Picking an API for a small product

Advice about choosing an AI model usually assumes a company with an evaluation team, an enterprise contract and a budget line. A small product — a side project, a feature in an indie app, an internal tool — has different constraints: one person's time, a new account with low limits, costs that must stay predictable, and no appetite for a migration project next year.

The right choice is usually not the most capable model. It is the one that is good enough, affordable at your volume and easy to leave.

Start from the task, not the model

Write down, in a sentence each:

• What the model must do — classify support emails, extract fields from receipts, draft product descriptions, answer questions over your documentation.
• How good is good enough — "correct fields on 95% of receipts" is testable; "high quality" is not.
• How fast it must respond — interactive features need a response in a second or two; background jobs can wait minutes.
• How much volume — requests per day now, and a guess for a year from now.

Many small-product tasks — classification, extraction, short rewriting — are handled well by smaller, cheaper models. The largest models earn their price on complex reasoning, long documents and difficult code. Paying for capability you do not use is the most common mistake.

The criteria that matter for a small product

1. Cost at your volume. Work out the cost of a typical request — input tokens plus output tokens at the model's rates — and multiply by your daily volume. Include any long system prompt, which you pay for on every call unless it is cached.

2. Quality on your examples. Twenty to fifty real inputs with known good answers. Run each candidate once; score them. This takes an afternoon and outranks every benchmark.

3. Limits on a new account. Rate limits usually start low and rise with spending. Check the limits for the exact model at your starting tier — a model you cannot call often enough is not an option, however good it is.

4. Data handling. What the provider does with your API inputs: whether they are used for training (major providers generally say no by default for API traffic, but read the current terms), how long they are retained, and where they are processed. If your users' data passes through, you need to be able to explain this in your privacy policy.

5. Structured output. If you need JSON, check whether the API can enforce a schema. It removes a whole class of parsing errors.

6. Stability. How often the provider retires models, and how much notice it gives. A small team does not want forced migrations every few months.

7. Tooling. An official SDK for your language, clear documentation, and usage dashboards that show cost per day.

Keep an exit

Whatever you choose, make leaving cheap:

• Put the model call behind one function in your code. The rest of the product should not know which provider it uses.
• Keep your prompts in files, not scattered through code.
• Keep your test set. When a cheaper or better model appears — and one will — you can evaluate it in an hour.
• Consider a fallback from a second provider for when the first is down or rate-limited.

Keep costs predictable

• Set a spending limit or budget alert in the provider's console on day one.
• Cap output length for each request type.
• Cache repeated work — identical requests, and long stable prompt prefixes where the provider offers prompt caching.
• Use batch processing for anything that can wait; several providers discount it.
• Limit per-user usage in your own product, so one user or script cannot run up your bill.

Hosted or self-hosted

For most small products, a hosted API is the right starting point: no hardware, no operations, pay for what you use. Self-hosting an open-weights model makes sense when data cannot leave your infrastructure, when volume is high and steady enough that fixed hardware costs less, or when you need to run offline. It is rarely the right first step.

A one-evening process

1. Write the task, quality bar, latency and volume.
2. Shortlist three models: one small and cheap, one mid-range, one top-tier for comparison.
3. Run your test set through each.
4. Price each at your volume.
5. Check limits at your tier and the data terms.
6. Choose the cheapest one that meets the bar, behind a single function.

Where AIonRadar helps

AIonRadar's LLM API Selector matches model routes to a described workload, which is a faster way to reach a shortlist than reading every provider's documentation. The API cost calculator prices each candidate at your own token volumes, and source-backed comparisons put models side by side with links to where each number came from.

Its news section follows model and API releases — new models, price changes, retirements — with links to the original announcements, so you notice when a better or cheaper option appears.

It is free on the web, and the iPhone and iPad app has no account, no advertising and no in-app purchases.