Recently, more and more people are asking me the same question: Can cheap AI intermediaries really be used? My answer is, this question isn't deep enough.

On the surface, intermediaries are indeed cheap. The official GPT-5.5 input price is $5 per million tokens, output $30; Claude Sonnet 4.7 costs $5 for input, $25 for output. But intermediaries can cut costs to about 15% of the official prices, buying tokens at 1 RMB per dollar. For users handling long texts, code generation, and automated workflows, this is not a small amount.

But I’ve noticed many overlook a core issue: you're not just paying money, you're also giving away data. Prompts, code, business documents, customer information, call logs, even the entire project development context—these may all go into a third-party system you don’t fully trust through API calls.

I suggest asking yourself an honest question first: Do I really need an intermediary? If it’s just occasional translation, summarization, or writing some copy, the free quotas of ChatGPT and Gemini are enough. Instead of handing over data to unknown platforms just for “cheapness,” it’s better to exhaust the official free quotas first. This is my most direct recommendation for light users.

Heavy developers don’t need to rush into using intermediaries for everything. A more robust approach is layered usage: powerful models handle requirement decomposition and architecture design, while domestic affordable models complete specific development tasks. Take Kimi K2.6 as an example, with an output price of only $4 per million tokens, about 13% of ChatGPT’s price, even lower than many intermediaries. Complex tasks mainly need direction judgment; specific implementations can be broken into multiple low-risk, small tasks.

Only when you have ongoing, high-frequency, multi-model calling needs, and official quotas are clearly insufficient, do intermediaries become a real option. Even then, it should be a “filtered tool,” not a default entry point.

If you ultimately decide to use them, the next question is: how to use them safely? I’ve organized a process:

First, verify before depositing funds. Call the same prompt through both the intermediary and official API, compare output quality and token consumption for consistency. Conduct 20-50 consecutive calls to test latency and stability. Check if platform documentation is complete and if model lists are clear. A serious platform will provide standard interfaces compatible with OpenAI formats and clear pricing.

Second, isolate configurations—don’t mix platforms. Generate separate API keys for each intermediary, don’t share keys across platforms. Manage keys via environment variables, don’t hard-code them into your code. Most importantly, set usage limits—this controls costs and provides a safety net.

Third, develop data classification habits. Before sending, ask yourself: if this content appears on a public forum tomorrow, can I accept it? Summaries of public data and open-source project discussions can be directly used. Internal meeting notes and business documents should be anonymized first: change names to roles, amounts to ratios, IDs to placeholders. Private keys, production environment keys, and unreleased financial data must never be handed to any intermediary.

Fourth, treat AI programming tools separately. When integrating intermediaries into Cursor or Claude Code, models not only see your prompts but can also access open files, project structures, terminal outputs, dependency settings, and Git logs. A seemingly simple “help me fix this bug” request might send far more data than expected. My advice is to only paste anonymized code snippets or switch back to official API for sensitive projects.

Fifth, monitor continuously and be ready to exit at any time. Regularly check billing records against usage. Follow platform announcements and community feedback—intermediary services’ operational status can change at any moment. It’s recommended to register 2-3 platforms simultaneously, keep minimum deposits, and avoid single points of dependency. When configuring, use OpenAI-compatible formats so switching platforms only requires changing the base URL and API key.

Ultimately, intermediaries are just tools. Their value lies in solving real access needs at controllable costs. But “controllable” is a term that’s in your hands. Through verification, isolation, tiered use, and monitoring, you retain control. Many people see intermediaries in annual reports or recommendations and jump right in—this is the easiest way to fall into traps. Just like before translating confidential documents, you need to verify the background of the translation agency—AI intermediaries follow the same principle.