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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsEstimate an AI feature by modeling its expected workload, pricing each request path against current provider rates, and adding the infrastructure and services the design requires. There is no reliable universal monthly price: the result depends on request volume, prompt and response size, model, architecture, and service mix. The method below turns those assumptions into a scenario-based estimate you can update as testing produces real usage data.
What belongs in a total-cost estimate?
Use this planning equation:
Estimated monthly cost = model usage charges + supporting infrastructure and service charges.
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For a token-priced model, estimate each request type separately: expected requests multiplied by expected tokens in each billed category, multiplied by the current price per token. Sum those costs, then add separately billed operations and the services used by the architecture. This is a planning framework, not a universal provider billing formula; providers may charge for tools, images, audio, hosting, or other operations in different ways. AWS recommends accounting for query volume and patterns, prompt and completion token usage, token prices, and infrastructure costs in a preproduction model (AWS Prescriptive Guidance).
The Tool Desk
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- Prompt and completion size: Estimate input and output token distributions for each request path, including repeated context. Include cached-input tokens only if the selected provider and model support that billing category. OpenAI recommends projecting token utilization from traffic, interaction frequency, and data volume (OpenAI latency optimization guidance).
- Model and inference rates: Apply the selected model’s current rates to each applicable usage category. Rates vary by model and modality, and may depend on conditions such as regional processing. OpenAI’s live pricing documentation lists input, cached-input, and output categories, as well as a regional-processing charge for eligible models released from March 5, 2026 (OpenAI API pricing).
- Architecture and infrastructure: Include compute, storage, vector database storage and queries, guardrails, and networking where the design uses them. Self-hosting also requires accounting for infrastructure uptime; managed inference and self-hosting have different cost structures (AWS Prescriptive Guidance; AWS guidance on LLM costs on EKS).
- Non-text operations and add-ons: Check pricing for image or audio operations, search grounding, batch processing, caching, and tools if the feature uses them. Supported modes can differ in price, latency, and reliability; caching may also have a storage charge. Verify the chosen provider’s current terms rather than assuming one model’s rates apply to every mode (Google Cloud Vertex AI generative AI pricing).
Provider pricing pages are live tariffs, not timeless benchmarks. Recheck them when making the estimate and before launch; no single monthly dollar figure is meaningful without a workload, geography, and architecture.
How to build the estimate
1. Define request types and the unit that matters
Describe the feature’s job, then separate materially different paths. A short classification call, a long-form generation, a retrieval-augmented answer, and a multi-step tool workflow can have very different token use and service costs. Track both expected monthly spend and a useful unit such as cost per successful task, so the estimate can inform a product decision rather than just report a bill.
2. Create low, expected, and high scenarios
For each request type, record expected volume, peak behavior, input and output size, and likely retrieval, tool calls, or retries. Keep assumptions visible and distinguish what is measured from what is guessed. Do not treat an arbitrary percentage buffer as evidence: AWS recommends maintaining and validating the model as the application is tested, but does not prescribe a standard buffer.
3. Price each candidate model and architecture
For managed APIs, multiply expected usage by current rates for the relevant token categories and add separately billed services. For a self-hosted option, estimate capacity, uptime, storage, and networking. Compare alternatives using the same traffic and performance assumptions; otherwise, the lower estimate may simply describe a smaller workload.
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When comparing options, assess the dimensions together:
| Dimension | What to compare |
|---|---|
| Expected total cost | Model usage plus infrastructure and separately billed services at the same workload. |
| Task quality | Whether the candidate meets the feature’s acceptance criteria. |
| Latency and reliability | Whether the service mode meets response-time and reliability requirements; provider-specific modes can trade price against latency or service guarantees. |
| Operational burden | Managed token billing versus responsibility for self-hosted capacity, uptime, storage, and networking. |
| Data and deployment requirements | Provider terms and regional requirements for the intended geography, including any pricing conditions that apply. |
4. Measure quality and cost together
Run representative tasks against candidate models and record actual token usage, outcomes, and response times. Start with a lower-cost candidate and move to a more capable one only when evaluation shows the cheaper option misses the required quality or latency bar. OpenAI identifies model choice, token count, shorter prompts, fine-tuning, and caching as possible cost levers; AWS recommends testing smaller models and evaluating whether they satisfy the workload (OpenAI latency optimization guidance; AWS Prescriptive Guidance).
5. Add service costs and owners
Review the architecture for compute, data storage, vector retrieval, guardrails, networking, and other paid services. For each line, write down the usage assumption and who will update it. This makes it easier to see which cost is driven by the model and which depends on product or infrastructure choices.
6. Replace estimates with observed usage
Use API responses, dashboards, and application measurements to update token counts and request volumes as testing progresses. Revisit provider rates before launch and periodically afterward. The estimate should change when the workload, model, architecture, or pricing changes—not remain a fixed spreadsheet inherited from an early prototype.
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How to keep the estimate useful
- Separate request types instead of applying one average token count to every interaction.
- Keep input, cached input, output, and non-text operations distinct whenever the provider bills them differently.
- Show low, expected, and high workload scenarios and record the assumptions behind each.
- Compare models on the same tasks, traffic, quality bar, and performance needs.
- Include the supporting services the architecture actually uses, rather than assuming the model invoice is the full cost.
- Recheck official pricing and deployment conditions for the relevant provider, model, and region.
Without a specified workload, geography, architecture, and quality threshold, a responsible dollar total cannot be calculated. The defensible result is a transparent estimate with explicit assumptions, updated using measured usage as the feature is tested.
Quick Recap
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