Shift Note: Building a Coding Agent’s Monthly Budget

The monthly bill starts with a task, not a prompt

A coding agent can inspect a repository, suggest changes, edit files, and run tests. That makes its cost harder to estimate than a chat session. One request to fix a bug may trigger several model calls before the agent finishes.

For a useful monthly estimate, count the work you expect to assign, estimate how many calls that work requires, and apply the relevant token prices. Then compare that estimate with a few days of actual usage.

The billable units behind each coding task

Most text-model API pricing separates input tokens from output tokens. Tokens are small pieces of text, not a fixed number of words. Input includes instructions, conversation history, repository content, and tool results sent to the model. Output includes the model’s generated response, such as code or explanations.

A short request can therefore create a large billable input. The words “fix this test” may be accompanied by source files, logs, and earlier attempts.

• Check the input and output rates for the exact model and route you plan to use.
• Check whether cached input has a separate rate and whether your requests qualify for it.
• Check whether reasoning tokens, tool services, or other features introduce additional charges.
• Keep subscriptions, execution environments, and other non-token expenses separate unless they are included in your service’s pricing.

A worked estimate for a small daily workload

Suppose you expect to use the agent on 20 working days each month, assigning 12 tasks per day. If each task takes five model calls on average, that is 1,200 calls per month.

Now suppose each call averages 20,000 input tokens and 2,000 output tokens. Monthly usage would be 24 million input tokens and 2.4 million output tokens. These averages must include the context sent on each call, not just newly typed text.

The basic calculation is monthly input tokens divided by one million, multiplied by the input rate, plus monthly output tokens divided by one million, multiplied by the output rate.

Using purely illustrative rates of $2 per million input tokens and $8 per million output tokens, the estimate becomes $48 for input plus $19.20 for output: $67.20 per month. These are arithmetic examples, not current prices for any model or provider.

You could reserve another 25% as planning headroom, producing an $84 budget. That buffer is a budgeting choice, not a prediction or a spending limit.

The usage patterns that move the estimate

Long debugging loops, repeated test failures, and large tool outputs can increase spending. Parallel agents can also multiply calls. A task that looks small to a person may require several attempts and repeated context processing.

Model choice matters, but compare cost per completed task rather than token prices alone. For example, you might evaluate Claude Haiku 4.5 and Gemini 2.5 Flash on the same representative tasks. Record their actual charges, completion quality, and retry counts rather than assuming the cheaper token rate produces the cheaper result.

A short usage log makes the budget dependable

Run a small pilot across ordinary fixes, unfamiliar code, and at least one difficult debugging task. Log the model, token usage, total charge, number of calls, and whether each task succeeded. Use billing records rather than estimating tokens from file sizes.

Multiply the observed average cost per task by your expected monthly task count. Keep a separate allowance for unusually difficult work. Set alerts or spending limits where supported, and check whether they actually stop requests or merely notify you.

A relay fits when several tools share the budget

Relay365 is a pay-as-you-go relay desk for multiple model families. It works with Claude Code, Codex, Gemini CLI, and OpenAI SDK clients, so it can fit a workflow that uses several coding tools. Check the applicable rates and billing details before applying your estimate.

If that matches your setup, create a Relay365 account and start with a small measured workload. Your first useful budget is not a perfect forecast; it is a clear calculation you can update as real usage arrives.