NAT Gateway Costs: The AWS Bill Shock Nobody Warned You About

There is a pattern that repeats itself across AWS environments of all sizes. An infrastructure team sets up private subnets, deploys a NAT Gateway, and moves on. Weeks or months later, someone opens the AWS Cost Explorer and asks the same question: why is the bill so high?

The answer, more often than not, traces back to the NAT Gateway.

What Is a NAT Gateway and Why Do Teams Use It?

A NAT (Network Address Translation) Gateway allows resources inside private subnets, such as EC2 instances or containers, to reach the internet without being directly exposed to it. It is a foundational piece of many AWS architectures. It is also one of the most consistently underestimated cost drivers in cloud infrastructure.

The setup is straightforward. Private subnets improve security by keeping resources away from direct internet exposure. A NAT Gateway sits in the middle, handling outbound traffic on their behalf. For most teams, it feels like a solved problem the moment it starts working.

The Part That Catches Teams Off Guard

AWS charges for NAT Gateways in two ways: an hourly rate for keeping it running, and a per-GB data processing fee for every gigabyte of traffic that passes through it. The hourly rate is visible and predictable. The data processing charge is neither.

Traffic through a NAT Gateway adds up faster than most teams expect. Software updates, log shipping, telemetry, API calls to external services, dependency downloads during deployments - all of this passes through the NAT Gateway and contributes to the data processing bill. None of it feels significant in isolation. Together, it can become a major line item.

The issue is not that the NAT Gateway is poorly designed. The issue is that the data processing cost is largely invisible until it becomes a problem.

Why Cost Visibility Breaks Down

Most infrastructure decisions are made with availability and security in mind. Cost modeling for network traffic often comes later, if it comes at all. Teams tend to instrument compute and storage costs well. Network egress and processing costs receive far less attention.

NAT Gateway usage is also distributed across many services and workloads. There is no single owner. Traffic from dozens of different applications flows through the same gateway, and without proper tagging and monitoring, it is nearly impossible to attribute costs to specific teams or workloads.

By the time the bill arrives, the data is old and the traffic patterns are hard to reconstruct.

Traffic Type Makes a Significant Difference

Not all traffic through a NAT Gateway has the same cost profile. A workload that calls an external API a few times per minute behaves very differently from one that streams large payloads to an external logging service. Batch jobs, backup processes, and data replication tasks can generate enormous volumes of traffic in short windows.

Traffic that stays within AWS - between services in the same region, or between a private subnet and an AWS-managed service - does not always need to travel through a NAT Gateway at all. Whether it does depends on how the architecture is built and which AWS services are involved.

This distinction matters a great deal when it comes to cost.

Predictability Is the Real Leverage Point

The teams that manage NAT Gateway costs well share one common trait: they understand their traffic patterns before costs become a problem. They know which workloads generate high volumes of outbound traffic, where that traffic is going, and whether there are architectural options that reduce the data processed by the NAT Gateway.

Predictable traffic is manageable traffic. Unpredictable traffic, without proper monitoring in place, tends to show up as a surprise on the monthly invoice.

Establishing baselines early, tracking data processing metrics consistently, and reviewing traffic patterns as workloads grow are practices that make cost conversations much easier to have.

Conclusion

NAT Gateways are a reliable and widely used component of AWS networking. They are also one of the quieter sources of unexpected cost in cloud environments. The billing model is not hidden, but it is easy to overlook during the design and deployment phase when the focus is on getting things to work.

Understanding how the data processing charge accumulates, why visibility tends to break down across distributed workloads, and where traffic patterns differ in cost impact is the first step toward managing these costs with any confidence. The teams that treat network traffic as a cost dimension from the start are far less likely to be caught off guard later.