Carbonteq
Best Practices/LLM Apps/Functional Arch

Comparison

Side-by-side comparison of chatbot-based vs workflow-based LLM architectures

Architecture Comparison

Quick Decision Matrix

FactorChatbot-BasedWorkflow-Based
Primary Use CaseReal-time conversationMulti-step business processes
Latency< 2.5s response timeMinutes to hours
User InteractionInteractive, conversationalBatch processing, async
ComplexitySimple to moderateModerate to high
Audit RequirementsBasic loggingFull audit trails
Human-in-the-LoopOptionalOften required
ScalabilityHigh concurrent usersHigh throughput processing

Detailed Comparison

Use Cases

Chatbot-Based

  • Customer support and help desks
  • Interactive assistants and virtual agents
  • Real-time Q&A systems
  • Conversational interfaces for applications
  • Educational tutoring systems
  • Creative collaboration tools

Workflow-Based

  • Document processing pipelines
  • Content generation workflows
  • Data analysis and reporting
  • Compliance and audit processes
  • Multi-step approvals and reviews
  • Batch processing operations

Performance Characteristics

Latency

  • Chatbot: p95 < 1.5–2.5s with streaming
  • Workflow: Minutes to hours depending on complexity

Throughput

  • Chatbot: High concurrent users (thousands)
  • Workflow: High batch processing (millions of items)

Resource Usage

  • Chatbot: CPU-intensive per request
  • Workflow: Memory-intensive for large datasets

Building Block Behavior

Building BlockChatbot-BasedWorkflow-Based
PromptsTurn-scoped, streaming-friendly, tool schemas inlineStep-scoped, deterministic, strict schemas, low temperature
AgentsSingle orchestrator, pre/post processors, telemetry hooksOrchestrator + workers, state transforms, step hooks
LLM GuardsLightweight, fast checks, interactive fallbacksHard gates, retry/human review, audited decisions
EvalsOnline sampling, A/B testing, real-time alertsStep gates, regression packs, quality budgets
RAGOn-demand retrieval, session filters, cachingStage-specific retrieval, pre-ingestion, artifact storage
MemoryConversation + short-term TTL, user preferencesRun context + artifacts, durable TTL, cross-run caches
Operationalp95 latency, cost per turn, chat observabilityExactly-once effects, queue health, run timelines