TECHNICAL GUIDE
Context before configuration
Last verified August 27, 2026 by the DevTools Stack Review Editorial Team
Developer tooling evolves quickly, and vendors often use the same technical terms to describe very different architectures. This glossary explains common cloud, DevOps, observability, security, data and AI terms in practical language for engineers and technical buyers.
Cloud and Infrastructure
Cloud Computing
On-demand access to computing resources such as servers, storage, databases and networking through a provider rather than operating all infrastructure directly.
Virtual Machine (VM)
A software-defined computer that runs its own operating system on shared physical hardware.
Container
A lightweight package containing an application and its dependencies while sharing the host operating system kernel.
Kubernetes
An open-source system for orchestrating containers across clusters, including scheduling, scaling, service discovery and recovery.
Serverless
A cloud execution model where infrastructure provisioning and scaling are largely managed by the provider. The term does not mean servers do not exist.
Infrastructure as Code (IaC)
Managing infrastructure configuration through version-controlled code rather than manual console changes.
DevOps and Delivery
Continuous Integration (CI)
Automatically building and testing code changes as they are merged or submitted.
Continuous Delivery / Deployment (CD)
Automating the process of preparing or releasing changes to production. “Continuous delivery” may still require a manual release decision, while continuous deployment typically releases automatically after checks pass.
Pipeline
An automated sequence of build, test, security, packaging or deployment steps.
Rollback
Reverting a system to a previous known-good version after a failed change.
Feature Flag
A control that turns product functionality on or off without requiring a new deployment.
Observability and Reliability
Observability
The ability to understand system behavior through signals such as logs, metrics and traces.
Logging
Recording discrete events generated by applications, infrastructure or services.
Metrics
Numeric measurements collected over time, such as latency, error rate, throughput or resource utilization.
Distributed Tracing
Tracking a request across multiple services to understand where time is spent or failures occur.
Service Level Indicator (SLI)
A measured indicator of service behavior, such as latency or availability.
Service Level Objective (SLO)
A target for an SLI, such as 99.9% availability over a defined period.
Site Reliability Engineering (SRE)
An engineering discipline focused on reliability, automation, capacity and operational performance.
Security
Zero Trust
A security model built around continuously verifying access rather than assuming trust based on network location.
Least Privilege
Granting users or systems only the minimum access required to perform their tasks.
Software Bill of Materials (SBOM)
An inventory of software components and dependencies included in an application or artifact.
Secrets Management
Secure storage and distribution of credentials, API keys, certificates and other sensitive values.
Vulnerability Scanning
Automated analysis intended to identify known security weaknesses in code, dependencies, containers or infrastructure.
APIs and Data
API
An interface that allows software systems to exchange data or trigger functionality programmatically.
REST
A common architectural style for web APIs using HTTP resources and methods.
GraphQL
A query language and runtime for APIs that allows clients to request specific fields from a typed schema.
Data Warehouse
A centralized analytical data store optimized for querying large historical datasets.
Data Lake
A storage environment designed to hold large volumes of raw or semi-structured data.
ETL and ELT
ETL means extract, transform, load. ELT means extract, load, transform. The difference is where transformation happens in the data pipeline.
AI and Machine Learning
Large Language Model (LLM)
A machine-learning model trained on large text datasets to predict and generate language.
Inference
Running a trained model to produce an output from new input.
Fine-Tuning
Further training a pre-trained model on additional data to adapt its behavior for a narrower task or domain.
Embedding
A numeric vector representation of data designed to capture semantic or structural similarity.
Vector Database
A database optimized for storing and searching vector embeddings.
Retrieval-Augmented Generation (RAG)
An approach that retrieves relevant external information and supplies it to a generative model as context.
Prompt
The instructions or input provided to a generative model.
Context Window
The amount of input and generated text a model can consider in one interaction.
Hallucination
A generated output that appears plausible but is unsupported, incorrect or fabricated.
Agent
A software system that uses a model to plan or execute multi-step actions, often with tools, memory or external systems.
How to Use This Glossary
When evaluating developer tools, ask vendors to explain the implementation behind broad claims such as “AI-native,” “serverless,” “zero trust” or “real-time observability.” Architecture details, operational constraints and pricing often matter more than the label. Use this glossary alongside How We Review Developer Tools and Editorial Policy.