Most engineering teams already know how to manage APIs. Authentication, authorization, rate limiting, logging — these are solved problems with well-established tooling. When those teams start deploying LLM APIs, the instinct is to apply the same practices. That instinct is mostly right, but it misses a few things that are specific to AI and genuinely matter.
The first is prompt injection. When an AI system connects to external data sources — documents, databases, emails, web content — that content becomes part of the model's input. Malicious content in a data source can include instructions designed to hijack the model's behavior: "ignore your previous instructions and do this instead." This is a different kind of attack surface than conventional API security, and it requires controls that most API management tooling wasn't designed to address.
The second is cost unpredictability. Standard APIs have predictable pricing: a fixed cost per call, or per data transfer. LLM APIs are priced by token, and token consumption varies dramatically with input length, output length, and the nature of the task. A single poorly designed prompt at scale can generate costs that are orders of magnitude higher than expected. Rate limiting and API rate limiting help, but they need to be configured with token budgets in mind, not just request counts. LLM routing and LLM load balancing add another layer, directing traffic to the right model for the right task and distributing load to avoid bottlenecks and runaway costs.
The third is the audit trail. When an AI system makes a consequential decision — approving a loan, flagging a transaction, generating a customer-facing response — there are often regulatory or operational requirements to explain what happened and why. API logging for LLMs needs to capture not just that a call was made, but what was in the prompt, what the model returned, and what downstream action followed. Standard logging practices capture the first part. The rest requires deliberate design.
Authentication and authorization apply here as they do everywhere: controlling who can call which models, with what permissions, under what conditions. The difference is that in an LLM context, authorization often needs to extend to what data sources the model can access and what actions it can trigger, not just whether the API call itself is permitted.
The articles in this section cover each of these layers. Taken together, they describe what it actually takes to deploy AI APIs in production with the controls that real organizations require.
