How AI APIs Work
Building a large language model from scratch requires hundreds of millions of dollars, a team of specialized researchers, and months of compute time on hardware most organizations will never own. The existence of AI APIs means none of that is necessary to build something useful with AI. You write code that sends a request to an endpoint, a response comes back, and your application does something with it. The model itself lives somewhere else entirely, maintained by someone else, running on hardware you'll never see.
That simplicity is genuinely valuable, and it comes with tradeoffs worth understanding before you build anything serious on top of it.
An API, application programming interface, is a standardized way for software systems to communicate with each other. AI APIs specifically expose the capabilities of a trained model, typically a large language model, through a set of structured requests and responses. You send text in, you get text back. The request includes your input, any instructions for how the model should behave, and parameters that control things like output length and randomness. The response includes the model's output and metadata about the request, including how many tokens were processed, which determines what you're billed.
The token-based pricing model is worth understanding in detail because it shapes how AI API costs scale with usage. Tokens are the units of text that language models work with, roughly corresponding to word fragments, and most AI APIs charge separately for input tokens, the text you send, and output tokens, the text the model generates. A system that sends long context to the model with every request, or that generates lengthy responses, accumulates costs faster than one that's been designed with token efficiency in mind. At low volumes this doesn't matter much. At scale, token economics become a meaningful engineering constraint.
The practical workflow for using an AI API involves an API key, which authenticates your requests and ties usage to your billing account, and a call to the model endpoint with your prompt structured according to the API's conventions. Most major AI APIs organize prompts into roles: a system message that sets the model's behavior and context, user messages representing the human side of a conversation, and assistant messages representing prior model responses. This structure allows you to maintain conversational context by including the history of an exchange in each new request, since the model itself is stateless and has no memory between calls.
Statelessness is one of the more important properties of AI APIs to internalize. The model doesn't remember your previous conversation. Every request is processed independently, with no persistent state between calls. Applications that need conversational memory achieve it by including the relevant conversation history in each request's context, which costs tokens. Applications that need to remember things about a user across sessions store that information in their own database and inject it into the prompt when relevant. The memory, in other words, is your responsibility to manage, not the API's.
What happens to your data when you send it to an AI API varies significantly by provider and by the tier of service you're using. Consumer-facing API tiers often include provisions allowing the provider to use inputs for model improvement, which can create compliance problems for organizations sending sensitive data. Enterprise tiers typically offer stronger data isolation guarantees, with contractual commitments that inputs won't be used for training and that data won't persist beyond the request. Reading the data handling terms of any AI API your organization uses isn't optional if you're processing customer data, proprietary information, or anything with regulatory implications. The defaults are not always what organizations assume they are.
Reliability and rate limits are operational realities of API dependence that matter more in production than in development. AI APIs impose limits on how many requests you can make per minute or per day, which can become a bottleneck for high-volume applications. They experience outages and degraded performance, like any cloud service, which means applications built on them need error handling, retry logic, and potentially fallback behavior for when the API is unavailable. Building a production system on an AI API without accounting for these failure modes is building on an assumption of perfect availability that won't hold.
The dependency question is the one that deserves the most strategic thought. An application built on a single AI API is dependent on that provider's pricing decisions, capability changes, and continued operation. Models get updated, sometimes in ways that change output behavior in ways that break downstream applications. Prices change. APIs get deprecated. Organizations that have built significant functionality on top of a specific model and API have limited leverage when any of these things happen. Architectural choices that reduce that dependency, abstracting the AI layer so it can be swapped out, evaluating multiple providers, or running open weights models for some use cases, are worth considering before the dependency becomes a constraint.