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What is an AI API? Definition and business implications
An AI API is a technical interface that lets a software application send requests to an AI model hosted by a provider, and retrieve its responses. It is the standard access mode to AI in enterprise, as opposed to local hosting of the model.
Concretely, an AI API operates by authenticated HTTPS call. The client application sends a structured message (prompt, parameters such as temperature, model choice) to a provider endpoint (api.anthropic.com, api.openai.com, etc.), authenticated by a secret key. The server runs the inference and returns the response, generally in streaming (token by token) for interactive uses. Three characteristics structure usage. The billing model: almost always pay-per-use, in tokens consumed at input and output, with no fixed subscription. Latency: 200 milliseconds to 2 seconds for the first response, plus the generation time. Resilience: the best-designed APIs offer an explicit SLA, with availability rates above 99.9% and a fallback endpoint in case of incident. The AI API has become the dominant mode of AI consumption in enterprise, ahead of local hosting, which remains reserved for cases with strong sovereignty or volume constraints.
Concrete example
A 120-employee industrial SME integrates an AI assistant in its ERP via the Anthropic API. Three metrics to monitor for production. Monthly inference cost (variable depending on the volume of business prompts, here about 280 euros per month for 2,000 daily calls). Average latency, measured at 1.1 seconds for first response plus 18 seconds for a typical 500-token generation. API error rate, tracked via the provider console: 0.4% of 5xx errors over three months of observation, compliant with the Anthropic SLA. Without these three metrics, the deployment is piloted blind.
FAQ
What is an AI API?
It is a technical interface that lets an application send requests to an AI model hosted by a provider and retrieve its responses. It is the standard way to access AI in the enterprise, as opposed to hosting the model locally, which is reserved for cases with strong sovereignty or volume constraints.
How is an AI API billed?
Almost always pay-per-use, based on the tokens consumed at input and output, with no fixed subscription. Technically, the application sends an HTTPS call authenticated by a secret key to a provider endpoint, which runs the inference and returns the response, often streamed token by token.
Which metrics should we track to run an AI API?
Three at a minimum: inference cost (ideally broken down by use case, not just a monthly total), the actual latency seen by users, and the API error rate. For an industrial SME integrating an assistant into its ERP, that meant about 280 euros per month for 2,000 daily calls, 1.1 seconds to first response and 0.4% errors over three months. Without these metrics, the deployment is flown blind.
Why is latency an adoption issue?
Because what matters is the latency actually experienced by the user, distinct from the raw latency the provider advertises. An application latency degrading to 4 seconds kills adoption, even if the model itself responds in 1 second. It must therefore be measured on the user side, not only on the API side.
Where is the security risk with AI APIs?
Mainly in how API keys are managed. A single key shared by all developers is a major security risk. The good practice is to isolate keys by user or by use, and to know at all times where they are stored and who can use them.
See also
Further reading
Sources
- Anthropic Messages API documentation, 2026. https://platform.claude.com/docs/en/api/messages
- OpenAI API reference, 2026. https://developers.openai.com/api/reference/overview