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Self-hosting an LLM is not automatically cheaper than using an API. An API makes usage costs visible as model-specific token charges; self-hosting replaces those charges with GPU capacity and the cost of running a dependable service. The better choice depends on your workload, required model capability, service targets, and the people and infrastructure available to operate it.
What you are actually comparing
There are three practical options: a hosted provider API, self-hosting on hardware you own, and self-hosting on rented cloud GPUs. They differ not only in price, but also in how quickly capacity can change, how much of the serving stack you control, and who is responsible for keeping it available.
| Option | Where costs arise | What to examine |
|---|---|---|
| Hosted API | Model- and tier-specific input, cached-input where available, and output token charges; other applicable service or processing options may affect the bill. | Chosen model and tier, usage mix, applicable caching or batch options, region, and provider pricing effective date. |
| Self-hosted on owned hardware | Hardware acquisition and lifecycle, power and facilities, software and infrastructure, and engineering and operations. | Capacity actually used, peak throughput, redundancy, upgrades, and the effort to maintain the service. |
| Self-hosted on rented cloud GPUs | GPU capacity and related infrastructure, plus engineering and operations. | Provisioned capacity versus demand, utilization, scaling speed, and the costs of running and maintaining the serving stack. |
A GPU-hour price is not directly comparable with an API token rate. The first prices capacity over time; the second prices processed tokens. Compare lifecycle cost for the same workload and service outcome instead.
How to estimate API cost
API rates depend on the provider, model, and pricing tier. Some pricing pages distinguish input, cached input, and output tokens; regional processing or other options can also change the applicable rate. For example, OpenAI’s pricing documentation describes a 10% uplift for eligible regional-processing endpoints for models released on or after March 5, 2026. Anthropic’s Claude Fable page lists a 1.1x multiplier for US-only inference. These are provider- and offering-specific terms, not general rules for all APIs.
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Google’s Gemini API pricing documentation notes that some listed prices change on January 1, 2027. That date is a reminder that a comparison needs an effective date; do not treat a rate as timeless. Check the selected model, endpoint, tier, and region on the provider’s pricing page before using a figure in a budget.
Build the estimate from your usage
- Choose the model and tier. Use a model with the capability your application needs, and record the relevant input, cached-input, and output rates when applicable.
- Measure a representative workload. Capture requests per day and at peak hour, input and output token distributions, context lengths, and concurrency.
- Apply only relevant pricing options. Include caching, batch processing, or regional processing only if the application uses that option and the chosen model and endpoint qualify.
- Calculate usage cost. For each usage category, multiply expected token volume by its applicable rate, then add the categories. Keep peak or unusually heavy use separate from the expected case if it changes the estimate materially.
- Date and preserve the assumptions. Record the provider, model, tier, region, pricing effective date, and usage assumptions alongside the estimate.
How to estimate self-hosting cost
Self-hosting economics depend on how much capacity must be provisioned, how efficiently it is used, and what it takes to serve requests reliably. A lightly used server can remain costly relative to the requests it handles; high utilization can change the economics, but it does not remove the need to meet peak demand or service targets.
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Include the full operating picture
- Compute and utilization: the hardware or rented GPU capacity needed for the selected model, runtime, concurrency, and target latency, including capacity that is idle outside busy periods.
- Infrastructure: power and cooling or other facilities costs for owned equipment; storage, networking, deployment, monitoring, and redundancy as applicable.
- People and maintenance: engineering and operations effort for setup, reliability, upgrades, monitoring, and recovery.
- Lifecycle and resilience: hardware lifecycle or ongoing rental, provisioned headroom, and the capacity needed to recover from failures or handle demand peaks.
A 2025 preprint proposing LCOAI argues that API token charges, GPU-hour billing, or conventional total cost of ownership alone may miss lifecycle costs. It offers a framework and examples, including API use and self-hosted LLaMA-2-13B; it is a proposed framework, not an established industry standard.
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What published GPU figures can—and cannot—tell you
NVIDIA’s H100 FAQ cites SemiAnalysis InferenceX benchmark figures from April 2026 for GPT-OSS-120B. The figures are useful as conditional examples, not as typical production costs or a matched hardware comparison.
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| GPU and setup | Cited figure | Conditions reported |
|---|---|---|
| H100 with vLLM | Approximately $0.09 per million tokens | GPT-OSS-120B at 66 tokens per second per user; NVIDIA citing SemiAnalysis InferenceX, April 2026. |
| B200 with TensorRT-LLM | $0.02 per million tokens | GPT-OSS-120B at 55 tokens per second per user; NVIDIA citing SemiAnalysis InferenceX, April 2026. |
The two examples use different runtimes and report different per-user speeds, so they do not isolate the effect of GPU hardware. Treat them as benchmark results under the stated conditions, not as a forecast for your workload. Measure the model and runtime you intend to deploy on the target hardware.
Compare service outcomes, not just token prices
A cheaper token is not a better deal if the model performs worse on the task or creates more review and correction work. Likewise, a low estimated infrastructure cost is not enough if the service cannot meet peak latency or availability needs.
- Capability and quality: test the candidate model on representative tasks and compare task success, not just generated tokens. Include downstream correction or human review effort when it differs.
- Latency and throughput: assess response time and sustained throughput at expected and peak load, using the application’s target context lengths and concurrency.
- Reliability and recovery: define availability expectations and how quickly service must recover; establish who will monitor and maintain a self-hosted deployment.
- Privacy and data location: identify any actual requirements for data handling, geography, or control over model weights, then account for the cost of meeting them.
- Flexibility: consider how readily each option can scale up or down as demand changes, and whether customization or deployment control is necessary.
The available pricing and benchmark examples do not establish that one option universally wins on these dimensions. They are evaluation criteria to test against your own requirements.
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A workload-based decision process
- Write down the workload trace. Record requests per day and peak hour, input and output token distributions, context lengths, concurrency, and latency target.
- Set the minimum acceptable capability. Test models against representative application tasks. If model quality differs, include success and correction effort in the comparison.
- Estimate API spend. Apply the actual provider rates and options for the selected model, tier, endpoint, and region to the measured usage mix.
- Estimate self-hosted lifecycle cost. Benchmark the selected model and runtime on target hardware, then include realistic capacity, utilization, infrastructure, redundancy, and operations effort.
- Run multiple utilization cases. Compare low, expected, and high utilization; idle capacity can materially alter self-hosted cost per served request.
- Compare like with like. Use cost per successfully served request or token at the same quality and service target, and check peak latency and throughput as well as average cost.
Do not infer a general break-even request volume from an API rate or a GPU benchmark. The threshold depends on the workload, capability requirement, utilization, service target, and full operating costs; the cited examples do not establish a typical-organization break-even point.
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When each approach may fit
A hosted API may fit when
- Usage varies enough that provisioning and maintaining dedicated capacity would be difficult to justify.
- You want token-based pricing and do not need to operate the inference stack yourself.
- The selected API model meets the application’s quality, latency, data-handling, and location requirements.
Self-hosting may fit when
- You have a concrete requirement for control over weights, deployment, or data location and can implement it.
- Your workload and service targets support a realistic capacity plan with acceptable utilization.
- Your team can benchmark, monitor, maintain, and recover the serving system, and that operational effort is included in the cost.
Neither list guarantees a lower bill. Treat control, customization, and operational responsibility as requirements to evaluate and cost, rather than assigning them an assumed monetary benefit.
Quick Recap
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