AI intermediaries are gaining traction as they offer cost-effective access to multiple AI models through a unified interface. Users benefit from lower costs, with intermediaries providing API access at significantly reduced rates compared to official channels. This is particularly appealing for high-volume users who face high costs with official APIs, such as OpenAI's GPT-5.5 and Anthropic's Claude Sonnet 4.7. Additionally, intermediaries simplify access to various models, overcoming regional restrictions and integrating seamlessly with development tools. However, the use of intermediaries raises security concerns. Users may inadvertently expose sensitive data, such as business documents and customer information, to third-party services. It is crucial for users to assess their actual need for intermediaries, especially if their usage is infrequent or can be managed with free tiers of official tools. For those who require intermediaries, implementing security measures such as data classification, technical isolation, and continuous monitoring is essential to mitigate risks. Ultimately, while intermediaries offer cost savings, users must carefully manage their use to protect sensitive information.