Adaptive RAG Architecture: Optimize Costs with Dynamic Knowledge Routing

A common mistake in early RAG adoption is “The Monolith Vector Store.”

Engineers try to embed everything—company wikis, daily news, stock prices, and competitor updates—into a single Vector Database (like Pinecone or Weaviate).

This approach hits two walls:

1. Cost: Vector storage and read operations are expensive at scale.
2. Freshness: By the time you scrape, embed, and upsert a news article, it’s already old news.

Enter Adaptive RAG (or the “Router” Architecture).

Instead of treating all queries the same, Adaptive RAG uses an LLM to classify user intent. It routes “Static Knowledge” queries to your Vector DB and “Dynamic/Real-Time” queries to the open web via SearchCans.

This “Just-in-Time” architecture keeps your Vector DB lean (and cheap) while giving your agent infinite, real-time knowledge.

The Economics: Vector DB vs. SearchCans

Why route? Let’s look at the “Cost of Freshness.”

Vector DB Approach

To answer “What is the latest pricing of Tool X?”, you must scrape the page, split text, generate embeddings (OpenAI text-embedding-3), and upsert to the DB. This costs compute + storage + latency.

SearchCans Approach

You simply route the query to our API. We fetch the live SERP and the Markdown content instantly for $0.56/1k requests. Zero storage costs. Zero indexing lag.

Implementation: Building the “Router”

We will build a simple Semantic Router using Python. This router decides if a user’s question needs Internal Knowledge (Vector DB) or External Knowledge (SearchCans).

Step 1: Define the Router Logic

We use a lightweight LLM call to classify the question.

Router Classification Code

import json
from openai import OpenAI

client = OpenAI(api_key="YOUR_OPENAI_KEY")

def route_question(question):
    """
    Classifies the question to determine the data source.
    """
    system_prompt = """
    You are an expert router. 
    If the user asks about specific internal company documents, policies, or historical data, output 'VECTOR_STORE'.
    If the user asks about current events, public market data, competitor pricing, or news, output 'WEB_SEARCH'.
    Return only the JSON: {"datasource": "..."}
    """
    
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": question}
        ],
        response_format={"type": "json_object"}
    )
    
    return json.loads(response.choices[0].message.content)

Step 2: The “Just-in-Time” Web Search Tool

If the router chooses WEB_SEARCH, we call SearchCans. Unlike standard SERP APIs that just give you snippets, we use the Search + Reader combo to get the full context.

import requests

def perform_web_rag(query):
    API_KEY = "YOUR_SEARCHCANS_KEY"
    
    # 1. Search for the URL
    print(f"Routing to SearchCans: {query}")
    search_url = "https://www.searchcans.com/api/search"
    headers = {"Authorization": f"Bearer {API_KEY}"}
    
    # Get top result
    resp = requests.get(search_url, headers=headers, params={"q": query, "engine": "google"})
    results = resp.json().get("organic_results", [])
    
    if not results:
        return "No results found."
        
    top_url = results[0]['link']
    
    # 2. Read the Content (The "Adaptive" Part)
    # We don't store this in a Vector DB. We use it once and discard.
    reader_url = "https://www.searchcans.com/api/url"
    read_resp = requests.get(reader_url, headers=headers, params={"url": top_url, "b": "true"})
    
    data = read_resp.json()
    content = data.get("markdown", "") or data.get("text", "")
    
    return f"Real-Time Context from {top_url}:\n{content[:4000]}"

Step 3: The Adaptive Flow

Now we stitch it together. This simple if/else logic saves you thousands of dollars in Vector DB credits.

Complete Adaptive RAG Pipeline

def run_adaptive_rag(user_question):
    # 1. Route
    decision = route_question(user_question)
    source = decision.get("datasource")
    
    context = ""
    
    if source == "WEB_SEARCH":
        # Cost: $0.00056 (SearchCans)
        context = perform_web_rag(user_question)
    else:
        # Cost: Embedding + Vector DB Read
        context = query_vector_store(user_question)
        
    print(f"Source Used: {source}")
    return context

# Test
# q1 = "What is our company's refund policy?" -> VECTOR_STORE
# q2 = "What is the current stock price of Apple?" -> WEB_SEARCH

Cost Comparison: Static vs. Adaptive RAG

Let’s say you have 1,000 queries per day:

Traditional RAG (Full Indexing)

Monthly Costs:

• Store 10,000 news articles: $500/month (vector DB)
• Daily updates require re-embedding: $100/month (OpenAI embeddings)
• Query cost: $50/month
• Total: $650/month

Adaptive RAG (Router + SearchCans)

Monthly Costs:

• Store only internal docs (1,000 articles): $50/month
• External queries (50% = 15,000/month): $8.40/month (SearchCans)
• Router LLM calls: $15/month
• Total: $73.40/month

Savings

88% cost reduction

Monitoring Router Performance

Track your routing decisions to optimize the system:

Routing Decision Logger

import sqlite3

def log_routing_decision(question, source, timestamp):
    conn = sqlite3.connect('routing_logs.db')
    cursor = conn.cursor()
    
    cursor.execute('''
        INSERT INTO routing_logs (question, source, timestamp)
        VALUES (?, ?, ?)
    ''', (question, source, timestamp))
    
    conn.commit()
    conn.close()

#### Routing Analytics Function

def analyze_routing_stats():
    conn = sqlite3.connect('routing_logs.db')
    cursor = conn.cursor()
    
    cursor.execute('''
        SELECT source, COUNT(*) as count
        FROM routing_logs
        WHERE timestamp > datetime('now', '-7 days')
        GROUP BY source
    ''')
    
    stats = cursor.fetchall()
    conn.close()
    
    for source, count in stats:
        print(f"{source}: {count} queries")

Why SearchCans Fits Adaptive RAG

For this architecture to work, the “External” path must be fast and reliable.

1. Latency: SearchCans is optimized for real-time routing.
2. No Rate Limits: If your application goes viral and routes 1,000 queries/minute to the web, SearchCans handles the burst.
3. Clean Data: The Reader API ensures that your “Just-in-Time” context is high-quality Markdown, not raw HTML garbage.

Conclusion

The future of RAG isn’t a bigger database; it’s a smarter router.

By distinguishing between what you need to remember (Vector DB) and what you need to know right now (SearchCans), you build systems that are both more accurate and significantly cheaper to run.

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Resources

Related Topics:

• Self-Correcting RAG (CRAG) - Advanced RAG patterns
• SERP API Pricing Index 2026 - Compare costs
• Hybrid RAG Tutorial - Practical implementation
• Context Window Engineering - Optimize token usage
• Deep Research Agent - Complex routing workflows

Get Started:

• Free Trial - Get 100 free credits
• API Documentation - Technical reference
• Pricing - Transparent costs
• Playground - Test in browser

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