Load Balancer Cost Optimization: Reducing Hidden Charges Across AWS, Azure & GCP

When organizations think about cloud cost optimization, compute, storage, and licensing often dominate the conversation. But one of the most overlooked cost drivers is load balancing. Load balancers are critical for distributing traffic, improving reliability, and ensuring scale—but they also come with hidden charges that add up quickly.

For mid-sized and enterprise workloads, load balancer costs can contribute 10–20% of monthly cloud spend if not actively managed. Across AWS, Azure, and Google Cloud, the pricing models differ, but the hidden charges often come from the same sources: data processing fees, outbound traffic, and idle resources.

The Real Cost of Cloud Load Balancers

At first glance, load balancers seem inexpensive—just a few cents per hour. But the real spend isn’t just the hourly rate; it’s the per-GB data processing fees and cross-zone traffic charges that silently inflate bills.

• AWS:
  
  
  • Application/Network Load Balancer = $0.0225 per hour.
    
  • Plus $0.008 per GB of data processed.
    
  • Cross-AZ traffic (inbound + outbound) is charged at standard data transfer rates.
    
  • Example: A mid-sized app moving 20TB through an ALB per month can pay $160 just in LB data fees, plus thousands in cross-AZ charges.
    
    
• Azure:
  
  
  • Standard Load Balancer = ~$0.025 per hour.
    
  • Data processed = $0.01 per GB.
    
  • Outbound data transfer (egress) adds another $0.087 per GB for the first 10TB.
    
  • A global deployment with 15TB/month traffic can see $2,000+ monthly just for LB + egress.
    
    
• GCP:
  
  
  • Global HTTP(S) Load Balancer pricing = $0.025 per hour.
    
  • Data processed = $0.008–$0.012 per GB, depending on destination.
    
  • Backend to frontend data transfers can push costs further.
    
  • Streaming 10TB through a GCP LB can cost $1,000+ in data processing alone.
    

These numbers show why “cheap per-hour pricing” can be misleading. Data movement is the real budget drain.

Common Hidden Charges Businesses Miss

1. Cross-Zone Traffic – Replicating workloads across zones increases resilience but doubles traffic charges.
   
   
2. Idle Load Balancers – Orphaned LBs from test/staging environments often sit running unnoticed.
   
   
3. Over-Provisioned Configurations – Multiple LBs for workloads that could be consolidated.
   
   
4. Unoptimized Routing – Traffic hair-pinning between regions/zones when local routing would be cheaper.
   
   
5. Data Processing Overhead – Every GB through the LB counts, even internal traffic, unless optimized.
   
   

Strategies to Optimize Load Balancer Costs

Audit Regularly – Use AWS Cost Explorer, Azure Cost Analysis, or GCP Billing to track LB usage at a granular level.

Consolidate Load Balancers – Where possible, reduce the number of LBs by grouping services under shared frontends.

Enable Zonal Locality – Keep traffic within the same availability zone to avoid cross-AZ transfer costs.

Use CDNs – Offload repetitive traffic from LBs by caching with CloudFront, Azure CDN, or Cloud CDN.

Scale Down Test/Dev Environments – Shut down unused or low-priority load balancers during off-hours.

Private Connectivity – For internal services, replace public LBs with private endpoints or service mesh routing.

FinOps Perspective: Making Load Balancer Spend Visible

Many finance teams miss LB costs because they’re scattered across “networking” or “data transfer” in billing reports. FinOps practices—such as tagging load balancers by project, setting budget alerts, and allocating costs back to teams—ensure accountability. When engineering sees the monthly LB bill, optimization becomes a design decision, not a finance afterthought.

Closing Thoughts

Load balancers are essential to cloud reliability and scalability, but they don’t have to be silent budget killers. By monitoring usage, consolidating where possible, and adopting smart routing strategies, businesses can cut LB spend by 20–40% while maintaining performance.

At Transcloud, we’ve helped organizations across AWS, Azure, and GCP uncover thousands in hidden LB costs—and reclaim that spend for innovation.