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ARuntime Reference

Compare Runtime Categories

Compare responsibilities and boundaries before comparing products. A compiler, inference engine, model server, gateway, workflow engine, and agent runtime solve different jobs.

Audience: Technical readers Reading time: 2 minutes Status: Research methodology Last reviewed:

This comparison distinguishes runtime categories by execution unit, input, output, state, policy, tools, traces, and typical boundary. It compares responsibilities, not universal product quality.

Category comparison

Compact runtime category comparison
Category Primary unit Typical input Typical output Owns tools Owns durable state Enforces application policy
Compiler/graph runtime Graph/module/dispatch Model graph and target Executable/tensors No Artifacts only No
Inference engine Model invocation/sequence Model and tokens/tensors Tokens/tensors No KV only, usually ephemeral No
Model server Network request API request and model version Network response No Deployment state Service-level only
AI gateway Proxied request Model/tool traffic Routed request/decision May mediate Limited Edge/gateway policy
Workflow engine Workflow step Events and activities Durable state/result Via activities Yes Generic workflow policy
Agent framework Turn/node/handoff Messages, agents, tools Messages/tool requests Declares Varies Varies
Agentic application runtime Task/tool/approval Actor, task, context, authority Controlled work/evidence Yes Yes Yes
Product application Domain transaction User/business intent Domain outcome Through runtime System of record Owns business rules

Boundary notes

“Owns” means the component provides the lifecycle and enforcement, not merely an integration. A model server can call a policy service without owning application policy. An agent framework can store checkpoints without establishing tenant isolation or compensation.

Products spanning layers

[ar_diagram id=”cross-layer-product-example”]

Record a product’s primary category, secondary categories, and covered layers. Compare only the subset relevant to a shared scope.

Some apparent product comparisons are category errors. GGUF vs GPT demonstrates the difference between an artifact format and a model architecture.

How to compare

Define workload, versions, hardware, deployment, data boundary, SLO, features, exclusions, and evidence. Use product comparison scope before making a table or recommendation.

Maintenance record

Found an error, outdated capability, or unclear category boundary? Submit a correction with a supporting source.