Cloud Waste Elimination: Finding and Fixing the 35% You're Overspending
Practical strategies and AWS tooling to identify idle resources, over-provisioned instances, unattached storage, and zombie assets — recovering 25-40% of your cloud bill
Executive Summary
The average organization wastes 32-35% of its cloud spend on resources that deliver zero business value. That's not a rounding error — for a company spending $500K/year on AWS, it's $175K literally evaporating. Flexera's 2025 State of the Cloud report confirmed this figure, and Gartner estimates the number climbs even higher for organizations without active FinOps practices.
Cloud waste isn't malicious — it's structural. Engineers provision resources for peak demand and never scale back. Dev environments run 24/7 even though they're used 8 hours a day. EBS volumes linger after instances are terminated. Snapshots accumulate without retention policies. Load balancers outlive the services they once fronted.
This article provides a systematic approach to finding and fixing cloud waste: categorizing waste types, deploying automated detection, right-sizing compute, optimizing storage, avoiding network cost traps, and building a culture of continuous cost awareness. Every technique has been validated in production environments, with a detailed case study showing $180K/year recovered at an Israeli SaaS company.
Quick Answer: How to Eliminate Cloud Waste
The average organization wastes 32-35% of cloud spend on idle, over-provisioned, or orphaned resources. The three fastest actions to recover wasted spend:
Delete unattached EBS volumes — volumes in "available" state cost $0.10/GB/month doing nothing
Terminate idle EC2 instances — instances with <5% CPU for 14+ days can be safely removed
Remove orphaned snapshots — snapshots from deleted volumes accumulate thousands per month
These three actions alone recover 10-15% of total cloud spend within days.
What Are the Five Types of Cloud Waste?
Cloud waste isn't a single problem — it's five distinct categories, each with different root causes and remediation strategies. Understanding the taxonomy is the first step toward systematic elimination.
Category 1: Idle Instances
Idle instances are running EC2 instances that perform little to no useful work. They typically originate from three scenarios: development environments left running overnight and on weekends, staging environments that outlive their sprint, and production instances for decommissioned features.
How to identify: CPU utilization under 5% for 14+ consecutive days, network I/O under 5 MB/day
Typical prevalence: 15-25% of all running instances in unoptimized environments
Savings potential: 100% of instance cost — these can be terminated outright
Category 2: Over-Provisioned Resources
Over-provisioning is the most common form of waste. Engineers select instance sizes based on estimated peak demand, adding a generous safety margin. The result: instances running at 10-20% average utilization when they could run the same workload at 60-70% on a smaller type.
How to identify: Sustained CPU utilization under 40%, memory usage under 30%, network bandwidth under 20% of capacity
Typical prevalence: 40-60% of production instances are at least one size too large
Savings potential: 25-75% per instance through right-sizing (e.g., m5.2xlarge → m5.large = 75% savings)
Category 3: Unattached Storage
When an EC2 instance is terminated, its attached EBS volumes may persist if "Delete on Termination" wasn't enabled. These orphaned volumes continue incurring charges — $0.10/GB/month for gp3 volumes — with no instance to serve.
How to identify: EBS volumes in "available" state (not attached to any instance)
Typical prevalence: 20-40% of all EBS volumes in accounts with high instance churn
Savings potential: 100% — snapshot the data if needed, then delete the volume
Category 4: Orphaned Snapshots
EBS snapshots are created for backups, AMI generation, and disaster recovery. But when the source volume is deleted or the AMI deregistered, associated snapshots often remain. At $0.05/GB/month, a single orphaned 500 GB snapshot costs $25/month — and organizations commonly accumulate hundreds.
How to identify: Snapshots whose source volume no longer exists, or snapshots older than retention policy with no associated AMI
Typical prevalence: 50-80% of snapshots in mature accounts have no active use
Savings potential: $500-5,000/month depending on snapshot volume
Category 5: Zombie Load Balancers and Other Orphaned Services
Application Load Balancers (ALBs) cost $0.0225/hour ($16.20/month) even with zero traffic. When the backend targets are removed or deregistered, the ALB persists. Elastic IPs not associated with running instances cost $0.005/hour. NAT Gateways without traffic still charge $0.045/hour.
How to identify: ALBs with zero healthy targets, Elastic IPs in "disassociated" state, NAT Gateways with zero bytes processed
Typical prevalence: 5-15 zombie resources per AWS account
Savings potential: $50-500/month per resource — small individually, significant in aggregate
The Compound Effect of Cloud Waste
Each waste category may seem manageable in isolation. Together, they compound:
15 idle instances × $150/month = $2,250/month
40 over-provisioned instances × $75/month savings each = $3,000/month
200 unattached EBS volumes × $10/month = $2,000/month
500 orphaned snapshots × $5/month = $2,500/month
12 zombie load balancers × $16/month = $192/month
Total recoverable: $9,942/month = $119,304/year — from a single AWS account
How Do You Automatically Detect Cloud Waste?
Manual waste hunting doesn't scale. You need automated detection that runs continuously and surfaces waste before it accumulates. AWS provides several native tools, but the real power comes from combining them with custom automation.
AWS Trusted Advisor
Trusted Advisor is the easiest starting point. With Business or Enterprise support, you get access to cost optimization checks that identify low-utilization EC2 instances, idle RDS instances, underutilized EBS volumes, and unassociated Elastic IPs. However, Trusted Advisor's thresholds are conservative — it flags instances under 10% CPU utilization, missing the large population of instances at 15-30% that are still significantly over-provisioned.
AWS Cost Explorer and Compute Optimizer
Cost Explorer provides spend breakdowns by service, tag, and time period. Its "Right Sizing Recommendations" tab identifies instances that could be downsized based on CloudWatch metrics. AWS Compute Optimizer goes deeper, using machine learning to analyze 14 days of utilization data and recommend optimal instance types, including newer-generation instances that offer better price-performance ratios.
Cost Explorer: Best for high-level spend trends and tag-based allocation. Free with any AWS account
Compute Optimizer: Best for instance-level right-sizing recommendations. Free tier covers up to 30 days of data
Limitation: Neither tool detects unattached volumes, orphaned snapshots, or zombie load balancers — you need custom automation for those
Custom Lambda-Based Waste Detection
For comprehensive coverage, deploy a scheduled Lambda function that scans all waste categories and generates a consolidated report. This fills the gaps that native tools miss.
import boto3 from datetime import datetime, timedelta ec2 = boto3.client('ec2') cloudwatch = boto3.client('cloudwatch') def find_unattached_volumes(): """Find EBS volumes not attached to any instance.""" volumes = ec2.describe_volumes( Filters=[{'Name': 'status', 'Values': ['available']}] )['Volumes'] waste = [] for vol in volumes: monthly_cost = vol['Size'] * 0.10 # gp3 pricing days_unattached = (datetime.utcnow() - vol['CreateTime'] .replace(tzinfo=None)).days waste.append({ 'VolumeId': vol['VolumeId'], 'SizeGB': vol['Size'], 'MonthlyCost': f"${monthly_cost:.2f}", 'DaysUnattached': days_unattached, 'AZ': vol['AvailabilityZone'] }) return waste def find_idle_instances(cpu_threshold=5, days=14): """Find instances with CPU < threshold for N days.""" instances = ec2.describe_instances( Filters=[{'Name': 'instance-state-name', 'Values': ['running']}] ) idle = [] for reservation in instances['Reservations']: for inst in reservation['Instances']: avg_cpu = get_avg_cpu(inst['InstanceId'], days) if avg_cpu < cpu_threshold: idle.append({ 'InstanceId': inst['InstanceId'], 'Type': inst['InstanceType'], 'AvgCPU': f"{avg_cpu:.1f}%", 'LaunchTime': str(inst['LaunchTime']), 'Tags': {t['Key']: t['Value'] for t in inst.get('Tags', [])} }) return idle def find_orphaned_snapshots(): """Find snapshots whose source volume no longer exists.""" snapshots = ec2.describe_snapshots( OwnerIds=['self'] )['Snapshots'] existing_volumes = {v['VolumeId'] for v in ec2.describe_volumes()['Volumes']} orphaned = [] for snap in snapshots: vol_id = snap.get('VolumeId', '') if vol_id and vol_id not in existing_volumes: monthly_cost = snap['VolumeSize'] * 0.05 orphaned.append({ 'SnapshotId': snap['SnapshotId'], 'SizeGB': snap['VolumeSize'], 'MonthlyCost': f"${monthly_cost:.2f}", 'SourceVolume': vol_id, 'Age': str(snap['StartTime']) }) return orphaned def get_avg_cpu(instance_id, days): """Get average CPU utilization over N days.""" end = datetime.utcnow() start = end - timedelta(days=days) metrics = cloudwatch.get_metric_statistics( Namespace='AWS/EC2', MetricName='CPUUtilization', Dimensions=[{'Name': 'InstanceId', 'Value': instance_id}], StartTime=start, EndTime=end, Period=86400, Statistics=['Average'] ) if not metrics['Datapoints']: return 0.0 return sum(d['Average'] for d in metrics['Datapoints']) / len( metrics['Datapoints'])
Schedule this Lambda to run daily via EventBridge. Send results to SNS for Slack notifications or to S3 for dashboard integration. The function itself costs under $0.10/month to run — negligible compared to the waste it detects.
Cloud Waste Cheat Sheet: Types, Detection, and Savings
Use this reference table to prioritize your waste elimination efforts. Start with the highest-impact items at the top.
| Waste Type | Detection Method | Typical Savings | Effort |
|---|---|---|---|
| Over-provisioned EC2 | Compute Optimizer, CloudWatch CPU/Memory | 25-75% per instance | Medium |
| Idle EC2 instances | CloudWatch CPU < 5% for 14 days | 100% (terminate) | Low |
| Unattached EBS volumes | EC2 API: status = "available" | $0.10/GB/month (100%) | Low |
| Orphaned snapshots | Lambda: check if source volume exists | $0.05/GB/month | Low |
| Zombie ALBs | ELBv2 API: zero healthy targets | $16-22/month each | Low |
| Unassociated Elastic IPs | EC2 API: no association ID | $3.60/month each | Low |
| Over-provisioned RDS | CloudWatch: CPU < 25%, connections < 20% | 30-60% per instance | Medium |
| NAT Gateway data charges | VPC Flow Logs + Cost Explorer | 40-60% with VPC endpoints | Medium |
| Old-gen instances | EC2 API: filter by instance family (m4, c4, r4) | 15-30% (m4 → m6i) | Medium |
| Dev/staging running 24/7 | Tag-based identification + scheduling | 65% (run 8h/day, 5 days/week) | Low |
How Does Right-Sizing Save 20-40% on Compute?
Right-sizing is the single highest-impact optimization for most organizations. The challenge is doing it safely — downsize too aggressively and you cause performance issues; too conservatively and you leave money on the table. The key is basing decisions on observed metrics, not estimated requirements.
Step 1: Collect CloudWatch Metrics
Standard CloudWatch provides CPU, network, and disk I/O. For memory utilization, install the CloudWatch Agent — this is critical because many over-provisioned instances are memory-constrained even when CPU is low. Collect at minimum 14 days of data, ideally 30, to capture weekly patterns and any monthly spikes.
import boto3 from datetime import datetime, timedelta cloudwatch = boto3.client('cloudwatch') def analyze_instance_utilization(instance_id, days=30): """ Analyze an EC2 instance to determine right-sizing recommendation based on actual CloudWatch metrics. """ end = datetime.utcnow() start = end - timedelta(days=days) # CPU utilization - average and P95 cpu_stats = cloudwatch.get_metric_statistics( Namespace='AWS/EC2', MetricName='CPUUtilization', Dimensions=[{'Name': 'InstanceId', 'Value': instance_id}], StartTime=start, EndTime=end, Period=3600, # 1-hour granularity Statistics=['Average', 'Maximum'] ) cpu_avg = sum(d['Average'] for d in cpu_stats['Datapoints']) / max( len(cpu_stats['Datapoints']), 1) cpu_max = max((d['Maximum'] for d in cpu_stats['Datapoints']), default=0) # Network In/Out net_in = cloudwatch.get_metric_statistics( Namespace='AWS/EC2', MetricName='NetworkIn', Dimensions=[{'Name': 'InstanceId', 'Value': instance_id}], StartTime=start, EndTime=end, Period=86400, Statistics=['Average'] ) avg_net_in_gb = sum(d['Average'] for d in net_in['Datapoints']) / max( len(net_in['Datapoints']), 1) / (1024**3) # Memory (requires CloudWatch Agent) mem_stats = cloudwatch.get_metric_statistics( Namespace='CWAgent', MetricName='mem_used_percent', Dimensions=[{'Name': 'InstanceId', 'Value': instance_id}], StartTime=start, EndTime=end, Period=3600, Statistics=['Average', 'Maximum'] ) mem_avg = sum(d['Average'] for d in mem_stats['Datapoints']) / max( len(mem_stats['Datapoints']), 1) if mem_stats[ 'Datapoints'] else None return { 'instance_id': instance_id, 'cpu_avg': round(cpu_avg, 1), 'cpu_max': round(cpu_max, 1), 'mem_avg': round(mem_avg, 1) if mem_avg else 'N/A', 'net_in_gb_day': round(avg_net_in_gb, 2), 'recommendation': get_recommendation( cpu_avg, cpu_max, mem_avg) } def get_recommendation(cpu_avg, cpu_max, mem_avg): """Generate right-sizing recommendation.""" if cpu_avg < 5 and (mem_avg is None or mem_avg < 10): return 'TERMINATE - instance appears idle' if cpu_avg < 20 and cpu_max < 50: return 'DOWNSIZE 2 steps (e.g. xlarge -> small)' if cpu_avg < 40 and cpu_max < 70: return 'DOWNSIZE 1 step (e.g. xlarge -> large)' if cpu_avg > 80 or cpu_max > 95: return 'UPSIZE - at risk of CPU throttling' return 'OPTIMAL - current size is appropriate'
Step 2: Safe Downsize Process
Never right-size and walk away. Follow this process to ensure zero impact on production:
Identify candidates: Run the analysis script across all instances. Filter for instances where P95 CPU is under 50% and P95 memory is under 60%
Test in staging first: Downsize equivalent staging instances and run load tests simulating production traffic patterns
Use Auto Scaling groups: For ASG-managed instances, update the launch template with the new instance type and perform a rolling update
Monitor for 48 hours: Watch CloudWatch alarms for CPU, memory, latency, and error rate regressions
Rollback plan: Keep the previous launch template version. If metrics degrade, revert within minutes
Right-Sizing Pitfall: Memory-Bound Workloads
A common mistake is right-sizing based solely on CPU. Java applications, Redis caches, and data processing workloads are often memory-bound — CPU sits at 15% but memory is at 85%. Without CloudWatch Agent providing memory metrics, you'll downsize these instances and cause OOM (Out of Memory) kills. Always install the CloudWatch Agent and check both CPU and memory before downsizing.
How Do You Optimize Storage Costs in AWS?
Storage costs are insidious because they grow monotonically — data is always being created, rarely deleted. Without active management, storage becomes the fastest-growing line item in your AWS bill.
EBS Volume Optimization
Beyond deleting unattached volumes, look for these optimization opportunities:
Volume type migration: Many organizations still run io1 volumes ($0.125/GB/month + $0.065/IOPS) that could be gp3 ($0.08/GB/month with 3,000 free IOPS). For a 500 GB volume provisioned with 3,000 IOPS: io1 costs $257.50/month vs. gp3 costs $40/month — 84% savings
Right-size volume capacity: A 1 TB gp3 volume using only 200 GB costs $80/month. Shrinking to 250 GB (with buffer) costs $20/month
Enable "Delete on Termination": For all non-persistent volumes, ensure this flag is set to prevent future orphaned volumes
S3 Lifecycle Policies
S3 Standard costs $0.023/GB/month. Most data accessed less than once per month should be in a cheaper tier. Implement lifecycle policies that automatically transition objects based on age:
| Storage Class | Cost/GB/Month | Access Pattern | Transition After |
|---|---|---|---|
| S3 Standard | $0.023 | Frequent access (daily) | — |
| S3 Standard-IA | $0.0125 | Infrequent (monthly) | 30 days |
| S3 Glacier Instant | $0.004 | Rare (quarterly) | 90 days |
| S3 Glacier Deep Archive | $0.00099 | Compliance/archive | 180 days |
For a company storing 10 TB in S3 Standard: $230/month. With lifecycle policies moving 70% of data to Glacier Instant after 90 days: $28 (Glacier) + $69 (Standard) = $97/month — a 58% reduction.
Snapshot Retention Policies
EBS snapshots are incremental, but they still accumulate cost. Implement Amazon Data Lifecycle Manager (DLM) policies to automatically manage snapshot retention:
Daily snapshots: Retain for 7 days
Weekly snapshots: Retain for 4 weeks
Monthly snapshots: Retain for 12 months
Compliance snapshots: Retain per regulatory requirement, archive to S3 Glacier
Without DLM, most organizations have a flat "keep everything forever" policy. One customer had 14,000 snapshots totaling 280 TB — $14,000/month. After implementing retention policies and deleting orphaned snapshots, they reduced to 2,100 snapshots and $2,800/month.
What Are the Hidden Network Cost Traps in AWS?
Network costs are the least visible and most frequently surprising line item in AWS bills. Unlike compute and storage, network charges are scattered across multiple services and difficult to attribute to specific workloads.
NAT Gateway: The $10,000/Month Surprise
NAT Gateways charge $0.045/hour ($32.40/month baseline) plus $0.045 per GB of data processed. For applications in private subnets making frequent API calls to AWS services (S3, DynamoDB, SQS), the data processing charges dwarf the hourly cost.
NAT Gateway Cost Example
Data pipeline processing 5 TB/month through S3, running in a private subnet:
NAT Gateway hourly: $32.40/month
NAT Gateway data processing: 5,000 GB × $0.045 = $225/month
Total NAT cost: $257.40/month
With S3 Gateway Endpoint (free): $32.40/month (hourly only, zero data processing charges)
Savings: $225/month ($2,700/year) — just from adding a free VPC endpoint
VPC Endpoints: Free Money
Gateway VPC Endpoints for S3 and DynamoDB are completely free — no hourly charge, no data processing charge. Interface VPC Endpoints for other services (SQS, SNS, KMS, ECR) cost $0.01/hour but eliminate NAT Gateway data processing charges, which are 4.5x more expensive per GB.
Priority 1: S3 Gateway Endpoint — free, reduces NAT data charges for any S3 traffic
Priority 2: DynamoDB Gateway Endpoint — free, same benefit for DynamoDB traffic
Priority 3: ECR Interface Endpoint — eliminates NAT charges for container image pulls (significant for Kubernetes clusters)
Priority 4: SQS/SNS/KMS endpoints — evaluate based on traffic volume
Cross-AZ and Cross-Region Data Transfer
AWS charges $0.01/GB for cross-AZ traffic and $0.02/GB for cross-region traffic. In microservices architectures where services communicate across availability zones, this adds up quickly.
Mitigation: Co-locate tightly coupled services in the same AZ using placement groups or topology-aware scheduling in Kubernetes
Mitigation: Use regional S3 endpoints instead of cross-region replication for non-critical data
Mitigation: Enable VPC Flow Logs to identify the highest-traffic paths and optimize accordingly
Case Study: Israeli SaaS Company Recovers $180K/Year
Company Profile
B2B SaaS platform based in Tel Aviv, 120 engineers, Series C. Annual AWS spend: $540K. Multi-account AWS Organization with 8 accounts (production, staging, development, data, security, shared services, sandbox, management).
The Trigger: CFO flagged that AWS costs had grown 45% year-over-year despite only 20% revenue growth. Engineering leadership was asked to reduce cloud spend by 25% within 90 days without impacting product performance.
Phase 1: Discovery (Weeks 1-2)
HostingX deployed automated waste detection across all 8 AWS accounts. The initial scan revealed:
42 idle EC2 instances across dev and staging — combined cost: $4,200/month. These were test instances from previous sprints, load testing infrastructure left running, and a legacy monitoring stack that had been replaced
380 unattached EBS volumes — total: 12 TB, costing $1,200/month. Root cause: CloudFormation stack deletions that didn't clean up volumes
2,800 orphaned snapshots — total: 45 TB, costing $2,250/month. No DLM policies had been configured
8 zombie ALBs and 23 unassociated Elastic IPs — combined: $210/month
NAT Gateway data charges — $3,400/month, with 60% of traffic going to S3 (no Gateway Endpoint configured)
Phase 2: Quick Wins (Weeks 2-4)
Immediate Actions and Savings
Terminated 42 idle instances → $4,200/month saved
Deleted 380 unattached volumes (after snapshot verification) → $1,200/month saved
Purged 2,400 orphaned snapshots, implemented DLM → $1,900/month saved
Removed 8 zombie ALBs + 23 Elastic IPs → $210/month saved
Deployed S3 and DynamoDB Gateway Endpoints → $2,100/month saved
Phase 2 Total: $9,610/month ($115,320/year)
Phase 3: Right-Sizing and Scheduling (Weeks 4-8)
With the low-hanging fruit captured, the team moved to structural optimization:
Right-sized 35 production instances: Based on 30-day CloudWatch analysis, downsized instances averaging 18% CPU and 25% memory utilization. Moved from m5.2xlarge to m5.large (75% savings) and c5.xlarge to c5.large (50% savings). Total: $3,100/month saved
Dev/staging scheduling: Implemented AWS Instance Scheduler to stop all dev and staging instances outside business hours (6pm-8am) and on weekends. 168 hours/week reduced to 60 hours/week = $1,800/month saved
S3 lifecycle policies: Implemented tiered storage for application logs and data exports. $480/month saved
Final Results After 90 Days
Total monthly savings: $14,990/month
Annualized savings: $179,880/year
Percentage reduction: 33% of total AWS spend
Performance impact: Zero — P99 latency actually improved by 8% due to right-sizing eliminating noisy-neighbor effects on shared tenancy instances
Ongoing automation: Weekly waste detection reports, automated DLM policies, instance scheduling — preventing waste from re-accumulating
Frequently Asked Questions
What percentage of cloud spend is typically wasted?
Industry research consistently shows that 25-35% of cloud spend is wasted. Flexera's 2025 State of the Cloud report found 32% average waste, while Gartner estimates up to 35% for organizations without active FinOps practices. The largest contributors are idle instances (30% of waste), over-provisioned resources (25%), and unattached storage (20%).
How do I find idle EC2 instances in AWS?
Use CloudWatch metrics to identify instances with sustained low CPU utilization (under 5% average over 14 days) and minimal network activity. AWS Trusted Advisor flags underutilized instances automatically. For a more comprehensive approach, deploy a Lambda function that queries CloudWatch metrics across all instances and generates a report of candidates for termination or downsizing.
What is the fastest way to reduce cloud waste?
The fastest wins come from three actions: (1) Delete unattached EBS volumes — these cost money even when not connected to any instance. (2) Terminate idle instances with less than 2% CPU utilization over 30 days. (3) Remove orphaned snapshots that reference deleted volumes. These three actions alone typically recover 10-15% of total cloud spend within days.
How much can right-sizing save on AWS?
Right-sizing typically saves 20-40% on compute costs. Most organizations over-provision by 2-4x because engineers choose instance sizes based on peak load estimates rather than observed usage. Moving from an m5.2xlarge to an m5.large (a common right-sizing move) cuts that instance cost by 75%. AWS Compute Optimizer provides specific right-sizing recommendations based on actual CloudWatch metrics.
Are NAT Gateway costs a significant source of cloud waste?
Yes — NAT Gateway costs are one of the most overlooked sources of cloud waste. AWS charges $0.045/hour per NAT Gateway ($32/month baseline) plus $0.045 per GB of data processed. Organizations with heavy outbound traffic in private subnets often discover NAT Gateways costing $5,000-15,000/month. VPC endpoints for S3 and DynamoDB are free and eliminate NAT Gateway data processing charges for those services, often saving 40-60% on data transfer costs.
HostingX Cloud Waste Elimination Service
Finding cloud waste is straightforward. Sustaining zero-waste operations — preventing waste from re-accumulating as your infrastructure evolves — requires ongoing discipline, tooling, and expertise. HostingX IL provides end-to-end cloud waste management:
Comprehensive Waste Audit: Multi-account scanning across all waste categories — idle compute, over-provisioned resources, orphaned storage, zombie services, and network inefficiencies. Delivered within 5 business days with prioritized remediation plan
Automated Waste Detection: Custom Lambda-based detection deployed to your accounts, with Slack/email alerting. New waste is identified within 24 hours of creation
Right-Sizing Implementation: Safe, data-driven instance downsizing with CloudWatch Agent deployment, 30-day metric analysis, staging validation, and production rollout with automated rollback
Storage Lifecycle Management: S3 lifecycle policies, DLM snapshot retention, EBS volume optimization, and ongoing compliance monitoring
Network Cost Optimization: VPC endpoint deployment, NAT Gateway traffic analysis, cross-AZ traffic reduction, and data transfer path optimization
Monthly FinOps Reviews: Ongoing cost reviews comparing spend against baseline, identifying new waste, and recommending Reserved Instance or Savings Plan commitments
Proven Results with Israeli Tech Companies
Average waste reduction: 28-38% of total cloud spend
Typical time to first savings: under 2 weeks
Performance impact on production: zero degradation (monitored via SLO dashboards)
ROI on HostingX engagement: 5-10x within first quarter
Stop Paying for Resources You Don't Use
HostingX IL identifies and eliminates 25-40% of wasted cloud spend. Get a free waste audit for your AWS accounts — most companies find $50K+ in annual savings within the first week.
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