A student project can burn through its AI allowance before the demo is ready. Long chat histories, repeated debugging requests, and automated retries all consume tokens. Treat the token budget as part of your project design: decide what you can spend, measure a representative task, and put limits in your application before teammates start testing.
1. Set a spending cap for the whole project
Start with money, then translate it into tokens. A single token allowance can be misleading because input and output prices differ, and each model has its own rates. Write down a total project cap and a smaller development allowance.
For example, a hypothetical $20 budget could allocate $12 to development, $5 to the final demonstration, and $3 to unexpected fixes. These are planning amounts, not model prices. Keep the demonstration reserve out of everyday testing.
2. Choose one model for the first working feature
Pick one model that meets your initial requirements, then test it against a small set of realistic tasks. Gemini 2.5 Flash could be a candidate for a study assistant that summarizes notes and generates practice questions. Check current pricing and evaluate its answers before committing.
SwiftRelay is a practical option for this setup because it provides pay-as-you-go access to multiple model families with per-token billing. Sign up for SwiftRelay when you are ready to run your first measured test. Treat the small gift credit for new accounts as testing runway, not as the foundation of your project budget.
3. Connect through a backend you control
Keep the API key on your server, not in a browser bundle, mobile app, or shared repository. Your backend should authenticate users and apply spending rules before forwarding requests. This also gives your team one place to record usage.
For an OpenAI-compatible SDK, use https://api.fastapirelay.com/v1 as the base URL. Claude Code and Gemini CLI use the bare host, https://api.fastapirelay.com. Keep credentials in environment variables and exclude local secret files from version control.
4. Measure ten representative requests
Build a tiny test set before opening access to classmates. Include short notes, a longer document, an ambiguous question, and a request that should be rejected as outside the feature’s scope. Run ten representative requests and record the usage returned by the API.
- Input tokens: Include instructions, source material, and any conversation history sent with the request.
- Output tokens: Record generated tokens separately from input tokens.
- Request details: Save the model, feature name, timestamp, and whether the request was a retry.
Calculate estimated cost as input tokens multiplied by the input rate, plus output tokens multiplied by the output rate. Match the units in the price sheet; if rates are per million tokens, divide token counts by one million first.
5. Forecast usage with room for mistakes
Multiply the measured average request cost by expected users, sessions per user, and requests per session. Add an explicit allowance for retries and longer-than-average inputs. This forecast is an estimate, not a spending guarantee.
Compare it with your development allowance. If it does not fit, narrow the feature before adding more credit. Summarizing one selected section of lecture notes is easier to budget than repeatedly processing an entire course folder.
6. Enforce limits before each request
Add application-side limits for input length, output tokens, requests per user, and total project spending. Reserve a conservative estimated request cost before sending it, then reconcile that reservation with reported usage. Make reservations atomic so simultaneous requests cannot all spend the same remaining balance.
Limit retries and require a fresh budget check for each attempt. When the allowance is exhausted, return a clear message instead of silently continuing. Keep a local demonstration dataset available so the presentation does not depend on additional paid calls.
7. Review the ledger after each work session
Compare your application’s usage ledger with provider billing records. Investigate missing usage, repeated requests, and unexpectedly large prompts. Avoid logging raw student notes or personal information when token counts and request identifiers are enough.
Finish each session by recording the remaining development balance and the next test’s expected cost. Your green-light checklist is simple: protected credentials, measured requests, enforced limits, and an untouched demonstration reserve.