Generative AI Costs Explained: Make a Budget for a Small Business App

A small group needs an AI product which will convert notes to short drafts. The first demo appears to be inexpensive. But, then the employees provide long notes, require multiple drafts, and spend some time correcting the responses. The monthly cost now includes more work than the group expected at the beginning. A realistic budget should consider the entire process, not just the demo request.

The expenses related to the use of generative AI may vary depending on the service, model, usage, and application. Some text services calculate a portion of the cost by tokens. There may also be costs for storage, search, testing, and support. There is no fixed cost for all AI applications. Look through the current conditions of the service and the planned task.

In this guide, we will explain an easy budget for the small-business application. As an example for the planning, we will use a draft message feature. These statistics on employees and requests are imaginary numbers for our explanation and are not a price offer nor a guarantee of the Digitonix product performance. No advanced mathematics is needed to understand the basic points and to know what questions to ask before developing the feature.

Begin with the finished task

Define the task that should be accomplished. A person gets a draft, checks its facts, edits it and applies supported saving or sending action. This whole process can be measured. The AI request is only a single piece. If one person requires three drafts and a lengthy check, the price of a request cannot reflect the total cost.

Explain what is meant by success. Your initial draft has to contain the proper references and be reviewable. An extremely inexpensive response, which introduces extra supporting tasks, might be inefficient. Compare the current situation with the task needs. The company has to find out what is now easier, what new tasks were introduced, and who will keep the tool further.

Understand tokens in plain words

A token is a small fragment of text for the model. A token may be a word, some part of the word, or some other text fragment. Never assume that one word corresponds to one token. The number of tokens depends on the model and text in question. AWS CountTokens Documentation says that token count is model-dependent.

Input is what the application sends to the model. Output is what the model generates. While many text-based models hosted on the cloud charge based on those figures, the details differ from one hosting service to another. Apply the method of the selected provider. A word count may be used to provide an example, but not as an exact billable token count.

Count normal use before guessing a bill

Judge how often people will be using the task. In our imaginary example, ten workers will request three drafts per day. This means thirty requests per day. Over twenty workdays, this results in six hundred requests without any retries. The above figures show the estimation method only. Your company should use its own work schedule and samples.

Consider the peak and low times. The monthly average is bound to miss a brief peak time. Certain employees may ask more often, and a new addition may affect their behavior. Make sure to keep your assumptions visible. Begin with an estimated range and not a confident number, and revise the plan when the actual usage will give you more correct information on the workload.

Measure short and long inputs

The brief request may be accompanied by a lengthy background note or conversation history. Measure the amount of data the application is sending. The user may see a brief text input box while the system sends other data. Analyze the data and keep what is necessary for the task execution. The budget should cover the entire request process, and not only the brief text entered by the employee.

Test representative samples. Use regular notes and notes that are longer than usual. Set right input and output limits for the task. Long answers are not always more helpful. Trim down unnecessary content, but be careful not to remove any important condition, since it will mislead the draft. Cost reduction must keep all necessary information and context for people to do the task right.

Include repeated requests

People may request a second draft if the first one was confusing. In addition, the app may try again for temporary problems, depending on how it is designed. Such actions will affect usage and cost. Document the reasons behind repeated requests. If there are many retries, it might mean the wording, app problem, or dependency problem instead of increased value.

Define clear policies for retry attempts and limits. There should be no endless attempts if the input cannot be processed. Tell the user what will happen next and let the user continue what was already allowed. Check uncertain results before trying important actions again. Cost design should never make the application save or send anything twice just for completing the delayed request.

Read the current pricing for your setup

Use the provider’s current pricing page for your chosen service and model. For example, the Amazon Bedrock pricing gives information about various cost options along with the related characteristics. Check the useful region, mode, and other services that you might use. Do not use any outdated screenshot or rates of another model.

Ask as to what is included in the estimate. The input, output, special options, storage, and other services related might be charged differently. Write down the source and date of the estimate. There may be changes in prices and availability of products. An estimate would have a clear set of assumptions that could easily be reviewed with an identified owner.

Count storage and search work

The application might use notes, drafts, history, documents, and even document collection processing before getting an answer from the model. This requires a different way of working and possible fees. Make sure that you identify the parts used instead of relying on the fact that the AI request is all there is to the feature.

Decide on useful rules for how long to keep data and who can access it. Storing all drafts indefinitely might increase costs and risks while being unnecessary for the job. Deleting all right away would complicate providing support. Select the policy depending on the business need and the rules that apply. Consider the work involved in updating documents and searching the archive, since stored records also need to be kept up to date.

Include the time people spend reviewing

The person may need to check all drafts separately. Check the effort by using samples. A clear response may require editing anyway; count the whole review process. If the system saves time spent on typing but requires more investigation, the end product may not be better than the beginning. Check the effort based on both the time and quality of the task done.

Also include support and testing. Someone must analyze problems, revise policies, and review changes. This is necessary for maintaining the usefulness of the feature. Budgets that do not consider these aspects make the initial release inexpensive but burden the operation team with tasks no one has prepared for or owned after the transition of the project.

Set useful limits and notices

Pick limits that fit the task and its demand. The application might impose limits on input size, multiple revisions, and unsupported requests. Describe the limits upfront to prevent the user from wasting time preparing an input that will not be processed by the feature. The team must know what will happen at a constraint and which supported alternative remains.

Cost visibility will help you analyze an unexpected increase. An increase in the number of requests can show either more users, an error repeating itself, or an unexpected loop. Assign owners and responses to alerts. A notice is not always a strict budget limit, so check how the selected service and application control works in reality, not by name.

Compare options using the same work

Compare models/designs using the same practical task. A lower request price does not necessarily imply that the cost of each completed task will be lower if the result requires more attempts to check. Compare the quality, waiting time, and the total cost. The company should be able to justify why the selected option suits their needs, not just choose the least expensive one.

Think whether a simpler technique would work on some portion of the task. A predefined template for reminders may suffice for confirming a date, while Gen AI will solve another kind of problem. This combination can coexist. The useful comparison involves full solutions for getting the task done, taking into account all the real needs for the support and test.

Update the budget after the pilot

Start with a small release and see how its use matches the expectations. Measure the volume of requests, length of inputs, revisions, reviews, and useful tasks done. Find out what causes the important differences. A pilot should help the team understand its daily work cost and value, not just prove the ability to find one good answer.

Make sure to keep the budget up-to-date whenever there is usage, changes to the model, source data changes, or differences in application's behavior. Give ownership to the person who can check both provider costs and the business process. Make use of facts to decide whether you should add, change, or even halt the feature. Budget must be up-to-date and should have clear assumptions, but not be a promise made forever based on the first demo.

How Digitonix can help

AI solutions provided by Digitonix, along with its cloud and DevOps services, can serve well as examples of discussion on the cost and operation needs of an AI feature. Make sure to bring the business task, safe sample inputs, possible demand, and process of reviewing it. Request the estimate to split the provider usage, linked services, testing, support, and people's involvement.

A project based on AWS and Python must be designed according to the real load and services to be used. Ask for small pilot, practical limits, and clear cost ownership through Digitonix contact. The idea is a feature whose value and workload the company can grasp, with justifications for decisions after the first release.

Frequently asked questions

Is one word the same as one token?

No, not necessarily. A token may consist of a word, part of a word, or other text part. This number is defined by the model and text being analyzed. Apply the provider's approach to calculate your app's use of tokens. You cannot take a word count or other model rule for granted as your billable count.

Does the cheapest model always give the lowest total cost?

No. Consider the entire process of attempting the task, reviewing it, waiting for it, other services required, and support involved. Even a low price could generate higher work load if the output produced is useless. Use representative samples and criteria for measuring quality. The business must have a reasonable decision-making approach based on the completed project, not just the prices of requests alone.

What costs can be missed in a first estimate?

Examples of missing parts are storage, search, multiple tries, testing, support, and human review. The exact list varies based on the application and service provider used. Analyze the request flow and operating process, and decide the services and people involved. Clearly state the assumptions so that the estimate can be verified and modified when necessary.

Will a budget alert stop all extra spending?

Assume not. Usage, notice, and actual control of the application could be different. Look at the existing settings for the selected service and also limits in the app. Give responsibility for the answer to someone. The people should know what would happen if the demand goes up and what supported task path would stay open for the users.

What should we bring to a cost discussion?

Bring in what is the allowed task, the safe input short and long, expected user behavior, needs on quality, and the review process. Include also the current application, linked services, data policy, and support owners. Understand what is included in the estimation and what assumptions could be different. This will make it easier to create a proper first budget and the pilot with good data.