Bottom line: when choosing a Claude / GPT API gateway, don’t focus only on “which one is cheaper.” You should pay more attention to compatibility, reliability, billing rules, privacy, and customer support. For personal testing, prioritize providers that are directly compatible with the OpenAI SDK. For team or production environments, also confirm how balances and project quotas work, whether failed requests are billed, and whether your data will be stored long term.
1. First, check whether you can avoid migration entirely
Many projects already use the OpenAI SDK. When teams later want to connect to Claude, the biggest concern is having to rewrite an entire codebase just to switch providers. The easiest option is to choose a gateway that supports an OpenAI-compatible API, so you only need to change the base_url and API key, then replace the model name according to the documentation.
In this regard, 4All API is relatively developer-friendly, with fairly complete documentation and examples. It is suitable for local development, script-based calls, and quick integrations. Keep in mind that “OpenAI-compatible” does not mean every parameter works exactly the same way. It’s best to test tool calling, visual inputs, streaming output, and other features with your own request payloads first.
2. A lower price does not necessarily mean a lower overall cost
The common pitfalls with API gateways are usually not the unit prices, but unclear billing practices. You should clarify in advance whether input and output tokens are billed separately, whether failed requests incur charges, how overly long contexts are handled, and whether prepaid balances expire.
Our team generally prefers platforms that charge by usage or per request and do not charge for failed requests. For example, 4ALL API supports a relatively broad range of models. A single key can be used to manage multiple models, and tokens and quotas can be allocated by project. For personal testing, you can also start with free models instead of making a large deposit right away.
3. For production environments, focus on reliability and data policies
If you’re only chatting, the occasional slowdown may be tolerable. But when these services are used for customer support, workflows, or batch generation, timeouts, rate limits, and temporary model unavailability can result in real losses. We recommend conducting a small-scale load test first to evaluate response times during peak periods, retry mechanisms, and error messages.
If your focus is image and video generation rather than just Claude / GPT text models, you can take a look at OmniAPI, which places greater emphasis on image and video generation capabilities. Overseas platforms such as OpenRouter offer a wide range of choices, but may involve international payment, network, and account-access barriers, so they are not necessarily suitable for every team.
A practical recommendation
For individual developers: start with a provider that supports OpenAI compatibility, has clear documentation, and offers a free trial. For team projects: confirm invoice availability, quota allocation, policies for failed requests, and data privacy practices. For production environments: don’t rely on just one provider—prepare at least one backup endpoint. If you’re looking for new products with transparent token-based billing, you can keep an eye on TokenNode, which is coming soon. In most cases, starting with a small amount, validating the service, and then migrating gradually is far more reliable than choosing based on claims of having the “lowest prices anywhere.”