gRPC

Microservices

An architectural style structuring an application as a collection of loosely coupled, independently deployable services.

Monolithic Architecture

A software architecture where all components are tightly integrated into a single deployable unit.

Service Mesh

An infrastructure layer handling service-to-service communication in microservices, managing traffic, security, and observability.

API

Application Programming Interface — a set of protocols and tools for building and integrating software applications.

REST API

An API architectural style using HTTP methods and stateless communication for web service interaction.

GraphQL

A query language for APIs that lets clients request exactly the data they need in a single request.

Event-Driven Architecture

An architecture pattern where the flow of the system is determined by events — significant changes in state.

Message Queue

A communication method where messages are stored in a queue until the receiving application can process them.

Pub/Sub

A messaging pattern where publishers send messages to topics and subscribers receive messages from topics of interest.

Edge Computing

Processing data near the source of data generation rather than in a centralised cloud data centre.

Content Delivery Network

A distributed network of servers that delivers web content to users based on their geographic location.

An open-source container orchestration platform for automating deployment, scaling, and management of containerised applications.

Spare cloud computing capacity offered at steep discounts compared to on-demand pricing, available when the provider has excess resources but subject to interruption.

A strategy using services from multiple cloud providers to avoid vendor lock-in and optimise capabilities.

The automated arrangement, coordination, and management of complex cloud computing systems and services.

A database service that automatically provisions, scales, and manages infrastructure on demand without manual server management.

Cloud infrastructure providing on-demand access to graphics processing units optimised for AI training and inference, enabling organisations to scale compute without capital investment.

Automatically adjusting compute resources based on current demand to maintain performance and optimise costs.

The specialised hardware, software, and networking stack required to train and serve AI models at scale, including GPU clusters, high-bandwidth interconnects, and model serving frameworks.