Core API, HTTP, REST, endpoints, and authentication foundations
An AI inference API is a cloud service that executes forward-pass neural network calculations on remote high-performance GPUs (like NVIDIA H100s or Google TPUs) to generate predictions or text from trained machine learning weights on demand.
API integration is the process of connecting two or more independent software systems through their APIs to automate workflows, synchronize data, and embed third-party capabilities into an application.
A JSON API is a web service that uses JSON (JavaScript Object Notation) as its primary data interchange format, sending and receiving structured key-value objects over HTTP.
An API key is a unique, secret cryptographic string passed in HTTP headers that identifies the calling application, verifies its permissions, enforces rate limits, and attributes usage billing.
An API endpoint is a specific digital location or URL where an API receives requests for a particular resource or service, typically consisting of a base URL, a path, and optional query parameters.
A REST API (Representational State Transfer) is an architectural style for web services that uses standard HTTP verbs (GET, POST, PUT, DELETE) and stateless communication to manipulate server resources represented in formats like JSON.
An API works through a deterministic client-server request-response lifecycle: the client dispatches a structured HTTP request over TLS/TCP, an API gateway validates permissions, the backend computes the task, and the server returns a structured response with status code and payload.
An API (Application Programming Interface) is a standardized set of protocols, routines, and definitions that allows two separate software applications to communicate and exchange data securely over a network without knowing each other's internal source code.
Mixture-of-Experts (MoE) is a neural architecture that replaces dense feed-forward layers with multiple specialized subnetworks ('experts'), using a routing gate to activate only a small fraction of parameters per token to achieve extreme inference speed.
A strategic comparison of the three primary methods for adapting foundation models: Prompt Engineering (fastest, lowest cost), RAG (best for factual retrieval and dynamic data), and Fine-Tuning (best for specialized formatting, tone, and latency reduction).
Retrieval-Augmented Generation (RAG) is an enterprise AI architectural pattern that retrieves relevant external documents from private vector databases and injects them into the model's prompt context, eliminating hallucinations and ensuring up-to-date factual grounding.
System prompts establish the overarching operating persona, safety constraints, and output guidelines for an AI model before any end-user input is processed, serving as the foundational instruction layer in chat completions APIs.
Temperature, Top-P (nucleus sampling), and Top-K are stochastic hyper-parameters that calibrate the probability distribution over next-token logits, allowing developers to balance deterministic precision (for code and JSON) against creative divergence.
Choosing the best AI API requires balancing four empirical dimensions: task reasoning complexity, Time to First Token (TTFT), token pricing per million, and operational reliability through automated multi-provider failover.
A context window is the maximum number of tokens (words and punctuation) that a large language model can ingest and consider simultaneously in a single prompt and completion conversation cycle.
An LLM API is a specialized web API that exposes text, vision, and multimodal inference endpoints for foundation models, accepting conversational message arrays and returning generated completions or token streams over HTTP.
A Large Language Model (LLM) is a deep neural network trained on vast text corpora using transformer self-attention mechanisms to understand, generate, reason with, and translate natural language.
An AI API (Artificial Intelligence Application Programming Interface) is a standardized cloud-based interface that enables software applications to send text, code, images, or audio to remote machine learning models and receive generated responses without hosting GPU infrastructure locally.
Access 100+ AI models through one lightning-fast OpenAI-compatible API with sub-50ms routing overhead and zero markup on cached tokens.
