Load balancer cost optimization (high-leverage fixes)

Load balancer optimization is usually about two levers: how many LBs you run (LB-hours) and how busy each LB is (LCU/NLCU). The best results come from reducing always-on LB count and removing traffic amplification patterns that inflate connections and bytes.

Fast optimization checks

  • Consolidate: reduce one-LB-per-service patterns.
  • Cross-zone: disable when safe to reduce cross-AZ transfer.
  • Idle LBs: remove unused listeners and test environments.

Step 1: reduce the number of load balancers (LB-hours)

  • Consolidate services behind shared ingress where feasible (especially in Kubernetes).
  • Delete abandoned LBs from migrations and experiments (they often remain “quietly expensive”).
  • Standardize patterns: “one public LB per environment” is usually cheaper than “one per microservice”.

Start by listing all LBs and tagging ownership; cost reduction is often “delete what nobody owns”.

Step 2: reduce LCU/NLCU drivers (requests, connections, bytes)

  • Reduce connection churn: keep-alive and fewer short timeouts reduce new connections.
  • Reduce bytes processed: compress payloads, avoid routing large downloads through the LB, offload to CDN/object storage.
  • Reduce request amplification: cache hot responses and avoid “polling every second” patterns.
  • Simplify routing rules: avoid unnecessary rule complexity that adds evaluation overhead.

If you can’t tell which driver dominates, run the LCU estimator from metrics and look at which dimension is highest: estimate LCU/NLCU.

Step 3: remove incident multipliers (the most common “spike” root cause)

  • Fix retry storms: set sane timeouts, jittered backoff, and circuit breakers for downstream outages.
  • Rate-limit abusive clients and bot traffic (a small amount of unwanted traffic can dominate LCU).
  • Watch for deploy storms: rolling deploys can temporarily multiply connections and error retries.

Step 4: quantify savings before changing architecture

Optimization gets easier when you model the before/after in the same terms:

  • LB-hours saved = LBs removed × 730 hours/month (or your scheduled hours)
  • Usage saved = (avg LCU/NLCU before − after) × hours/month
LB cost calculator LCU/NLCU calculator Units converter

Validation plan (what to measure for a week)

  • LB count and hours (did LB-hours actually drop?)
  • LCU/NLCU drivers (connections, bytes, rule evals) for avg and p95
  • Incident windows: did retries and errors drop after fixes?
  • Related side effects: cross-AZ transfer and NAT/egress if routing changed

Related cost domains: NAT gateway costs and VPC data transfer.

Sources

Related guides

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Related calculators

RPS to Monthly Requests Calculator
Estimate monthly request volume from RPS, hours/day, and utilization.
API Request Cost Calculator
Estimate request-based charges from monthly requests and $ per million.
CDN Request Cost Calculator
Estimate CDN request fees from monthly requests and $ per 10k/1M pricing.

FAQ

What's the fastest lever for load balancer cost?
Reduce LB-hours by reducing the number of load balancers. In Kubernetes, one load balancer per service patterns create many always-on LBs.
How do I reduce LCU/NLCU charges?
Reduce the drivers: connection churn, active connections, bytes processed, and rule evaluation overhead. Fix retry storms and chatty clients that multiply traffic.
Should I optimize LB-hours or LCUs first?
If you have many LBs, start with LB-hours. If you have a few high-traffic LBs, focus on LCU/NLCU and the traffic drivers.
Why do load balancer bills spike during incidents?
Retries and timeouts can multiply requests and connections. Bot traffic and thundering herds can inflate bytes processed even when success traffic is flat.
Last updated: 2026-02-07