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There is no universal cost per million inference tokens. Your effective price depends on the model, input-to-output mix, cache hits, geography, latency target, traffic pattern, utilization and whether you use an API, provision capacity or run GPUs yourself. Compare those variables on one representative workload; a list price alone cannot identify the cheapest design.
What “inference cost” includes
Inference is the compute used to generate a model response after a request arrives. A useful cost figure includes more than the provider’s advertised token rate. Count the tokens sent, tokens generated, cache treatment, regional or feature surcharges, capacity purchased but not used, and the operational infrastructure required to serve the model.
Hosted pay-as-you-go APIs
Most APIs separate input tokens from output tokens. A workload with long prompts and short answers can therefore have a very different bill from one with short prompts and long answers, even when both requests contain the same total number of tokens. Prompt caching can lower the price of repeated context: Anthropic describes cache reads as being charged at a fraction of its standard input rate. Its pricing documentation also lists a 1.1× multiplier for US-only inference on supported Claude model generations and deployments, with exceptions and platform-specific details. Check the live model and deployment terms at Anthropic’s Claude pricing documentation before calculating a quote.
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Provisioned throughput
Provisioned services sell reserved capacity rather than an unlimited stream of on-demand requests. Microsoft defines provisioned-throughput utilization as PTUs consumed during a period divided by PTUs deployed during that period. Its estimator first uses prompt tokens minus cached tokens and the requested max_tokens, then corrects the estimate with actual usage. At capacity, a request can receive an immediate HTTP 429 with retry guidance; this mechanism does not queue accepted work. Details are documented in Microsoft’s provisioned-deployment guide.
Google Cloud similarly describes model-specific scale units and burndown rates. Provisioned throughput is tied to a project, region, model and version, and unused throughput does not accumulate or carry over. Use the current model table and estimator at Google Cloud’s provisioned-throughput documentation; an example rate for one model cannot be transferred to another.
Self-hosted inference
Running your own GPU exposes two different costs:
- Usage-based cost: infrastructure consumed while the model is actively processing tokens.
- Allocation-based cost: GPU reservation, model loading, networking, storage, orchestration and other fixed or semi-fixed costs spread across the tokens actually served.
The allocation figure is the relevant one for a build-versus-buy decision. A server that is busy only part of the day still incurs reservation and hosting costs while idle. As the CNCF OpenCost article explains, “A model serving light traffic will have a higher allocation-based cost per token because fixed hosting costs—GPU reservation, model loading, and infrastructure components—are spread across fewer tokens.”
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Worked cost figures—and what they do and do not prove
| Source and scenario | Reported figure | How to interpret it |
|---|---|---|
| CNCF OpenCost illustrative case | $1.00 usage-based and $4.00 allocation-based per million tokens at 25% utilization; $2.00 external API comparison | Illustrative accounting example, not a universal hardware or API price |
| Hugging Face Inference billing example | 10 seconds × $0.00012 per second = $0.0012 | Documentation example for compute-time billing after included credits; hardware price and service scope must be checked currently |
| GPT-OSS-120B on H100 | Approximately $0.09 per million tokens at 66 tokens per second per user, using vLLM | NVIDIA page reporting a SemiAnalysis InferenceX benchmark as of April 2026 |
| GPT-OSS-120B on B200 | Approximately $0.02 per million tokens at 55 tokens per second per user, using TensorRT-LLM | NVIDIA reports this as roughly 4.5× cheaper than the H100 comparison; it is not a universal price quote |
The CNCF example defines utilization as usage-based cost per million tokens divided by allocation-based cost per million tokens. That relationship applies only to the cost bases and assumptions in that article; it is not a general GPU-utilization formula. Read the complete example at CNCF OpenCost.
Hugging Face documents billing as compute time multiplied by the price of the underlying hardware. Its page states that, as of July 2025, HF-Inference focused mostly on CPU inference, a dated service-scope statement that should be rechecked. See Hugging Face’s pricing documentation.
NVIDIA’s benchmark figures are conditional results, not a promise for every model or deployment. They preserve the model, GPU, serving stack, per-user throughput and benchmark date at the H100 FAQ. A different quantization, batch size, context length, concurrency target or software stack can change both throughput and cost.
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How to calculate your own cost per million tokens
- Define the workload. Record requests per hour, peak and off-peak traffic, average and percentile prompt length, generated length, concurrency, latency target and required region.
- Separate token classes. Measure input tokens, output tokens, cached input tokens and uncached input tokens independently. Do not substitute total characters or requests for tokenizer output.
- Choose the quality target. Test candidate models on the real task, including accuracy, refusal behavior, tool use and structured-output reliability. A cheaper model that requires retries or human correction may cost more per successful result.
- Price the hosted alternatives. Apply the current input and output rates, cache-read terms, regional multipliers and any provisioned-capacity commitment to the same token trace.
- Price self-hosting fully. Add GPU reservation or purchase amortization, electricity or cloud instance charges, model loading, storage, networking, orchestration, monitoring, engineering and failover capacity.
- Divide by tokens actually served. For allocation-based cost, divide the period’s complete infrastructure cost by tokens delivered during that period, not by theoretical GPU capacity.
- Stress peak demand. Recalculate with peak concurrency and the required headroom. A design that is cheap at average traffic can throttle or require extra replicas at the busiest minute.
- Validate with production telemetry. Compare estimated and actual token counts, cache hits, latency, throttling and GPU occupancy, then update the model.
Why input and output mix changes both price and capacity
Two systems with identical monthly token totals may need different amounts of hardware because decoding output tokens is often more sequential than processing a prompt. Provisioned services therefore use model-specific token weights rather than a universal input-to-output conversion.
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Requested max_tokens matters even when the model usually stops early: Azure’s estimator uses the requested maximum before correcting the estimate with actual usage. Set it high enough for valid answers but not so high that it reserves avoidable capacity. Measure the resulting truncation, latency and 429 rate rather than choosing a limit by habit.
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Utilization: the hidden multiplier in GPU economics
Utilization is not simply “GPU percent busy.” For cost accounting, ask how many billable tokens the reserved capacity served during the paid period. Fixed costs divided by fewer tokens raise allocation-based cost. At 25% utilization in the CNCF illustration, $1.00 of usage-based cost becomes $4.00 of allocation-based cost per million tokens.
Why utilization falls
- Traffic is bursty, with long idle periods between peaks.
- Capacity is reserved for failover or an availability target but rarely used.
- Large models require a replica even when request volume is small.
- Long context windows consume memory and limit batching.
- Requests are unevenly distributed across replicas or regions.
- Overly conservative
max_tokensor low batching leaves compute unused.
How to raise useful utilization
- Batch compatible requests where your latency objective permits it.
- Route flexible, noninteractive jobs to off-peak capacity.
- Scale replicas with measured demand, while retaining enough headroom for bursts.
- Use prompt caching for repeated system instructions or retrieved context.
- Track tokens per GPU-hour, not only average device utilization.
Higher utilization is not automatically better if it causes queueing, tail-latency violations or retries. Include service-level objectives in the cost calculation.
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Choosing among API, provisioned capacity and self-hosting
| Decision axis | Pay-as-you-go API | Provisioned throughput | Self-hosted GPUs |
|---|---|---|---|
| Billing basis | Usually input and output tokens, with feature and geography rules | Reserved capacity such as PTUs or model-specific scale units | Infrastructure allocation plus active compute and operations |
| Best fit | Variable or uncertain demand and fast deployment | Steady demand with a known throughput and latency target | Large, predictable workloads or requirements for deployment control |
| Main risk | Unexpected token mix, cache or regional charges | Paying for unused reservation and receiving capacity errors during bursts | Idle GPUs, engineering overhead and hardware lifecycle cost |
| What to measure | Tokens, cache hits, response latency and retries | Consumed versus deployed capacity, throttling and burst profile | Tokens per GPU-hour, allocation cost, occupancy and operational incidents |
Make the comparison on one workload and one geography. Include quality, latency, throughput, peak demand, off-peak demand, cache rate, idle capacity and operational effort. A single “price per million tokens” strips away the variables that usually decide the result.
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Using GPU benchmarks without overgeneralizing
A benchmark number is a point on a specific configuration, not a property of a GPU in isolation. Preserve at least these fields when recording one:
- Model and parameter variant
- GPU and number of GPUs
- Serving software and version
- Quantization or precision
- Prompt and output lengths
- Concurrency or tokens per second per user
- Whether the cost is active compute or fully allocated capacity
- Benchmark date and attribution
The NVIDIA figures above compare different GPUs and serving stacks as well as different per-user throughput values, so the reported 4.5× difference cannot be applied to an arbitrary workload. Reproduce the workload or run a small pilot before committing capital.
A practical decision sequence
Start with an API when demand is uncertain
Capture a representative token trace and quality score first. This gives a real baseline without committing to idle hardware. Revisit the decision after traffic distribution and cache behavior stabilize.
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Use the provider’s current estimator for the exact model, region and version. Size for the measured input/output mix, then reserve headroom for bursts. Confirm what happens at capacity and monitor 429 responses rather than assuming requests will queue.
Consider self-hosting when allocation economics and control justify it
Self-hosting can make sense when sustained volume keeps GPUs busy, when data or deployment control is decisive, or when a supported open model meets the quality target. Include standby replicas, upgrades, security, observability and staff time in the allocation denominator.
What to monitor after launch
- Input, output and cached tokens by model and route
- Cost per successful request and per million tokens
- Cache-hit rate and cache-read charges
- Tokens per second, queue time and tail latency
- Provisioned capacity consumed versus deployed
- GPU-hours, active occupancy and idle allocation
- 429s, retries, timeouts and fallback-model use
- Quality metrics and human correction rate
Prices, cache terms, model versions, capacity weights, geography adjustments, GPU benchmarks and hardware availability change frequently. Recheck provider documentation and recalculate with actual traffic whenever one of those inputs changes; no universal cheapest model or self-hosting break-even point follows from the figures above.
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