Compare Architectures
Side-by-side: use case, requirements, latency, cost, strengths, weaknesses, failure modes, example — and when to choose each.
Workflow vs AgentSingle agent vs Multi-agentRAG vs Fine-tuningVector search vs BM25Function calling vs MCPDirect API integration vs MCPPlan-then-execute vs ReActShort-term memory vs Long-term memoryLLM-as-judge vs Deterministic evaluatorsHuman-in-the-loop vs Human-on-the-loopSupervisor vs PipelinePrompt caching vs Semantic cachingRouter vs SupervisorFramework vs No framework
| Direct API integration | MCP | |
|---|---|---|
| Use case | Code calls the third-party API; the LLM never selects the endpoint | The LLM discovers and invokes capabilities at runtime |
| Requirements | HTTP client, auth, your own typed wrapper | MCP server exposing tools / resources, client integration |
| Latency | Lowest: one HTTP call | Transport hop plus tool-selection reasoning |
| Cost | API cost only | API cost plus tokens for tool schemas and selection |
| Strengths | Deterministic, typed, testable with ordinary mocks | Model can pick among many capabilities without bespoke code |
| Weaknesses | Each new capability is code you must write | Adds a protocol layer to debug and secure |
| Failure modes | Hard-coded flow cannot cover unexpected needs | Model calls the wrong tool; server exposes too much surface |
| Example | Workflow step that always fetches a customer record | Agent that may need any of 30 Jira / Slack / Drive operations |
| Choose this when | Choose direct integration when the call is a fixed step in a workflow. | Choose MCP when the model needs runtime choice over a broad, evolving tool surface. |