Estimate NAT Gateway GB processed (quick methods)
Start with a calculator if you need a first-pass estimate, then use this guide to validate the assumptions and catch the billing traps.
This page is the processed-GB measurement workflow, not the bill-boundary page: the goal is to turn NAT metrics, flow logs, throughput charts, and traffic-source evidence into a defendable GB-per-month model. NAT Gateway processing charges scale with GB processed, so your budget depends on getting this number roughly right.
If you still are not sure which costs belong inside the NAT Gateway bill versus beside it, go back to the pricing guide first. Open the pricing guide.
Method 1) From NAT metrics (best)
- Pick a representative window (7–30 days) and sum bytes processed.
- Convert bytes → GB using the same convention you use for pricing (and keep it consistent).
- Scale to monthly: GB/month ≈ GB/window × (30 / days in window).
Use this whenever possible because it directly measures the billed driver.
Method 2) From VPC Flow Logs (good for attribution)
Flow logs are useful when you need to answer “what produced the GB?”. A practical workflow:
- Filter flow logs to traffic that traverses NAT gateway ENIs.
- Group by destination (top domains/IPs) and source (top subnets/workloads).
- Sum bytes and convert to GB/month.
This method is slower but helps you find the 2–3 sources that actually matter (images, updates, external APIs).
Method 3) From throughput charts (Mbps) (fast first pass)
If you only have an “average Mbps” chart, you can estimate monthly GB with a units converter. Steps:
- Start from average Mbps (not peak).
- Apply utilization: if the chart is peak, multiply by an average factor you can defend.
- Convert Mbps → GB/month with consistent units.
Tool: Units converter
Method 4) Sanity-check from common sources (useful when data is missing)
If you have no metrics yet, estimate the big buckets and add them:
- Image pulls: image size × pulls/month × fleet size
- Updates: update GB/week × nodes × weeks/month
- External APIs: requests/month × avg payload size (GB)
- Log shipping: log export GB/day × days/month
This is crude but helps you avoid pretending it’s zero.
Evidence pack before you trust the model
- Measured baseline: a representative period from NAT metrics.
- Attribution evidence: flow logs or destination summaries that explain what produced the GB.
- Fallback estimate: throughput charts or source-bucket math when direct telemetry is incomplete.
- Incident scenario: retries, autoscaling, or download surges that can dominate monthly peaks.
Worked example (structure, not provider-specific rates)
- Representative week metrics: 1.2 TB processed in 7 days
- Scale to month: (1.2 TB / 7) × 30 ≈ 5.1 TB/month
- Price: 5.1 TB × $/GB + gateway-hours × $/hour
Validation checklist
- Compare estimate vs real processed-bytes metrics for a week after deployment.
- Identify the top 3 sources (images, updates, APIs) and verify they match expectations.
- Include an incident scenario: retries often dominate monthly peaks.
- After changes, confirm GB processed dropped (not just moved to transfer/egress elsewhere).
Next steps
Once the processed-GB model is believable, move to the optimization guide and change one traffic lever at a time.