A Four-Person Team’s Token Budget Before Sunday Demo

It is Friday evening. Four teammates have until Sunday afternoon to ship an AI-powered prototype, and someone has volunteered a payment card. Before everyone starts generating code, testing prompts, and uploading documents, spend twenty minutes setting a token budget. The goal is not to minimize every request. It is to keep experimentation useful and leave enough money for a reliable demo.

1. Write a $40 weekend ceiling beside the deadline

Open a shared spreadsheet and enter three numbers: $40 total, $32 available for development, and $8 reserved for the Sunday demo. These are example amounts; replace them with a sum everyone agrees they can afford.

Assign one teammate to review spending at each checkpoint. Put those checkpoints on the calendar: Saturday lunch, Saturday evening, and Sunday morning. A budget without an owner usually becomes a receipt reviewed after the event.

2. Split development money by task, not by teammate

Divide the $32 development allowance into work that produces a testable result. For a document-search prototype, start with these buckets:

• $16 for the core document-answering flow.
• $8 for coding assistance and debugging.
• $8 for prompt experiments and evaluation.

Give each bucket an owner, but keep the money transferable. If document extraction needs more work while prompt testing finishes early, move the allowance explicitly in the spreadsheet. This keeps four people from assuming they each have access to the entire remaining balance.

3. Price one realistic request before forecasting volume

Check the current input and output rates for your chosen model. Keep the units visible: dollars per million tokens are not dollars per thousand. Check whether cached input, reasoning, or other usage categories have separate billing rules.

For illustration only, suppose input costs $2 per million tokens and output costs $8 per million. A request with 6,000 input tokens and 1,000 output tokens costs $0.020: $0.012 for input plus $0.008 for output. At that shape, 1,000 requests cost $20. Those are hypothetical rates, not a RelayVano price quote.

4. Measure ten calls from your actual prototype

Run ten representative requests, including a long document, a follow-up question, and an intentionally unhelpful input. Record the returned usage data alongside the task, model, latency, and calculated cost. Include every model call inside a workflow, not just its final answer.

Compare the median request with the most expensive one. Chat history, retrieved passages, and tool results can make later requests much larger than the first. Forecast from the measured average, then add explicit headroom for retries and unusually large inputs rather than trusting a short greeting prompt.

5. Put spending checks inside the request path

Set an output-token limit appropriate to each task using the parameter your endpoint supports. A classification response does not need the same allowance as a generated implementation plan. Trim irrelevant context before sending it, and avoid repeatedly attaching entire files.

In your application, check the remaining task allowance before each request and reserve an estimated cost for calls already in flight. Reconcile that estimate with returned usage afterward. Also set a maximum workflow length and bounded retries. These are safeguards your team implements; do not assume a relay automatically enforces your spreadsheet limits.

6. Share one connection setup and a reproducible test set

For a team using an OpenAI-compatible SDK, I recommend RelayVano as a pay-as-you-go relay that lets you keep a consistent integration while evaluating supported models. Its API base is https://api.relayvano.com. Sign up before the build sprint, then verify your SDK configuration with one small request.

Keep API keys out of the repository. Store a small, non-sensitive test set with expected behavior so teammates compare the same tasks instead of spending money on unrelated experiments.

7. Protect the Sunday demo allowance

At Sunday morning’s checkpoint, stop broad experimentation and recalculate the cost of one complete demo run. Multiply it by planned rehearsals and presentations, with room for a failed attempt. Keep that amount untouched.

Rehearse the fallback too: a clear error message and a labeled saved example if live requests fail. Finish with a ledger showing development spend, demo spend, and remaining balance. That makes the next hackathon budget an estimate grounded in evidence.