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AI inference has no universal price per token. Your real cost depends on the model, input/output mix, cache hits, geography, traffic pattern, throughput target and whether you buy an API, reserve capacity or operate GPUs. Compare those options on one representative workload, then divide the complete bill by the tokens actually served.
What AI inference cost includes
Inference is the computation required to produce a model response after a request arrives. A quoted price per million tokens is only one part of the bill. You also need to account for:
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- Input tokens: the prompt, system instructions, conversation history and retrieved context.
- Output tokens: the generated answer, tool calls or structured result.
- Cache treatment: some services charge cached prompt reads at a discounted rate, while others count them differently for capacity.
- Capacity: reserved throughput, minimum commitments, GPU reservation and idle time.
- Performance: latency, tokens per second, concurrency and peak-demand headroom.
- Operations: model loading, monitoring, networking, storage, upgrades and on-call work for a self-hosted deployment.
For a meaningful comparison, measure total spend for the same model quality, region, service features, latency objective and traffic distribution. Then calculate:
effective cost per million tokens = total inference cost ÷ total tokens served × 1,000,000
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Use actual input and output counts, not a nominal maximum context or maximum output setting.
How hosted API billing works
Pay-as-you-go APIs commonly price input and output tokens separately. The ratio between them matters: a summarization workload may consume many input tokens and few output tokens, while an agent that reasons and calls tools can generate substantially more output. Two applications with the same request count can therefore have very different bills.
Pricing can also vary by deployment geography and features. Anthropic’s pricing documentation describes prompt caching as reuse of prompt portions, with cache reads charged at a fraction of the standard input rate. It also documents a 1.1× multiplier for US-only inference on specified Claude model generations and deployments, subject to exceptions and platform details. Check the live Anthropic pricing documentation for the exact model and deployment you use.
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Neither example is a universal token rate. Convert a time-based or feature-adjusted bill into a token cost only after measuring the tokens your workload actually serves.
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Hosted API, provisioned throughput or self-hosting?
| Option | Billing basis | Capacity behavior | Operational burden | Usually fits |
|---|---|---|---|---|
| Pay-as-you-go API | Input and output usage, with model- and feature-specific rates | Scales with requests but may enforce rate limits | Lowest infrastructure responsibility | Variable demand, rapid launches and teams without GPU operations |
| Provisioned throughput | Reserved capacity units or throughput purchased for a period | Predictable capacity; unused allocation can still cost money | Capacity planning, quota and throttling management | Steady traffic with strict latency or throughput targets |
| Self-hosted GPUs | Hardware and infrastructure allocation plus active compute | Fixed capacity until you add or remove servers | Highest: serving stack, drivers, scaling, monitoring and upgrades | Large or sensitive workloads that can keep hardware busy |
Compare all three with identical assumptions: model version, region, input/output distribution, cache hit rate, peak and off-peak volume, latency target, quality requirement and support features. A single list price per million tokens omits those variables.
Why tokens and token mix change the answer
Input versus output
Providers often publish separate input and output rates because generation is not equivalent to reading a prompt. Record both counts for every request class. Include hidden or indirect tokens such as system prompts, conversation history, retrieved documents and tool results. If your application retries, count the retried tokens in the cost ledger.
Maximum output settings
A large max_tokens setting can affect provisioned capacity even when responses are usually short. Microsoft Azure Foundry documentation says a provisioned request is initially estimated using prompt tokens less cached tokens plus the requested max_tokens, then corrected using actual token usage. At capacity, a request can receive an immediate HTTP 429 with retry guidance; this mechanism does not queue accepted requests. Set realistic maxima, implement backoff and monitor rejected bursts. Details are in Microsoft’s provisioned deployment guide.
Model-specific token weights
Azure’s sizing documentation gives examples where one output token counts as eight input tokens for gpt-5 and four input tokens for gpt-4.1 when calculating utilization. Some models use different relationships from the standard price ratio. Those figures are examples tied to the cited documentation, not a general conversion between input and output tokens. Use the current model table and estimator at Azure’s provisioned-throughput sizing guide.
Prompt caching, geography and service features
Caching can lower the charge for repeated long prefixes, but it does not automatically make every request cheap. Measure cache-hit rates, cache write or storage rules, minimum cacheable lengths and invalidation behavior for your selected model. A workload with a 90% cache hit rate can have a very different effective input cost from one with identical prompts that never reuse a prefix.
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Geography can add a premium or change available capacity. Keep region and data-residency requirements fixed while comparing providers; otherwise, a lower nominal rate may not represent the service you are allowed to use.
Provisioned throughput and utilization
Azure PTUs
Azure defines provisioned-throughput utilization as PTUs consumed during a period divided by PTUs deployed during that period. Because the estimate incorporates requested maximum output and then reconciles actual usage, both request configuration and traffic shape affect the reported percentage. A service that looks lightly utilized on average can still reject bursts if its instantaneous capacity is exhausted.
Google Cloud provisioned throughput
Google Cloud documents model-specific scale units and burndown rates. Unused throughput does not accumulate or carry over, and provisioned throughput is tied to a project, region, model and version. Its worked examples are specific to particular input/output mixes; use the current model table and estimator at Google Cloud’s provisioned-throughput documentation rather than importing an example rate into another workload.
Measure useful utilization
Track tokens served, deployed capacity, queue or rejection rate, latency percentiles and idle time in the same time windows. Average utilization alone can hide a short peak that causes 429 responses or forces you to reserve capacity for hours when little traffic arrives.
Self-hosted GPU cost: active use versus allocation
A GPU can be inexpensive while it is computing and expensive while it is reserved but idle. CNCF OpenCost distinguishes:
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- Usage-based cost: infrastructure consumed during active inference.
- Allocation-based cost: GPU reservation, model loading and infrastructure components spread across the tokens served.
The allocation-based figure is the relevant one for a build-versus-buy decision because it includes fixed capacity that must be paid for even when traffic is light. CNCF’s illustrative case reports $1.00 usage-based cost per million tokens and $4.00 allocation-based cost per million tokens at 25% utilization, compared with an illustrative $2.00 external API price. These are examples, not market-wide prices. The relationship shown in the article is:
Utilization = usage-based cost per million tokens ÷ allocation-based cost per million tokens
That equation applies only to the article’s defined cost bases and measurement assumptions; it is not a universal hardware-utilization metric. CNCF summarizes the reason for the gap: “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.” See the CNCF OpenCost article published August 5, 2026.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What GPU benchmarks can and cannot tell you
Benchmark numbers are conditional results, not price quotes. NVIDIA’s H100 FAQ cites SemiAnalysis InferenceX results, as of April 2026, for GPT-OSS-120B:
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| Hardware and serving stack | Reported cost | Throughput condition |
|---|---|---|
| H100 with vLLM | Approximately $0.09 per million tokens | 66 tokens per second per user |
| B200 with TensorRT-LLM | Approximately $0.02 per million tokens | 55 tokens per second per user |
NVIDIA describes the B200 result as roughly 4.5 times cheaper in that comparison. The figures preserve the model, hardware, serving stack, per-user throughput and benchmark date; they do not predict your retail server cost, utilization, concurrency, quantization, prompt length or quality. Source: NVIDIA’s H100 FAQ.
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A practical cost-calculation workflow
- Define the workload. Record requests per hour, peak concurrency, input tokens, output tokens, retries, cache hits, latency percentiles and required model quality.
- Separate traffic classes. Keep interactive, batch, agentic and background jobs distinct because their token mix and latency needs differ.
- Price the hosted path. Apply the current model’s input and output rates, cache rules, geography adjustment and any minimum commitment.
- Size provisioned capacity. Use the provider’s current calculator with your token mix and maximum output setting. Include headroom for bursts and monitor throttling.
- Build the self-hosted ledger. Include GPU reservation, host CPU and memory, storage, networking, orchestration, observability, model loading, redundancy and engineering time.
- Divide by actual tokens served. Report both active-use and fully allocated cost so idle capacity is visible.
- Validate quality and latency. A cheaper model that fails your accuracy or response-time target is not a lower-cost solution.
How model choice changes total cost
Model names and list prices are poor substitutes for a workload test. A smaller model may reduce token price but require longer prompts, more retries or extra verification calls. A larger model may answer in fewer turns and lower total application cost despite a higher per-token rate. Compare candidate models on the same evaluation set and record:
- Task quality and failure rate;
- Input and output tokens per successful task;
- Time to first token and completion latency;
- Sustained throughput at your concurrency;
- Cache hit rate and context reuse;
- Safety, tool-use and structured-output behavior.
Use the cost of a successful completed task, not just cost per million raw tokens:
cost per successful task = total tokens and infrastructure for attempts ÷ successful tasks
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Using a single universal token price
Problem: input, output, cache and region differences disappear in one number.
Fix: publish separate input/output assumptions and calculate an effective blended rate for your measured mix.
Counting only active GPU seconds
Problem: reservation, idle time and model-loading overhead are omitted.
Fix: report allocation-based cost using the full serving window.
Sizing from average traffic
Problem: short bursts cause throttling while capacity sits idle off-peak.
Fix: model peak concurrency, burst duration, retry behavior and acceptable queueing or rejection.
Quoting a benchmark without conditions
Problem: readers cannot reproduce a number from a different model, stack or throughput target.
Fix: always include model, hardware, serving software, throughput, date and whether the result is usage-based or fully allocated.
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- Choose pay-as-you-go when demand is uncertain, traffic is spiky or infrastructure staffing is limited.
- Choose provisioned throughput when traffic is predictable and you need a defined latency or throughput floor, but verify that utilization remains high enough to justify the reservation.
- Choose self-hosting when you can keep hardware busy, need control over data or model serving, and can operate the complete stack. Compare fully allocated cost with the hosted alternatives, not with an idealized GPU-second rate.
Prices, cache terms, model versions, capacity weights, geography adjustments and benchmark results change frequently. Recheck the linked provider pages and recalculate with your current token lengths, cache rate, request volume, latency target and measured utilization before committing to a deployment.
Quick Recap
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