People often think of encryption as something expensive that should be used only when absolutely necessary. The assumption is that encryption burns a lot of CPU, adds overhead, and risks increasing latency. Because of that, teams sometimes choose to skip extra encryption, even when it would be the smart thing to do.

Public/private key cryptography does use more CPU than sending data in the clear, or than “just” sending everything over HTTPS without any extra layer. Symmetric encryption also has a cost, even if it is usually small on modern hardware. So yes, encrypting content is not free.

But when you compare that cost to what you are already spending to run a language model, the picture changes completely. A typical request that sends data to a model involves tokenization, network transfer, and, most importantly, heavy inference compute on GPUs or other accelerators. That inference step dominates the resource usage by a huge margin.

If a request/response workflow is mostly about sending data to and from a language model, is it really worth dropping encryption to save some CPU cycles? You are already paying for massive amounts of compute to run the model. In that context, the overhead of encrypting a few kilobytes or even megabytes of text is a rounding error.

This is where the usual reasoning breaks down. People worry about “extra CPU” for encryption, and as a result avoid using public/private key technologies or additional encryption layers, even for sensitive prompts and responses. But if the data is important enough to send to a powerful model, it is usually important enough to protect properly on the way there and back.

A more realistic way to think about it is: if you can afford the compute cost of the model, you can almost certainly afford the CPU cost of encrypting the content around it. The trade-off is clear: a tiny increase in CPU usage versus a potentially large improvement in privacy and security.

So when your system is already spending a crazy amount of compute on running a language model, skipping encryption because of CPU considerations is rarely a good argument. Instead of asking “Is encryption too expensive here?”, the better question is: given what we already spend on model compute, is it really worth not encrypting this data?