An occasional AI task should start with a clear spending decision, not an open-ended experiment. Whether you need help reviewing a script, drafting release notes, or untangling an unfamiliar function, the goal is the same: fund useful work, keep requests bounded, and stop when the job is done.
SwiftRelay fits this workflow with per-token billing and access to multiple model families through one relay. This first-run checklist takes you from a small starting balance to a repeatable routine for intermittent use.
1. Define one task before funding your first run
Write down a concrete deliverable before opening your coding assistant. “Explain this function and suggest one fix” is easier to budget than “Improve this project.” A bounded task limits the material you send and gives you a clear stopping point.
- Input: One relevant file or a short excerpt.
- Output: A brief explanation, a proposed patch, or a checklist.
- Finish line: A result you can inspect without another broad research round.
Keep unrelated files and old conversation history out of the first request. Extra context can be useful, but it should earn its place.
2. Use the gift credit for a controlled trial
Sign up for SwiftRelay and use the small gift credit provided to new accounts to test your setup. Treat it as a trial budget, not a reason to submit your largest workload immediately.
Check current model pricing before making requests. Token billing means spending depends on the model and the tokens processed and generated; a request count alone is not a useful budget. Choose a modest top-up only after you understand what your representative task consumes.
3. Point your existing tool at the correct host
SwiftRelay works with Claude Code, Codex, Gemini CLI, and OpenAI-compatible SDKs. Keeping your familiar tool can make the trial easier to evaluate: you are changing the connection rather than rebuilding your workflow.
- Claude Code and Gemini CLI: Use the bare host https://api.fastapirelay.com.
- OpenAI-compatible SDKs: Use https://api.fastapirelay.com/v1 as the API base.
Add your SwiftRelay credentials through your tool’s supported configuration method. Keep credentials out of source files and shared transcripts. Send a short test request before giving an assistant access to a larger working context.
4. Choose a model for the bounded task
Start with one model rather than sampling several on every request. For a short explanation or draft, Claude Haiku 4.5 is one option to evaluate. If the task needs deeper analysis, you might test Claude Sonnet 5.5 on the same narrowly defined problem.
Use current pricing and your own acceptance criteria to make the decision. A cheaper request is not automatically cheaper work if you repeatedly ask for corrections. Conversely, paying for more capability does not help when a simple response already satisfies the task.
5. Put a ceiling on each working session
Decide how much you are willing to spend on this task before continuing beyond the trial. Where your client supports it, set an output-token limit and require approval before an agent continues into additional work.
Ask for concise output explicitly: “List the three relevant issues and show only the changed lines.” Review available usage information after the first meaningful response. If you cannot see a confirmed charge yet, do not treat a rough token estimate as an exact bill.
6. Record the result before your next top-up
Keep a simple note with the task, model, observed spending, and whether the result was usable. After a few occasional sessions, those notes become a more practical funding guide than guessing from a single prompt.
At the next pit stop, top up against a specific upcoming task rather than an imagined workload. Finish the deliverable, save the useful output, and end the session. The repeatable habit is straightforward: small scope, deliberate model choice, checked spending, and a clear finish line.