There is no universal monthly price for self-hosted LLM inference on Kubernetes. The cost depends on the accelerator and region, the model and serving setup, the input and output token mix, workload utilization, and the latency and capacity you need. A useful estimate comes from benchmarking that specific workload, then comparing its measured token throughput with the billed accelerator time and the rest of the cluster’s costs.
Why one monthly price would be misleading
Two deployments can serve the same model and still have different costs per token. Hardware choice, model configuration, context length, concurrency, and traffic patterns all affect how much useful work a GPU delivers during its billed time. Input and output tokens also need to be tracked separately: their throughput and reported costs can differ.
Requests per second alone is a weak comparison when requests contain different numbers of tokens. Google Cloud’s inference guidance recommends token-level throughput measures and latency measures such as time to first token and normalized time per output token. A cost figure without its workload and performance conditions is therefore difficult to apply to another deployment.
How to estimate your deployment’s cost
- Define the workload. Record the model, serving stack, quantization or other serving configuration, expected input and output tokens, context lengths, concurrency, and latency targets. Confirm that the model fits the proposed hardware under the intended settings.
- Choose candidate hardware and a region. Get the applicable price for the exact accelerator and service configuration from the provider. A benchmark’s estimated cost is not a substitute for that price or for your actual bill.
- Benchmark representative traffic. Measure input and output tokens per second, time to first token, normalized time per output token, latency percentiles, GPU utilization, and memory or KV-cache pressure where available. Test the context lengths and concurrency you expect to serve, rather than relying on a generic throughput number.
- Convert capacity into effective token cost. Divide the accelerator cost over the measured serving period by the input or output tokens delivered in that period. Report input and output costs separately, or state the mix used for any blended figure. Make clear whether the calculation assumes continuous use or accounts for idle capacity.
- Add costs outside the accelerator. Include other cluster resources and operational overhead in a separate total-cost view. Keep these costs distinct from a GPU-only benchmark so readers can see what is—and is not—included.
For a monthly estimate, use the measured workload and the hours the capacity will actually be provisioned, with the provider’s price for that configuration. A continuous-use calculation is a capacity scenario, not evidence that your service will achieve that utilization in production.
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What a published benchmark can—and cannot—tell you
Google Cloud’s GKE Inference Quickstart (accessed 2026) reports this example profile for an a3-highgpu-1g with an NVIDIA H100 80GB, serving gpt-oss-20b with vLLM. These are USD estimates and observed benchmark figures at a saturation inflection point, under the profile’s region assumptions—not a general Kubernetes price or a guaranteed production rate.
| Metric | Reported profile value |
|---|---|
| Estimated cost per million input tokens | $0.009 |
| Estimated cost per million output tokens | $0.035 |
| Output throughput | 13,335 tokens per second |
| Normalized time per output token | 67 ms |
| Time to first token | 297 ms |
Google cautions that actual billing may differ from these estimates and is subject to GKE pricing. The profile is useful as a concrete example of how token cost and latency can be presented together; it should not be used as a quote for a different model, region, accelerator, serving configuration, or traffic shape.
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Account for idle capacity and the whole cluster
GPU allocation is only one part of operational accounting. A service may reserve accelerator capacity even when demand is low, while its token output fluctuates with request lengths and concurrency. Cost per token should therefore be calculated over a representative period and labeled with its utilization assumptions. If the service has distinct busy and quiet periods, a single saturation benchmark will not describe the monthly average by itself.
Cluster resources beyond the GPU and engineering or operations effort also matter to a total-cost estimate. The cited benchmark profile does not establish a complete bill for every production deployment. Cost-allocation tooling can help connect resource costs to usage: CNCF describes an OpenCost and llm-d integration that combines GPU allocation costs with vLLM prompt and generation token metrics and processing-time measurements. Treat such allocation as a way to inspect costs, then reconcile it with provider billing and your own workload accounting.
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Choose hardware by measured fit, not a universal ranking
Google Cloud guidance identifies NVIDIA L4 as an option for small models and RTX PRO 6000 as a cost-effective option for models under 30B parameters and image generation. These are workload-specific examples, not proof that either accelerator is cheapest for every model or deployment. The relevant comparison is whether a candidate meets the same quality, context, concurrency, throughput, and latency requirements at a lower effective cost.
Benchmark at least the configurations you are considering under the same representative workload. Compare accelerator and region pricing alongside input and output throughput, time to first token, per-token latency, utilization, idle time, and supporting cluster requirements. If one option meets a different service target, its cost-per-token figure is not an apples-to-apples result.
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Kubernetes enables deployment; it does not guarantee savings
vLLM and AWS EKS document ways to run GPU-backed inference on Kubernetes, and GKE provides inference benchmarking guidance. These demonstrate deployment paths, not a universal cost reduction from Kubernetes. The platform choice should be evaluated with the same workload and total-cost accounting as the accelerator choice.
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