An AI Budget for Two Developers Shipping in June

Two developers, one product, and a launch planned for June: what should the team put in its monthly AI budget? Start with two separate buckets. The first covers AI used to build the product. The second covers AI requests made by the product itself. Keeping those buckets separate makes the estimate easier to check when customers arrive.

You do not need a precise forecast on day one. You need a reasonable starting allowance, a way to measure actual usage, and a spending limit that protects your runway.

The three charges on your AI budget

A small startup may pay for tool subscriptions, usage-based API requests, or both. A coding assistant subscription might include an allowance, while a tool configured with your own API credentials may generate separate usage charges. Check which arrangement each developer uses before adding up costs.

• Subscriptions: recurring charges for the tools each person keeps.
• Development API usage: requests for coding, debugging, research, and testing.
• Product API usage: requests triggered by customers or background jobs.

A subscription allowance does not automatically cover requests sent through a separate API account. Record each charge under the account that actually pays it, so you avoid counting the same work twice.

Tokens turn everyday work into a bill

Most text APIs charge by tokens, which are small pieces of text. Input tokens include your instructions, attached files, and conversation history sent with a request. Output tokens are the model’s response. Input and output commonly have different prices, usually quoted per million tokens.

The basic calculation is input tokens divided by one million, multiplied by the input rate, plus output tokens divided by one million, multiplied by the output rate. Calculate each model separately if you use several.

A short instruction can still produce a large bill if the tool sends a long codebase or conversation alongside it. One coding task can also trigger several model requests. Estimate requests from usage records where possible, rather than counting messages you type.

A worked month for two developers

Suppose both developers work 22 days and each generates 40 model requests per day. That produces 1,760 requests. Assume each request averages 12,000 input tokens and 2,000 output tokens.

For this example only, use an input price of $2 per million tokens and an output price of $10 per million tokens. These are illustrative rates, not a quote for any provider or model.

• Development input: 21.12 million tokens, costing $42.24.
• Development output: 3.52 million tokens, costing $35.20.
• Total development API estimate: $77.44.

Now suppose the product handles 3,000 customer requests during the month. Each uses 1,500 input tokens and produces 300 output tokens. At the same illustrative rates, input costs $9 and output costs $9, adding $18.

The combined API estimate is $95.44. Add a 30% planning buffer for debugging loops, retries, and heavier days: approximately $124. Round that to a $125 API allowance. If you also retain two $20 monthly subscriptions, the planned total becomes $165, before any applicable taxes or payment fees.

The assumptions most likely to change

Request volume, context length, and model choice deserve the most attention. Doubling the files attached to every request can increase spending even when the team works the same hours. A feature that retries failed responses can also make more requests than its customer count suggests.

Use a more capable model, such as Claude Sonnet 5.5, where testing shows it improves results enough to justify its cost. For routine tasks, test a less expensive option against the same examples. Compare the cost of completing a task successfully, including retries.

A weekly spending check before launch

After the first week, replace your assumed token averages with actual usage. Keep development and customer traffic distinguishable through separate keys or usage labels where supported. Set billing alerts, and confirm whether any configured budget actually stops requests when reached.

RelayVano is a practical pay-as-you-go option for a small team using Claude Code, Codex, Gemini CLI, or an OpenAI-compatible SDK, with the API base at https://api.relayvano.com. Sign up to check current model rates and run a small, capped trial before setting your monthly allowance.

Review spending weekly through launch. Update the forecast when traffic or workflows change, and keep the buffer visible as its own line in the budget.