How to Choose an AI API Relay Service: An 8-Point Checklist and How to Verify One Yourself

Published 2026-11-11

"AI relay" (中转站, zhongzhuan) is the term used in the Chinese-speaking developer community for a third-party service that sits between you and a model provider such as Anthropic, OpenAI or Google. You do not open an account with the provider; you buy credit from the relay and call the models with the address and key it gives you. "Claude relay" and "API relay" mean the same thing for a specific provider. There are many such services, they differ a lot in quality, pricing and compliance, and there is no universal "best one". This guide does not recommend a specific service. It gives you a checklist and checks you can run yourself without taking anyone's word for it.

Disclosure: we run OriginCoder, an AI API gateway that belongs to this category, so we have a stake in the topic. We use OriginCoder as one example below, but the checklist applies to every service equally, ours included: check us against it rather than trusting what we say. Information here is current as of November 2026; policies and prices change, so treat each provider's console and official documentation as the source of truth.

Sorting out the terms

The lines between these are blurry: one service can be both a reseller and a hosted gateway. The differences that matter are who is your counterparty, whose servers your requests pass through, and who holds the money and the logs. What a gateway is and when you need one is in What is an AI gateway; a side-by-side of the options is in the LLM gateway comparison and OpenRouter alternatives.

Why people use a relay

Common reasons: one key for several providers, pay-as-you-go instead of a subscription, payment methods that work for them, one bill and one usage record, and extra capacity when an official subscription has usage caps (see Claude Code usage limits). These are real needs, but each comes with a matching risk, and the checklist below is built around those risks.

The 8-point checklist

1. Is billing transparent, with per-request records?

• Are per-token prices published, with input, output and cache listed separately?
• Can you see the model, token counts and charge for each request in the console? That is your only basis for reconciling afterwards.
• Are there multipliers, minimum spends, expiry dates or credit-voiding rules? Read them before you top up.

2. Is the model real, or can it be swapped?

This is the biggest worry: you pay for a high-end model and get a cheaper one. You cannot prove "no substitution" from the outside, but you can run checks, described in "How to verify it yourself" below. Be wary of a service priced far below the official rate with no explanation of why.

3. Data and logging policy

• Are your prompts and responses recorded, for how long, who can see them, and are they used for training?
• Is there a written terms of service and privacy statement, and not just a verbal promise?
• Whatever the provider says, do not send secrets, customer data or unreleased business information to any third-party service until you have assessed the risk.

4. Stability and failover

• Is there a public status page or a history of availability?
• When an upstream errors or rate-limits, does the service return the error or fail over to another channel? After failover, is the model still the same?
• Where are concurrency and rate-limit rules written, and what is returned when you exceed them?

A figure like "99.9%" without a definition and a history is marketing.

5. Payment and invoices

• Which payment methods are supported, can you get an invoice, and from which legal entity?
• Are refund rules written down? Can unused prepaid balance be refunded?

Companies in particular should confirm this before a large top-up.

6. Support and compliance

• Who can you reach when something breaks, and is there a response-time commitment?
• Who operates the service, where is it registered, and which region's law do the terms follow? Is the service open to your region?
• Does it state its compliance boundaries, such as prohibited uses?

7. Protocol and format compatibility

• Which formats are supported: OpenAI Chat Completions, Anthropic Messages, Gemini native, or only one of them?
• Do streaming, tool calls, image input and long context work? Compatibility usually covers only the most common parts; see What is an OpenAI-compatible API.
• Can the coding tool you use (such as Claude Code) be configured against it directly?

8. Exit cost

• Does your code depend on only the Base URL and the key? If so, switching provider is a configuration change.
• Are you tied to a private SDK, private parameters or private model aliases?
• Can the balance be refunded and the usage history exported?

Keeping exit cost low is your fallback against every other risk: even if you choose wrongly, you can switch quickly.

How to verify it yourself

None of these requires believing the provider. They can reveal problems, but they cannot prove that everything is fine.

1. Start with a small top-up. Run a few days of real but light traffic before adding more. Do not prepay a large amount at once.
2. Reconcile. Compare the token counts in the console with those you compute using the official tokenizer or the usage field of an official response. A persistent, clear overcount is worth asking about.
3. Check the response format. Compare response fields, error bodies and the stream end marker against the official documentation. A large difference means a lot of conversion in between.
4. Look at the model name and usage in the response. The model field is filled in by the service, so it is a clue, not evidence.
5. Probe with capability differences. Some abilities exist only in particular models: very long context, image input, specific tool-calling behaviour, reasoning output. Give the relay's model and a source you trust (for example a small official call) the same fixed set of test questions and compare. The same model answers differently from run to run, so look at the overall pattern and not a single answer.
6. Compare latency and throughput. An unusual time to first token or speed can point to a different model or route behind the scenes.
7. Read the terms. Check that the terms and privacy statement spell out data handling, refunds and service changes.

Signals worth a second question

None of these means something is definitely wrong, but each deserves a question:

• A price far below the official rate with no explanation.
• Only verbal promises, no written terms.
• No per-request records, or records you cannot export.
• A demand for a large one-off prepayment with no refund rules.
• Absolute claims such as "never rate-limited", "100% stable", "cheapest" or "best".

Putting OriginCoder through the checklist

As an example, you can run the same checklist on OriginCoder and judge for yourself:

• Pricing: per-token prices are published on the model pricing page and can differ from the official ones, so compare before you decide.
• Per-request records: every call appears in the console usage records with token counts and cost.
• Data handling and compliance boundaries: see the Terms of Service; we are an independent platform and are not affiliated with any model provider.
• Formats: an OpenAI-compatible interface, plus Claude- and Gemini-related interfaces; the exact scope is whatever the console and documentation say.
• Payment, invoices, refunds, status and other policies: follow the current statements in the console and the terms, and we do not draw conclusions for them here.

For any service, ours included: start small, reconcile yourself, and keep your exit cost low. If you want to try it, create an account.