Terminology

2205 terms, tools, patterns and metrics an architect is expected to use precisely. Each one gets a short explanation of what it is, and — where it matters — what it is commonly confused with. Search filters as you type; the column headers sort.

All areas2205 Architecture Fundamentals77 Distributed Systems107 Data Architecture110 Cloud Architecture94 Networking88 API & Integration Architecture82 Reliability & Resilience75 Observability70 Performance & Capacity Engineering72 Security Architecture86 Cost Architecture & FinOps71 Business Architecture67 Architecture Communication67 Enterprise Architecture66 Legacy Modernization71 AI-Era Architecture69 Software Architecture & Engineering71 Architecture Patterns71 Architecture Decision-Making63 The Architect's Meta-Skills61 Delivery & Release Engineering66 Platform Engineering & Developer Experience70 Testing & Quality Architecture66 Data Platform Architecture64 Streaming & Real-Time Data69 Data Governance & Semantics69 Frontend & Experience Architecture66 Edge, Mobile & IoT68 Regulatory & Data Protection Architecture65 Assurance, Audit & Model Risk64

Kind Any concept794 practice734 pattern368 metric125 case-study77 tool62 protocol45

4 terms shown.

Term	Kind	Topic	What it is
Inference Telemetry	practice	AI Observability	Recording the full context of each model interaction — inputs, outputs, tokens, latency, model version and evaluation scores — so quality and cost can be investigated.
Model Drift Monitoring	practice	AI Observability	Detecting that a deployed model's inputs or performance have shifted away from the conditions it was validated under.
Quality Proxy Metric Implicit Quality Signal, Behavioural Quality Indicator	metric	AI Observability	A continuously available production signal that moves when answer quality moves - abstention rate, regeneration rate, escalation rate - used to page someone in the hours before a labelled evaluation could ever run.
Serving Path Divergence Per-Pool Quality Drift, Heterogeneous Fleet Skew	concept	AI Observability	One configuration in a fleet of otherwise identical inference paths behaving differently from its peers - detectable by comparing paths against each other, and invisible to any metric averaged across them.