Use Gemini CLI for cost-effective LLM workloads on GKE

This formula assumes an output token costs four times as much as an input token. The reason for this heuristic is that the prefill phase (processing input tokens) is a highly parallel operation, whereas the decode phase (generating output tokens) is a sequential, auto-regressive process. You can ask Gemini CLI to change this ratio for you to fit your workload’s expected input/output ratio.

The key to cost-effective LLM inference is to take a data-driven approach. By relying on benchmarks for your workloads and using metrics like cost per token, you can make informed decisions that directly impact your budget and performance.

Next steps

GKE Inference Quickstart goes beyond cost insights and Gemini CLI integration, including optimizations for storage, autoscaling, and observability. Run your LLM workloads today with GKE Inference Quickstart to see how it can expedite and optimize your LLMs on GKE.