring guaranteed capacity
  • Databases or caching layers (use RDS/ElastiCache instead)
  • Single-instance services without redundancy

Spot Implementation Best Practices:

Diversification Strategy:

  • Request 4-5 instance types across multiple families (c5, c5a, c6i, m5, m5a)
  • Distribute across multiple availability zones
  • Use Spot Fleet or Auto Scaling Groups with mixed instance policy
  • Result: 95%+ capacity availability despite occasional interruptions

Interruption Handling:

  • Monitor EC2 Spot interruption notices (2-minute warning)
  • Graceful shutdown: Save work state, drain connections
  • Automatic failover: Load balancer removes instance from rotation
  • Queue-based processing: Message remains in queue if processing incomplete

Capacity Optimization:

  • Use Spot Placement Score to find optimal instance type combinations
  • Attribute-based instance type selection (4 vCPU, 16GB RAM, x86)
  • Capacity-optimized allocation strategy (AWS selects lowest interruption risk pools)
  • Price-capacity-optimized for cost and stability balance

Real-World Spot Results:

  • Typical savings: 70-85% vs. on-demand pricing
  • Interruption rate: <5% with proper diversification
  • Best for: 30-50% of total compute when architected correctly

Data Lifecycle Management and Storage Optimization

S3 Storage Optimization:

Intelligent-Tiering (Automatic Cost Optimization):

  • Use case: Unpredictable access patterns or unknown workloads
  • How it works: AWS automatically moves objects between tiers based on access
  • Tiers: Frequent → Infrequent (30 days) → Archive (90 days) → Deep Archive (180 days)
  • Cost: $0.0025 per 1,000 objects per month (monitoring fee)
  • Savings: 40-95% depending on access patterns
  • Recommendation: Default choice for most S3 workloads

Custom Lifecycle Policies:

  • Use case: Predictable data access patterns (logs, backups, archives)
  • Example policy: Logs to IA after 30 days → Glacier after 90 days → Delete after 7 years
  • Transition rules: Balance cost savings against retrieval needs
  • Deletion policies: Automated cleanup for temporary data

S3 Storage Classes Selection Guide:

  • Standard: Frequent access, millisecond retrieval (default, most expensive)
  • Intelligent-Tiering: Unknown access patterns (best general-purpose choice)
  • Standard-IA: Infrequent access but instant retrieval when needed
  • Glacier Instant Retrieval: Archive with millisecond retrieval (90+ day retention)
  • Glacier Flexible Retrieval: Archive with minutes-hours retrieval (compliance)
  • Glacier Deep Archive: Lowest cost archive for 7+ year retention (12-hour retrieval)

EBS Volume Optimization:

Volume Type Selection:

  • GP3 (General Purpose SSD): Default choice, 20% cheaper than GP2
  • GP2: Legacy, upgrade all volumes to GP3 for immediate 20% savings
  • IO2: High-performance databases requiring >16K IOPS per volume
  • ST1 (Throughput Optimized HDD): Big data, log processing (70% cheaper than SSD)
  • SC1 (Cold HDD): Infrequently accessed data (80% cheaper than SSD)

Snapshot Optimization:

  • Delete orphaned snapshots (volume deleted but snapshots remain)
  • Migrate snapshots to Glacier for long-term retention
  • Use Amazon Data Lifecycle Manager for automated snapshot scheduling
  • Cross-region snapshot replication only where disaster recovery requires

Database Storage Optimization:

  • Migrate from Provisioned IOPS to GP3 where performance permits (50%+ savings)
  • Aurora I/O-Optimized for high I/O workloads (predictable pricing)
  • Enable storage autoscaling to prevent over-provisioning
  • Review slow query logs: Inefficient queries drive unnecessary storage I/O costs

Ongoing Governance and Continuous Optimization

Monthly Cost Review Process

Standard Operating Procedure for FinOps Team:

Week 1: Cost Data Collection and Analysis

  • Export cost and usage data for previous month
  • Calculate month-over-month and year-over-year trends
  • Identify cost anomalies and significant changes (>10%)
  • Prepare cost breakdown by service, environment, team, and application

Week 2: Optimization Opportunity Identification

  • Review Compute Optimizer recommendations (new right-sizing opportunities)
  • Analyze RI/Savings Plan utilization and coverage gaps
  • Identify zombie resources and unused assets
  • Calculate potential savings for each opportunity

Week 3: Stakeholder Review Meetings

  • Engineering leadership: Overall cost trends and optimization priorities
  • Individual team reviews: Team-specific cost deep dives
  • Finance review: Budget variance analysis and forecasting
  • Executive summary: Key metrics for C-level stakeholders

Week 4: Implementation Planning and Execution

  • Prioritize optimization initiatives by ROI and implementation effort
  • Create implementation tickets and assign to engineering teams
  • Track progress on ongoing optimization initiatives
  • Document lessons learned and best practices

Key Metrics Tracked:

Cost Efficiency Metrics:

  • Cost per customer/user/transaction (unit economics)
  • Infrastructure cost as percentage of revenue
  • Cost trend: Month-over-month and year-over-year growth
  • Budget variance: Actual vs. planned spending

Optimization Metrics:

  • RI/Savings Plan coverage percentage (target: 70-80%)
  • RI/Savings Plan utilization rate (target: 95%+)
  • Right-sizing opportunity value
  • Monthly optimization savings realized

Operational Metrics:

  • Resource tagging compliance (target: 95%+)
  • Cost anomaly resolution time
  • Optimization backlog size and age
  • Engineering team cost awareness (survey-based)

Automated Cost Anomaly Detection and Response

Machine Learning-Based Anomaly Detection:

  • AWS Cost Anomaly Detection service with automatic baseline learning
  • Custom anomaly thresholds by service ($5K for EC2, $2K for S3, etc.)
  • Segment-based detection (by cost allocation tag)
  • Historical context: Compare to same day previous week/month

Automated Investigation and Triage:

  1. Anomaly detected: Cost spike >$5K daily increase
  2. Context gathering: Identify service, account, tags, time period
  3. Automated analysis: Recent deployments, configuration changes, traffic patterns
  4. Notification routing: Alert responsible team based on cost allocation tags
  5. Ticket creation: JIRA/ServiceNow ticket with investigation details
  6. Escalation: If unaddressed for 48 hours, escalate to engineering leadership

Common Anomaly Patterns and Resolutions:

Data Transfer Spike:

  • Root cause: Application misconfiguration causing data loops or excessive API calls
  • Resolution: Review CloudWatch Logs, identify problematic code, deploy fix
  • Prevention: Implement rate limiting and circuit breakers

EC2 Auto-Scaling Event:

  • Root cause: Traffic surge or performance degradation triggering scale-out
  • Resolution: Verify legitimate traffic increase vs. performance issue
  • Prevention: Optimize application performance, review scaling policies

Development Environment Overrun:

  • Root cause: Developer provisioned large instances or resources for testing
  • Resolution: Right-size or terminate unnecessary resources
  • Prevention: Policy enforcement limiting instance types in non-production accounts

Performance vs. Cost Trade-off Analysis

Framework for Architectural Decisions:

Cost-Performance Matrix:

               Low Performance    High Performance
High Cost   |  Over-Provisioned |  Necessary Investment
Low Cost    |  Good Value       |  Under-Provisioned (Risk)

Decision Criteria:

When to prioritize performance (accept higher cost):

  • User-facing services where latency directly impacts revenue
  • Payment processing and transaction systems
  • Real-time dashboards and analytics for C-level executives
  • Services under SLA commitments with financial penalties

When to prioritize cost (accept reasonable performance trade-offs):

  • Internal tools and admin interfaces
  • Batch processing with flexible completion timelines
  • Development and staging environments
  • Archive and backup systems

Balanced Approach (Most Common):

  • Establish performance SLOs (P95 latency <200ms, for example)
  • Optimize cost while maintaining SLO compliance
  • Use performance monitoring to validate cost optimization doesn’t degrade UX
  • Right-size iteratively: Reduce resources incrementally with validation

Ready to Transform Your AWS Costs with FinOps?

Daily DevOps specializes in AWS FinOps implementations that deliver 40-60% cost reductions while building sustainable cost optimization cultures. Our proven methodologies balance immediate savings with long-term efficiency and governance.

Schedule Your Free AWS Cost Audit:

  • Comprehensive analysis of your current AWS spending
  • Identification of immediate cost optimization opportunities
  • Projected savings calculation with implementation roadmap
  • FinOps maturity assessment and organizational recommendations

What You’ll Receive:

  • 2-hour cost audit session reviewing your AWS environment
  • Detailed report with 30-60-90 day optimization roadmap
  • Quick-win opportunities for immediate savings
  • Custom proposal for ongoing FinOps implementation

Contact Jon Price:

Transform your AWS spending from uncontrolled cost center to optimized business investment. Let’s unlock your $1M+ cost savings opportunity together.

This article is part of our AWS Cost Optimization and FinOps series. For more insights on cloud financial management, Reserved Instances, and AWS best practices, explore our comprehensive resource library and case studies.

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