Table of Contents

From Single Model to Multi-Model: 2026 AI Application Architecture Evolution Guide
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In 2026, a single model can no longer meet the demands of production-grade AI applications. This article walks you through five architecture evolution phases, from the simplest single-model call to autonomous multi-model agent systems, with architecture diagrams, code examples, and migration guides at every step.

Introduction
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The AI landscape of 2026 looks dramatically different from two years ago. Claude 4.7 excels at long-context reasoning, GPT-5.5 dominates multimodal generation, Gemini 3.0 leads in search-augmented scenarios, and Llama 4 shines in private deployment with its open-source ecosystem. With such diverse model options, “which model should I use?” has become a trick question — the real question is: how do you design an architecture where multiple models work together?

This article systematically introduces five architecture evolution phases to help you choose the right pattern based on business scale and technical maturity.

Phase 1: Single Model Architecture (Simple but Limited)
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Architecture Diagram
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┌──────────────┐     ┌──────────────────┐
│              │     │                  │
│  Application │────▶│  AI API Call     │
│  Frontend    │     │  (Single Model)  │
└──────────────┘     └────────┬─────────┘
                              │
                              ▼
                     ┌──────────────────┐
                     │                  │
                     │  Claude 4.7      │
                     │  (Only Choice)   │
                     │                  │
                     └──────────────────┘

Characteristics
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The simplest architecture: the application directly calls a single model’s API. Ideal for prototyping and MVP stages.

Advantages: Fast development, simple logic, easy debugging
Disadvantages: Single point of failure, can’t leverage different models’ strengths, uncontrolled costs

Code Example
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import httpx

class SingleModelClient:
    """Phase 1: Simplest single model call"""

    def __init__(self, api_key: str):
        self.api_key = api_key
        self.model = "claude-4.7"
        self.endpoint = "https://api.xidao.online/v1/chat/completions"

    async def chat(self, messages: list) -> str:
        async with httpx.AsyncClient() as client:
            response = await client.post(
                self.endpoint,
                headers={"Authorization": f"Bearer {self.api_key}"},
                json={
                    "model": self.model,
                    "messages": messages,
                    "max_tokens": 4096
                }
            )
            return response.json()["choices"][0]["message"]["content"]

# Usage
client = SingleModelClient(api_key="xd-xxxxx")
answer = await client.chat([{"role": "user", "content": "Hello"}])

When Should You Move On?
#

Upgrade when your application shows these signals:

Model API timeouts causing user complaints
Different tasks requiring different model capabilities
Monthly API costs exceeding $500 with room for optimization

Phase 2: Model Fallback Architecture (Resilience)
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Architecture Diagram
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┌──────────────┐     ┌──────────────────┐     ┌─────────────────┐
│              │     │                  │     │                 │
│  Application │────▶│  Fallback Router │────▶│  Primary Model  │
│  Frontend    │     │                  │     │  Claude 4.7     │
└──────────────┘     └────────┬─────────┘     └─────────────────┘
                              │ Failure
                              ▼
                     ┌──────────────────┐
                     │  Fallback #1     │
                     │  GPT-5.5         │
                     └────────┬─────────┘
                              │ Failure
                              ▼
                     ┌──────────────────┐
                     │  Fallback #2     │
                     │  Gemini 3.0      │
                     └──────────────────┘

Characteristics
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Introduces fallback mechanisms to automatically switch to backup models when the primary is unavailable. This is the first step toward production readiness.

Advantages: Significantly improved availability (99% → 99.9%)
Disadvantages: Different models may produce inconsistent output formats and quality

Code Example
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import httpx
import asyncio
from dataclasses import dataclass

@dataclass
class ModelConfig:
    name: str
    model_id: str
    priority: int
    timeout: float = 30.0

class FallbackRouter:
    """Phase 2: Model router with fallback mechanism"""

    def __init__(self, api_key: str):
        self.api_key = api_key
        self.endpoint = "https://api.xidao.online/v1/chat/completions"
        self.models = [
            ModelConfig("Claude 4.7", "claude-4.7", priority=1),
            ModelConfig("GPT-5.5", "gpt-5.5", priority=2),
            ModelConfig("Gemini 3.0", "gemini-3.0", priority=3),
            ModelConfig("Llama 4", "llama-4", priority=4),
        ]

    async def chat(self, messages: list) -> dict:
        last_error = None
        for model in sorted(self.models, key=lambda m: m.priority):
            try:
                result = await self._call_model(model, messages)
                return {"model": model.name, "content": result}
            except Exception as e:
                last_error = e
                print(f"[Fallback] {model.name} failed: {e}, trying next...")
                continue
        raise RuntimeError(f"All models unavailable: {last_error}")

    async def _call_model(self, model: ModelConfig, messages: list) -> str:
        async with httpx.AsyncClient(timeout=model.timeout) as client:
            resp = await client.post(
                self.endpoint,
                headers={"Authorization": f"Bearer {self.api_key}"},
                json={"model": model.model_id, "messages": messages}
            )
            resp.raise_for_status()
            return resp.json()["choices"][0]["message"]["content"]

Migration Guide: Phase 1 → Phase 2
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Externalize model configuration: Move model lists to config files or databases
Add retry logic: Implement exponential backoff retries
Monitoring & alerts: Log every fallback event, set alert thresholds
Use XiDao Gateway: Route all model requests through the gateway with built-in fallback

Phase 3: Task-Based Routing Architecture (Optimization)
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Architecture Diagram
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┌──────────────┐     ┌──────────────────┐
│              │     │                  │
│  Application │────▶│  Task Classifier │
│  Frontend    │     │  (Task Router)   │
└──────────────┘     └────────┬─────────┘
                              │
              ┌───────────────┼───────────────┐
              │               │               │
              ▼               ▼               ▼
    ┌──────────────┐ ┌──────────────┐ ┌──────────────┐
    │ Code Gen     │ │ Summarization│ │ Creative     │
    │ Claude 4.7   │ │ GPT-5.5      │ │ Gemini 3.0   │
    │              │ │              │ │              │
    └──────────────┘ └──────────────┘ └──────────────┘
     Strong Reasoning  Long Context    Multimodal

Characteristics
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Different tasks are assigned to the most suitable model. This is the optimal balance of cost and quality.

Advantages: Each task uses the best model, highest overall quality
Disadvantages: Requires task classification capability, increases routing complexity

Code Example
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from enum import Enum
from dataclasses import dataclass

class TaskType(Enum):
    CODE_GENERATION = "code"
    SUMMARIZATION = "summary"
    CREATIVE_WRITING = "creative"
    DATA_ANALYSIS = "analysis"
    TRANSLATION = "translation"

@dataclass
class RoutingRule:
    task_type: TaskType
    model_id: str
    system_prompt: str
    temperature: float = 0.7

class TaskRouter:
    """Phase 3: Intelligent routing based on task type"""

    def __init__(self, api_key: str):
        self.api_key = api_key
        self.gateway = "https://api.xidao.online/v1/chat/completions"
        self.routing_table = {
            TaskType.CODE_GENERATION: RoutingRule(
                TaskType.CODE_GENERATION,
                "claude-4.7",
                "You are a professional software engineer. Generate high-quality, maintainable code.",
                temperature=0.2
            ),
            TaskType.SUMMARIZATION: RoutingRule(
                TaskType.SUMMARIZATION,
                "gpt-5.5",
                "Provide a precise summary while preserving key information.",
                temperature=0.3
            ),
            TaskType.CREATIVE_WRITING: RoutingRule(
                TaskType.CREATIVE_WRITING,
                "gemini-3.0",
                "You are a creative writer with vivid imagination.",
                temperature=0.9
            ),
            TaskType.DATA_ANALYSIS: RoutingRule(
                TaskType.DATA_ANALYSIS,
                "claude-4.7",
                "You are a data analysis expert. Provide rigorous analysis.",
                temperature=0.1
            ),
            TaskType.TRANSLATION: RoutingRule(
                TaskType.TRANSLATION,
                "gpt-5.5",
                "Provide high-quality multilingual translation preserving the original style.",
                temperature=0.3
            ),
        }

    async def classify_task(self, user_message: str) -> TaskType:
        """Classify task using lightweight rules or small model"""
        keywords = {
            TaskType.CODE_GENERATION: ["code", "function", "bug", "implement", "program"],
            TaskType.SUMMARIZATION: ["summary", "summarize", "overview", "extract"],
            TaskType.CREATIVE_WRITING: ["write", "create", "story", "copy"],
            TaskType.DATA_ANALYSIS: ["analyze", "data", "statistics", "trend"],
            TaskType.TRANSLATION: ["translate", "翻译"],
        }
        for task_type, kws in keywords.items():
            if any(kw in user_message.lower() for kw in kws):
                return task_type
        return TaskType.CREATIVE_WRITING  # default

    async def chat(self, messages: list) -> dict:
        user_msg = messages[-1]["content"]
        task_type = await self.classify_task(user_msg)
        rule = self.routing_table[task_type]

        full_messages = [
            {"role": "system", "content": rule.system_prompt}
        ] + messages

        import httpx
        async with httpx.AsyncClient() as client:
            resp = await client.post(
                self.gateway,
                headers={"Authorization": f"Bearer {self.api_key}"},
                json={
                    "model": rule.model_id,
                    "messages": full_messages,
                    "temperature": rule.temperature,
                }
            )
            return {
                "task": task_type.value,
                "model": rule.model_id,
                "content": resp.json()["choices"][0]["message"]["content"]
            }

Migration Guide: Phase 2 → Phase 3
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Analyze historical requests: Map task type distributions and model performance
Build routing rule table: Design routing strategies for your business scenarios
Implement task classifier: Start with keyword rules, upgrade to model-based classification
A/B testing: Run online experiments on routing strategies

Phase 4: Ensemble / Multi-Model Architecture (Quality)
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Architecture Diagram
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┌──────────────┐     ┌──────────────────────────────┐
│              │     │      Ensemble Inference       │
│  Application │────▶│          Engine               │
│  Frontend    │     │                              │
└──────────────┘     │  ┌──────┐ ┌──────┐ ┌──────┐  │
                     │  │Claude│ │GPT   │ │Gemini│  │
                     │  │4.7   │ │5.5   │ │3.0   │  │
                     │  └──┬───┘ └──┬───┘ └──┬───┘  │
                     │     │        │        │      │
                     │     ▼        ▼        ▼      │
                     │  ┌──────────────────────┐    │
                     │  │  Quality Scoring &   │    │
                     │  │  Result Fusion       │    │
                     │  └──────────┬───────────┘    │
                     │             │                 │
                     └─────────────┼─────────────────┘
                                   ▼
                            ┌──────────────┐
                            │  Best Result  │
                            └──────────────┘

Characteristics
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Multiple models perform inference in parallel, with a scoring mechanism to select the best result or fuse multiple outputs. Ideal for quality-critical scenarios.

Advantages: Highest output quality, reduced hallucinations and errors
Disadvantages: Multiply costs, increased latency

Code Example
#

import asyncio
import httpx
import time
from dataclasses import dataclass

@dataclass
class ModelResponse:
    model: str
    content: str
    latency_ms: float
    score: float = 0.0

class EnsembleEngine:
    """Phase 4: Multi-model ensemble inference engine"""

    def __init__(self, api_key: str):
        self.api_key = api_key
        self.gateway = "https://api.xidao.online/v1/chat/completions"
        self.ensemble_models = [
            {"id": "claude-4.7", "weight": 0.4},
            {"id": "gpt-5.5", "weight": 0.35},
            {"id": "gemini-3.0", "weight": 0.25},
        ]

    async def _call_single(self, model_id: str, messages: list) -> ModelResponse:
        start = time.monotonic()
        async with httpx.AsyncClient(timeout=60.0) as client:
            resp = await client.post(
                self.gateway,
                headers={"Authorization": f"Bearer {self.api_key}"},
                json={"model": model_id, "messages": messages, "temperature": 0.3}
            )
            latency = (time.monotonic() - start) * 1000
            content = resp.json()["choices"][0]["message"]["content"]
            return ModelResponse(model=model_id, content=content, latency_ms=latency)

    async def score_response(self, query: str, response: ModelResponse) -> float:
        """Use a judge model to score the response"""
        judge_messages = [
            {"role": "system", "content": "You are an AI output quality judge. Score from 0-10 on accuracy, completeness, and fluency. Return only the number."},
            {"role": "user", "content": f"Question: {query}\n\nAnswer: {response.content}\n\nScore:"}
        ]
        score_resp = await self._call_single("llama-4", judge_messages)
        try:
            return float(score_resp.content.strip()) / 10.0
        except ValueError:
            return 0.5

    async def ensemble_chat(self, messages: list) -> dict:
        query = messages[-1]["content"]

        # 1. Parallel model calls
        tasks = [
            self._call_single(m["id"], messages)
            for m in self.ensemble_models
        ]
        responses = await asyncio.gather(*tasks, return_exceptions=True)
        valid_responses = [r for r in responses if isinstance(r, ModelResponse)]

        # 2. Parallel scoring
        score_tasks = [
            self.score_response(query, r) for r in valid_responses
        ]
        scores = await asyncio.gather(*score_tasks)

        for resp, score in zip(valid_responses, scores):
            resp.score = score

        # 3. Select best result
        best = max(valid_responses, key=lambda r: r.score)

        return {
            "model": best.model,
            "content": best.content,
            "score": best.score,
            "all_scores": {r.model: r.score for r in valid_responses},
            "strategy": "ensemble_best_of_n"
        }

Migration Guide: Phase 3 → Phase 4
#

Identify critical tasks: Not everything needs ensemble inference — select high-value scenarios
Implement async parallel calls: Use asyncio.gather for parallel requests
Design scoring system: Start with simple rule-based scoring, evolve to judge models
Cost controls: Set budget limits and trigger conditions for ensemble inference

Phase 5: Agentic Multi-Model Architecture (Autonomous)
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Architecture Diagram
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┌──────────────────────────────────────────────────────────┐
│                 Agent Orchestrator Layer                   │
│                                                          │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐     │
│  │   Planner   │  │  Executor   │  │  Validator  │     │
│  └──────┬──────┘  └──────┬──────┘  └──────┬──────┘     │
│         │                │                │             │
│         ▼                ▼                ▼             │
│  ┌──────────────────────────────────────────────┐       │
│  │          Model Capability Registry           │       │
│  │                                              │       │
│  │  Claude 4.7  → Reasoning, Code, Long Ctx     │       │
│  │  GPT-5.5     → Multimodal, Chat, Functions   │       │
│  │  Gemini 3.0  → Search Augmented, Realtime    │       │
│  │  Llama 4     → Private Data, Local Inference │       │
│  │  DeepSeek V4 → Math, Logic, Reasoning        │       │
│  └──────────────────────────────────────────────┘       │
│         │                │                │             │
│         ▼                ▼                ▼             │
│  ┌──────────────────────────────────────────────┐       │
│  │              Tools & Data Layer              │       │
│  │  [Search] [Database] [API] [FS] [VectorDB]  │       │
│  └──────────────────────────────────────────────┘       │
└──────────────────────────────────────────────────────────┘
                              │
                              ▼
                     ┌──────────────────┐
                     │  User / System   │
                     └──────────────────┘

Characteristics
#

The most advanced architecture form: the agent system autonomously decides which models to call, in what order, and how to combine results. Models are no longer tools being called — they become “brain components” of the agent.

Advantages: Fully automated, adaptive, can handle complex multi-step tasks
Disadvantages: Complex architecture, difficult debugging, requires mature infrastructure

Code Example
#

import json
import httpx
from typing import Any

class ModelCapability:
    """Model capability descriptor"""
    def __init__(self, model_id: str, capabilities: list[str],
                 cost_per_1k: float, max_context: int):
        self.model_id = model_id
        self.capabilities = capabilities
        self.cost_per_1k = cost_per_1k
        self.max_context = max_context

class AgenticMultiModel:
    """Phase 5: Autonomous multi-model agent system"""

    def __init__(self, api_key: str):
        self.api_key = api_key
        self.gateway = "https://api.xidao.online/v1/chat/completions"
        self.registry = {
            "claude-4.7": ModelCapability(
                "claude-4.7",
                ["reasoning", "code", "long_context", "analysis"],
                cost_per_1k=0.015, max_context=500_000
            ),
            "gpt-5.5": ModelCapability(
                "gpt-5.5",
                ["multimodal", "conversation", "function_calling", "vision"],
                cost_per_1k=0.020, max_context=256_000
            ),
            "gemini-3.0": ModelCapability(
                "gemini-3.0",
                ["search_augmented", "realtime", "multimodal"],
                cost_per_1k=0.012, max_context=2_000_000
            ),
            "llama-4": ModelCapability(
                "llama-4",
                ["private_data", "local_inference", "fine_tuned"],
                cost_per_1k=0.005, max_context=128_000
            ),
            "deepseek-v4": ModelCapability(
                "deepseek-v4",
                ["math", "logic", "code", "reasoning"],
                cost_per_1k=0.008, max_context=256_000
            ),
        }

    async def plan_and_execute(self, user_message: str, context: list = None) -> dict:
        """Agent autonomously plans and executes multi-model tasks"""

        planning_prompt = f"""You are an AI agent orchestrator. Create an execution plan based on the user's request.

Available models:
{json.dumps({k: {"caps": v.capabilities, "cost": v.cost_per_1k} for k, v in self.registry.items()}, indent=2)}

User request: {user_message}

Return a JSON execution plan with a steps array. Each step specifies the model and task.
Return only JSON, nothing else."""

        plan_messages = [
            {"role": "system", "content": planning_prompt},
            {"role": "user", "content": user_message}
        ]

        # Use Claude 4.7 for planning
        plan_resp = await self._raw_call("claude-4.7", plan_messages, temperature=0.2)

        try:
            plan = json.loads(plan_resp)
        except json.JSONDecodeError:
            # Fallback to simple single model call
            result = await self._raw_call("claude-4.7",
                [{"role": "user", "content": user_message}])
            return {"strategy": "fallback", "content": result}

        # Execute each step in the plan
        step_results = []
        for step in plan.get("steps", []):
            model_id = step.get("model", "claude-4.7")
            query = step.get("query", user_message)
            result = await self._raw_call(model_id,
                [{"role": "user", "content": query}])
            step_results.append({
                "step": step.get("name", "unnamed"),
                "model": model_id,
                "result": result
            })

        # Synthesize all results
        synthesis_input = "\n\n".join(
            f"[{s['step']} - {s['model']}]: {s['result']}" for s in step_results
        )
        final = await self._raw_call("claude-4.7", [
            {"role": "system", "content": "Synthesize the following multi-model results into the best possible answer."},
            {"role": "user", "content": synthesis_input}
        ], temperature=0.3)

        return {
            "strategy": "agentic_multi_model",
            "plan": plan,
            "step_results": step_results,
            "final_answer": final
        }

    async def _raw_call(self, model_id: str, messages: list,
                        temperature: float = 0.7) -> str:
        async with httpx.AsyncClient(timeout=120.0) as client:
            resp = await client.post(
                self.gateway,
                headers={"Authorization": f"Bearer {self.api_key}"},
                json={
                    "model": model_id,
                    "messages": messages,
                    "temperature": temperature
                }
            )
            return resp.json()["choices"][0]["message"]["content"]

Migration Guide: Phase 4 → Phase 5
#

Build a model capability registry: Describe each model’s capabilities, costs, and constraints
Implement tool-calling framework: Enable agents to call models, search, and data tools
Introduce plan-execute-verify loops: Agent plans first, executes, then validates
Gradual authorization: Start with simple tasks, progressively increase agent autonomy
Comprehensive observability: Log every decision and execution step

XiDao API Gateway: Foundation for Multi-Model Architecture
#

Regardless of which phase you’re in, the XiDao API Gateway is the ideal foundation for building multi-model architectures:

┌─────────────────────────────────────────────────────┐
│                  XiDao API Gateway                   │
│                                                     │
│  ┌───────────┐ ┌───────────┐ ┌───────────┐        │
│  │ Unified   │ │ Smart     │ │Observability│        │
│  │ Access    │ │ Routing   │ │ Layer      │        │
│  │           │ │           │ │            │        │
│  │ • OpenAI  │ │ • Load    │ │ • Logs     │        │
│  │  Compat.  │ │  Balancing│ │ • Metrics  │        │
│  │ • Auth    │ │ • Fallback│ │ • Tracing  │        │
│  │ • Rate    │ │ • Cost    │ │ • Alerts   │        │
│  │  Limiting │ │  Optimize │ │            │        │
│  └───────────┘ └───────────┘ └───────────┘        │
│                                                     │
│  ┌─────────────────────────────────────────────┐    │
│  │          Model Provider Adapters            │    │
│  │  Anthropic │ OpenAI │ Google │ Meta │ ...   │    │
│  └─────────────────────────────────────────────┘    │
└─────────────────────────────────────────────────────┘

Core Advantages
#

Feature	Description
Unified API	OpenAI-compatible format, seamless model switching
Smart Fallback	Built-in fallback mechanism, automatic model switching
Cost Optimization	Auto-selects the best cost-performance model per task
Observability	Full-chain tracing, model selection visibility per request
Streaming Support	Unified SSE streaming output across all models

Integration Example
#

# Just change the endpoint to access XiDao Gateway's multi-model capabilities
import openai

client = openai.OpenAI(
    base_url="https://api.xidao.online/v1",
    api_key="xd-your-key"
)

# Automatically routes to the optimal model
response = client.chat.completions.create(
    model="auto",  # XiDao auto-selects the best model
    messages=[{"role": "user", "content": "Analyze this financial report"}],
)

Architecture Selection Decision Matrix
#

Phase	Scale	Monthly Cost	Availability	Quality	Complexity
Phase 1	Personal/MVP	< $100	99%	★★★	Low
Phase 2	Startup	$100-1K	99.9%	★★★	Low-Med
Phase 3	Growth	$500-5K	99.9%	★★★★	Medium
Phase 4	Mature Product	$2K-20K	99.95%	★★★★★	Med-High
Phase 5	Platform	$5K-50K+	99.99%	★★★★★	High

Summary & Recommendations
#

In 2026, AI application architecture has evolved from “pick a model” to “orchestrate multiple models.” Key recommendations:

Don’t skip phases: Each phase has its value and lessons
Start from Phase 2: Any production environment should have fallback mechanisms
Task routing is the highest-ROI upgrade: Phase 3 is the sweet spot for most enterprises
Ensemble inference for critical scenarios: Not every request needs multi-model
Agentic architecture is the future direction: But it requires solid infrastructure

Regardless of which phase you’re in, XiDao API Gateway helps you rapidly implement multi-model architecture. Start today by replacing your single-model endpoint with https://api.xidao.online for plug-and-play multi-model capabilities.

Next step: Visit the XiDao Documentation for a complete multi-model architecture practice guide, or create your first multi-model project directly in the Console.

Written by the XiDao team, last updated May 2026. For questions, reach out via GitHub.

From Single Model to Multi-Model: 2026 AI Application Architecture Evolution Guide#

Introduction#

Phase 1: Single Model Architecture (Simple but Limited)#

Architecture Diagram#

Characteristics#

Code Example#

When Should You Move On?#

Phase 2: Model Fallback Architecture (Resilience)#

Architecture Diagram#

Characteristics#

Code Example#

Migration Guide: Phase 1 → Phase 2#

Phase 3: Task-Based Routing Architecture (Optimization)#

Architecture Diagram#

Characteristics#

Code Example#

Migration Guide: Phase 2 → Phase 3#

Phase 4: Ensemble / Multi-Model Architecture (Quality)#

Architecture Diagram#

Characteristics#

Code Example#

Migration Guide: Phase 3 → Phase 4#

Phase 5: Agentic Multi-Model Architecture (Autonomous)#

Architecture Diagram#

Characteristics#

Code Example#

Migration Guide: Phase 4 → Phase 5#

XiDao API Gateway: Foundation for Multi-Model Architecture#

Core Advantages#

Integration Example#

Architecture Selection Decision Matrix#

Summary & Recommendations#

Related

From Single Model to Multi-Model: 2026 AI Application Architecture Evolution Guide
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Introduction
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Phase 1: Single Model Architecture (Simple but Limited)
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Architecture Diagram
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Characteristics
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Code Example
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When Should You Move On?
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Phase 2: Model Fallback Architecture (Resilience)
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Architecture Diagram
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Characteristics
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Code Example
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Migration Guide: Phase 1 → Phase 2
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Phase 3: Task-Based Routing Architecture (Optimization)
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Architecture Diagram
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Characteristics
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Code Example
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Migration Guide: Phase 2 → Phase 3
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Phase 4: Ensemble / Multi-Model Architecture (Quality)
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Architecture Diagram
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Characteristics
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Code Example
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Migration Guide: Phase 3 → Phase 4
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Phase 5: Agentic Multi-Model Architecture (Autonomous)
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Architecture Diagram
#

Characteristics
#

Code Example
#

Migration Guide: Phase 4 → Phase 5
#

XiDao API Gateway: Foundation for Multi-Model Architecture
#

Core Advantages
#

Integration Example
#

Architecture Selection Decision Matrix
#

Summary & Recommendations
#