API
An API (Application Programming Interface) is a set of protocols and tools that allows different software systems to communicate. AI APIs enable developers to integrate machine learning capabilities like text generation, image recognition, and speech processing into applications.
Understanding API
APIs serve as the critical connective tissue between AI capabilities and the applications that deliver them to end users, enabling developers to integrate powerful machine learning features without building models from scratch. Major AI providers like OpenAI, Google, and Anthropic offer APIs that provide access to large language models for text generation, embeddings for semantic search, and multimodal capabilities for image understanding. A developer building a chatbot can call an API endpoint with a user message and receive a generated response in milliseconds, abstracting away the complexity of the underlying neural network. RESTful and GraphQL APIs have become standard interfaces, while streaming APIs enable real-time token-by-token output for conversational AI applications. The API economy has democratized access to AI, allowing startups and enterprises alike to leverage state-of-the-art models like ChatGPT and BERT without the infrastructure costs of training and hosting models themselves.
Category
AI Infrastructure
Is AI recommending your brand?
Find out if ChatGPT, Perplexity, and Gemini mention you when people search your industry.
Check your brand — $9Related AI Infrastructure Terms
AI Chip
An AI chip is a specialized processor designed specifically for artificial intelligence workloads like neural network training and inference. Examples include NVIDIA's GPUs, Google's TPUs, and custom ASICs.
CUDA
CUDA (Compute Unified Device Architecture) is NVIDIA's parallel computing platform that allows developers to use GPUs for general-purpose processing. CUDA is the foundation of GPU-accelerated deep learning training.
Data Lake
A data lake is a centralized storage repository that holds vast amounts of raw data in its native format. AI systems often draw training data from data lakes that store structured, semi-structured, and unstructured information.
Data Pipeline
A data pipeline is an automated series of data processing steps that moves and transforms data from source systems to a destination. ML data pipelines handle ingestion, cleaning, feature engineering, and model training workflows.
Data Warehouse
A data warehouse is a centralized repository for structured, processed data optimized for analysis and reporting. AI and ML systems often source their training data from enterprise data warehouses.
Distributed Training
Distributed training is the practice of splitting model training across multiple GPUs or machines to handle large models and datasets. It uses data parallelism or model parallelism to accelerate training.
Edge AI
Edge AI refers to running artificial intelligence algorithms locally on hardware devices rather than in the cloud. Edge AI enables real-time inference with lower latency, better privacy, and reduced bandwidth requirements.
Feature Store
A feature store is a centralized repository for storing, managing, and serving machine learning features. It enables feature reuse, consistency between training and serving, and collaboration across ML teams.