Shift note: prototype trial on a small top-up

The first top-up should answer one question

A small top-up is useful when it buys a clear answer, not just a few interesting conversations. Before connecting an AI model to your prototype, write down the task you want to test. For example: can the application turn a short customer message into a useful draft reply that a person can review?

Keep the first experiment narrow. You do not need a complete product, a large document collection, or several automated agents. You need a working request, a representative set of inputs, and a way to decide whether the responses are useful.

A prepaid balance still needs a spending boundary

Pay-as-you-go access lets you pay for usage rather than committing to a recurring subscription for the experiment. Relay365 is a relay desk for multiple model families and can fit this kind of short, controlled trial. Check its current pricing, available models, and payment terms before adding funds.

Do not assume that a small balance is the same thing as a configured spending limit. Check what happens when credit runs low, whether automatic top-ups are enabled, and what budget controls are available. In your own application, limit the number of requests and stop the test when its planned allowance is reached.

Count the text going in and coming back

Many model APIs charge according to tokens, which are small pieces of text. Your instructions, the user's message, and any supplied documents contribute to input usage. The model's response contributes to output usage. Check the selected model's pricing because these categories may have different rates.

A short question can still become an expensive request if your application attaches a long conversation or a large document every time. For the first trial, send only the context needed for the task and set a reasonable output limit. Shorter answers are not automatically better, but unnecessary length spends money without necessarily improving the result.

Connect one client before building the workflow

Relay365 supports Claude Code, Codex, Gemini CLI, and OpenAI SDK clients. Its API base is https://api.api365relay.com; OpenAI clients use https://api.api365relay.com/v1. Follow the setup instructions for your chosen tool, and keep the API key in an environment variable or secret store rather than in committed source code.

Start with one request and inspect the response before adding a user interface or automation. Confirm the exact model identifier from the current catalog rather than guessing it from a display name. SDK compatibility does not necessarily mean every model supports every feature, so verify the options your prototype requires.

Run a small test set without changing everything

Choose one available model, such as Gemini 2.5 Flash, after checking its current price and capabilities. Save a small set of realistic inputs and run the same prompt against each. If the results fall short, change one thing at a time: the instructions, the supplied context, or the model.

• Include a straightforward input that represents the everyday task.
• Include an incomplete input to see whether the model asks for missing information.
• Include an awkward input that exposes a likely failure in your application.
• Record whether each response is usable, needs editing, or should be rejected.

If you later try Claude Haiku 4.5, reuse the same inputs. Compare actual usefulness and measured cost rather than assuming that a different model will solve the problem.

Leave the trial with a cost record and a decision

Record the model, request count, reported usage, and charges where available. Keep retries visible: an automatic retry is another request and may add cost. Avoid unlimited retry loops, and avoid recording sensitive user content unnecessarily.

When you are ready for a bounded trial, create a Relay365 account and choose a small top-up that fits your experiment after reviewing current terms. The useful outcome is not an empty balance. It is a decision: continue with the prototype, revise the task, or stop before spending more.