Building GenAI features can be quick by using an LLM API key to create chatbots and MVPs. After deployment, operational issues appear, including runaway costs from rogue logic and insecure practices like hardcoded API keys. Better architecture is needed beyond discipline. An AI Gateway acts as a control plane between internal developers and external model providers, enabling security controls, traffic normalization, governance guardrails, and semantic caching to improve performance and reduce cost. Traditional rate limiting by requests per minute fails because LLM requests vary widely in token usage and cost. Token-based rate limiting using a Token Bucket algorithm provides more accurate cost control by limiting tokens rather than HTTP requests.
"However, LLMs introduce massive variance. A single API request could be a two-token "Hello World" (cost: $0.0001) or a context-heavy summarization of a 50-page PDF (cost: $2.00). If you rely on RPM, a developer can technically stay within their rate limit while blowing through the monthly budget in an hour. RPM is a useless metric for GenAI cost control. The architectural fix is Token-Based Rate Limiting. Instead of counting HTTP requests, the AI Gateway utilizes a Token Bucket algorithm"
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