API
A standard interface for exchanging data and functionality between software applications. In AI, it serves as a core tool to connect cloud-hosted Large Language Models (LLMs) to external apps, enabling immediate implementation of features like text generation and image analysis.
Detailed explanation
Choosing an AI API directly impacts service quality and profitability. Alongside model performance (IQ), factors like 'Latency,' 'Token Pricing,' and 'Rate Limits' are critical. In particular, a compliance review is essential to ensure that corporate data is not reused for model training.
Why It Matters in Tool Selection
What to Check
- Cost per input/output token and whether caching discounts apply
- Is the Time to First Token (TTFT) suitable for the user experience?
- Whether there is a security policy (Zero Data Retention) that does not use API call data for model training
- Does it support Batch APIs for bulk processing to reduce costs?
Example
When a user enters a question into a chatbot UI, the application internally uses an API key to send data to OpenAI's 'gpt-4o' endpoint. The server generates a response, returns it in JSON format, and the application parses this data to display it on the screen.
Commonly Confused Terms
API vs SDK
While an API is a protocol for communication, an SDK is a development kit that bundles libraries and tools to make using that API easier.
REST vs Streaming
REST delivers a single response to a single request, whereas streaming delivers responses in real-time chunks as they are generated.