There is no single monthly price for self-hosted LLM inference on Kubernetes. Your cost depends on the model and serving setup, accelerator and region, traffic and context lengths, performance targets, utilization, and the rest of the cluster. The useful number is one measured against your workload: benchmark representative traffic, then divide the billed compute cost by the input and output tokens it actually serves.
What determines the cost?
Kubernetes is the deployment environment, not a pricing model or a guarantee of savings. GPU-backed deployments are documented for vLLM on Kubernetes and for vLLM on GPU nodes in Amazon EKS, but deployment guidance establishes feasibility—not a universal cost advantage.
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- Model and serving configuration: The model, quantization, serving stack, and hardware needed to fit and run it affect both capacity and cost.
- Traffic shape: Input and output token volumes, context lengths, concurrency, and the mix of short and long requests change how much work the accelerators perform.
- Performance target: Required time to first token, per-token generation speed, and latency percentiles can constrain how much traffic one configuration can serve.
- Billing and utilization: Accelerator price and billed hours set a major cost component; idle time reduces the amount of useful work delivered for that spend.
- Supporting operations: Account for the other cluster resources and operational costs in addition to the GPU. Benchmark cost examples are not a complete production bill.
What does a real benchmark figure look like?
Google Cloud’s GKE Inference Quickstart reports this specific benchmark profile: an a3-highgpu-1g instance with an NVIDIA H100 80GB, serving gpt-oss-20b with vLLM. At the profile’s saturation inflection point, it reports estimated costs of $0.009 per million input tokens and $0.035 per million output tokens, output throughput of 13,335 tokens per second, normalized time per output token of 67 ms, and time to first token of 297 ms. These are USD estimates under the quickstart’s region assumptions, not a general Kubernetes rate or a promised production result. Google cautions that actual billing may differ from the estimates and is subject to GKE pricing.
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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →| Profile | Estimated input cost | Estimated output cost | Output throughput | Latency measures |
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| Google Cloud GKE Inference Quickstart benchmark, accessed 2026: a3-highgpu-1g, NVIDIA H100 80GB, vLLM, gpt-oss-20b; saturation inflection point, USD under the quickstart’s region assumptions | $0.009 per million input tokens | $0.035 per million output tokens | 13,335 tokens per second | 67 ms normalized time per output token; 297 ms time to first token |
Do not read the output-token figure as the input-token price: this profile gives them separately. Nor should its throughput be treated as an ordinary production rate. It describes a particular benchmark profile and operating point; another model, traffic mix, region, or service target can produce a different result.
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How do you estimate your own monthly and per-token costs?
- Define the workload. Specify the model, serving stack, quantization, expected input/output token mix, context lengths, concurrency, and latency targets. Include the quality and response-time requirements that a candidate configuration must satisfy.
- Choose candidate hardware and region. Check the provider’s price for the exact accelerator service and configuration you intend to use. A benchmark estimate is not a substitute for that price or a final 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. Google Cloud’s GKE guidance recommends benchmarking and tuning; its token-level measures help reveal trade-offs that request counts alone can hide.
- Calculate effective token cost. Divide the accelerator spend over the measurement period by the corresponding input or output tokens served, calculating the two separately. To express the result per million tokens, multiply the per-token result by 1,000,000. For a monthly estimate, use the billed accelerator hours expected for the month and add the other costs you have included.
- Make the utilization assumption explicit. State whether the estimate assumes continuous use of measured serving capacity or includes expected idle time. If the benchmark ran at saturation, do not assume a production service can sustain that rate while meeting its latency target.
- Compare only equivalent service. Compare configurations against the same model-quality requirement, context length, concurrency, traffic mix, throughput, and latency targets. A lower cost per token is not a useful win if it reflects a materially different workload or service level.
Requests per second alone can mislead because requests contain different numbers of tokens. Google’s GKE guidance defines token-level throughput measures and cautions that request rate may not reliably represent LLM throughput when context lengths vary. Measure latency alongside token throughput: time to first token and normalized time per output token show how a configuration behaves under load, not just how many tokens it processes.
How should you compare accelerators?
Use hardware recommendations as candidates to test, not a universal ranking. Google Cloud’s GKE guidance identifies NVIDIA L4 as an option for small models and RTX PRO 6000 as a cost-effective option for models under 30 billion parameters and image generation. Those examples do not establish that either accelerator is cheapest for every model, region, or production workload.
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For each candidate, compare the accelerator and region price with measured input and output token throughput, time to first token, per-token latency, required context and concurrency, and expected utilization. Include supporting cluster costs and operational constraints in the total-cost view. The hardware that looks cheapest by hourly price may not deliver the required quality or service level at the workload’s actual traffic shape.
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How can you attribute inference costs to workloads?
Allocation tooling can help connect infrastructure spend to model-serving activity. CNCF describes an OpenCost and llm-d integration that combines GPU allocation costs with vLLM prompt and generation token metrics and processing-time metrics. Use that kind of allocation to understand how costs map to workloads, then validate it against provider bills and your own workload accounting; an allocation measure is not itself proof of the final bill.
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