NAT Gateway cost optimization (high-leverage fixes)

NAT Gateway optimization is unusually measurable: almost everything comes down to gateway-hours and GB processed. The best savings usually come from keeping AWS-service traffic private and eliminating recurring downloads and retry storms that silently multiply outbound traffic.

Step 0: baseline the two drivers

  • Gateway-hours: how many NAT gateways are always on (by environment and region)
  • GB processed: average and peak (incident) weeks
  • Top traffic sources: images, updates, external APIs, log shipping

If you don’t know GB processed yet: estimate GB processed.

1) Keep traffic private (the biggest lever for many teams)

  • Use VPC endpoints/private connectivity for common AWS services where available.
  • Avoid routing AWS API calls through NAT by accident (it looks like “internet egress” in the NAT bill).
  • Validate that route tables and DNS resolution actually keep the traffic on the private path.

Cost comparison: NAT vs VPC endpoints

2) Reduce large recurring downloads (often the hidden baseline)

  • Cache OS/package updates where practical (or use internal mirrors).
  • Reduce container image size and avoid re-pulling unchanged layers.
  • Prevent “download storms” during autoscaling by pre-pulling or staggering updates.

3) Fix retry storms and noisy egress

  • Set sane timeouts and jittered backoff for outbound calls.
  • Identify the top external destinations (APIs/SaaS) and validate volume against business expectations.
  • Watch “polling” and keepalive patterns that create constant egress even at low traffic.

4) Reduce non-prod waste

  • Schedule dev/test workloads so NAT isn’t needed 730 hours/month.
  • Don’t mirror production traffic volumes into staging unless required.
  • Use smaller test datasets to reduce background job egress.

5) Endpoint-first checklist (common NAT drivers)

A fast way to reduce NAT processed GB is to identify which traffic is going to AWS services and keep it on a private path. Common NAT drivers to check (availability varies by region/service):

  • Object storage access (often large and steady)
  • Container registry pulls (large bursts during deploys/autoscaling)
  • Security token / identity calls (small per call, but can be high frequency)
  • Monitoring/logging APIs (can be noisy in large fleets)

Practical flow: identify top NAT destinations, pick the top 1–2 AWS-service buckets, then validate the NAT GB drop after enabling private connectivity.

6) Validate savings (and ensure costs didn’t just move)

  • Confirm GB processed dropped and identify which source changed.
  • Check cross-AZ transfer and internet egress costs after routing changes.
  • Re-check incident windows; if retries still spike, monthly savings will erode.

Tools and next steps

Sources


Related guides


Related calculators


FAQ

What's the fastest way to reduce NAT Gateway cost?
Reduce GB processed through NAT by keeping traffic private (endpoints/private access) and eliminating large recurring downloads (images and updates).
Why do container image pulls matter?
Large images pulled frequently by nodes behind NAT can drive high processed GB. Autoscaling and frequent redeploys amplify the effect.
Why do NAT bills spike during incidents?
Retries/timeouts multiply outbound calls to external APIs. During scaling events, downloads can increase at the same time, making spikes worse.
What should I measure first?
Gateway-hours and GB processed. If GB processed dominates, focus on traffic sources and private connectivity. If gateway-hours dominate, focus on consolidation and schedules.

Last updated: 2026-01-27