Cloudsway vs Exa: Which Search API for Your Agent

TL;DR — Cloudsway Search delivers ranked web results with summaries (think “Google for agents”). Exa Search delivers semantic content matching (think “find pages by meaning”). Different search intents, different agent patterns. Use Cloudsway for research, monitoring, and fact-checking. Use Exa for content discovery, RAG augmentation, and similarity-based retrieval. Best strategy: use both for different stages of the same workflow.

Two search paradigms in one ecosystem

SandBase provides access to both Cloudsway Search and Exa Search through the same API infrastructure. They solve fundamentally different problems:

Cloudsway Search answers: “What does the web say about X?”

Returns ranked results like a traditional search engine
Optimized for breadth, freshness, and authority
Adds dynamic summaries to each result

Exa Search answers: “Find content that matches this concept/meaning”

Returns content by semantic similarity
Optimized for precision and conceptual matching
Highlights relevant passages within documents

For a deep dive into Cloudsway’s capabilities, see our Cloudsway Search guide. For Exa’s comparison with other search tools, see our Exa vs Tavily vs Firecrawl comparison.

Head-to-head comparison

Dimension	Cloudsway Search	Exa Search
Search paradigm	Ranked relevance	Semantic similarity
Best query type	Keywords / natural questions	Descriptive content statements
Index source	Live web (broad)	Curated web content (deep)
Freshness	Real-time	Crawl-dependent (hours-days)
Result format	Title + URL + snippet + summary	Content + highlights + score
Full text access	Optional (per-result)	Built-in (content extraction)
Authority signals	Yes (domain authority, freshness)	No (pure content matching)
Language support	Multi-language	Primarily English
Operations	1 (search)	Multiple (search, find_similar, contents)
Price point	Per query	Per query
On SandBase	Yes	Yes

Query style differences

The same information need expressed differently for each API:

Intent	Cloudsway query	Exa query
Learn about a topic	”AI agent memory architecture guide 2026"	"technical explanation of how AI agents store and retrieve memories across sessions”
Find competitors	”alternatives to LangChain for agent building"	"open source framework for building AI agents with tool use and memory, similar to LangChain”
Get latest news	”OpenAI product announcement July 2026”	N/A (Exa isn’t ideal for recency)
Find similar content	N/A (Cloudsway isn’t similarity-based)	“content similar to: [paste your article URL]“
Research pricing	”AI image generation API pricing comparison"	"detailed breakdown of costs per image for different AI generation services”
Technical deep dive	”vector database indexing performance benchmarks"	"content that explains HNSW index tuning parameters and their effect on recall”

When to use Cloudsway

Pattern 1: Current events and monitoring

from openai import OpenAI

client = OpenAI(base_url="https://api.sandbase.ai/v1", api_key="your-key")

# Cloudsway excels at: "What's happening right now?"
response = client.post("/v1/run", body={
    "model": "cloudsway/search",
    "operation": "search",
    "input": {
        "query": "AI regulation updates European Union August 2026",
        "num_results": 10,
        "freshness": "week",
        "include_summary": True
    }
})

# Returns: latest news articles, official announcements, analysis pieces
# Ranked by relevance and authority

Pattern 2: Market research

# Finding what competitors are doing
response = client.post("/v1/run", body={
    "model": "cloudsway/search",
    "operation": "search",
    "input": {
        "query": "enterprise AI agent platform market share 2026 report",
        "num_results": 15,
        "include_summary": True,
        "include_full_text": True
    }
})

# Returns: analyst reports, blog posts, market studies — ranked by authority

Pattern 3: Fact verification

# Checking if a claim is supported by authoritative sources
def verify_with_cloudsway(claim: str) -> list:
    response = client.post("/v1/run", body={
        "model": "cloudsway/search",
        "operation": "search",
        "input": {
            "query": claim,
            "num_results": 10,
            "include_summary": True
        }
    })
    return response.json()["output"]["results"]

# Authority signals in ranking help identify credible sources

When to use Exa

Pattern 1: Content discovery by meaning

# Exa excels at: "Find content that discusses this concept"
response = client.post("/v1/run", body={
    "model": "exa/search",
    "operation": "search",
    "input": {
        "query": "technical blog posts explaining how to implement "
                 "tool selection algorithms in autonomous agents "
                 "using reward-based approaches",
        "num_results": 10,
        "type": "neural"
    }
})

# Returns: highly relevant technical content that matches the *concept*
# even if they don't use the exact keywords

Pattern 2: Similar content discovery

# Find pages similar to a known good resource
response = client.post("/v1/run", body={
    "model": "exa/search",
    "operation": "find_similar",
    "input": {
        "url": "https://example.com/great-article-about-agent-memory",
        "num_results": 10
    }
})

# Returns: semantically similar pages — great for building reading lists

Pattern 3: RAG augmentation

# Finding precise content to add to a RAG context window
response = client.post("/v1/run", body={
    "model": "exa/search",
    "operation": "search",
    "input": {
        "query": "step-by-step implementation of hierarchical planning "
                 "in multi-agent systems with Python code examples",
        "num_results": 5,
        "type": "neural",
        "contents": {"text": True}  # Get full text for RAG
    }
})

# Returns: precise, relevant content ready to inject into LLM context

Decision framework

Choose Cloudsway when:

Signal	Example
You need recency	”What happened with X this week?”
Authority matters	”Official documentation for Y”
Broad coverage needed	”All perspectives on Z”
News/events	”Latest announcements from competitor”
Verification	”Is this claim supported by sources?”
Quantity over precision	”Give me 20 sources about this topic”

Choose Exa when:

Signal	Example
Conceptual matching	”Content that explains X in way Y”
Precision over quantity	”The 3 most relevant technical deep dives”
Similarity search	”Pages like this one”
RAG context	”Precise content to feed into an LLM”
Niche topics	”Specific technical implementation details”
Content quality focus	”Well-written explanations” (Exa’s index is curated)

Combining both: the power pattern

The most effective agent search strategy uses both APIs at different stages:

class DualSearchAgent:
    """Use Cloudsway for breadth, Exa for depth."""
    
    def __init__(self, api_key: str):
        self.client = OpenAI(
            base_url="https://api.sandbase.ai/v1",
            api_key=api_key
        )
    
    def deep_research(self, topic: str) -> dict:
        """Research using both search paradigms for maximum coverage."""
        
        # Stage 1: Cloudsway for broad landscape
        # "What's out there about this topic?"
        landscape = self._cloudsway_search(
            f"{topic} overview guide analysis 2026",
            num_results=15
        )
        
        # Stage 2: Identify the most interesting angles from results
        angles = self._extract_angles(landscape, topic)
        
        # Stage 3: Exa for precise deep content on each angle
        # "Find the best content that deeply explains each subtopic"
        deep_content = {}
        for angle in angles[:3]:
            deep_content[angle] = self._exa_search(
                f"detailed technical explanation of {angle} in context of {topic}",
                num_results=3
            )
        
        # Stage 4: Cloudsway for latest developments
        latest = self._cloudsway_search(
            f"{topic} latest news developments",
            num_results=5,
            freshness="week"
        )
        
        return {
            "landscape": landscape,
            "deep_dives": deep_content,
            "latest_developments": latest,
            "total_sources": len(landscape) + sum(len(v) for v in deep_content.values()) + len(latest)
        }
    
    def _cloudsway_search(self, query: str, num_results: int = 10, freshness: str = None):
        input_params = {
            "query": query,
            "num_results": num_results,
            "include_summary": True
        }
        if freshness:
            input_params["freshness"] = freshness
        
        response = self.client.post("/v1/run", body={
            "model": "cloudsway/search",
            "operation": "search",
            "input": input_params
        })
        return response.json()["output"]["results"]
    
    def _exa_search(self, query: str, num_results: int = 5):
        response = self.client.post("/v1/run", body={
            "model": "exa/search",
            "operation": "search",
            "input": {
                "query": query,
                "num_results": num_results,
                "type": "neural",
                "contents": {"text": True}
            }
        })
        return response.json()["output"]["results"]
    
    def _extract_angles(self, results: list, topic: str) -> list[str]:
        summaries = "\n".join(r.get("summary", r.get("snippet", "")) for r in results[:10])
        response = self.client.chat.completions.create(
            model="openai/gpt-4o-mini",
            messages=[{
                "role": "user",
                "content": f"Based on these search results about '{topic}', "
                          f"what are the 5 most interesting angles to explore deeper?\n\n"
                          f"{summaries}\n\nList 5 angles, one per line."
            }],
            max_tokens=200
        )
        return [line.strip() for line in response.choices[0].message.content.strip().split("\n") if line.strip()]

Performance comparison

Metric	Cloudsway	Exa
Typical latency	1–3s	1–4s
Results freshness	Minutes	Hours–days
Relevance for broad queries	9/10	7/10
Relevance for specific concepts	7/10	9/10
Content extraction quality	Good (optional full text)	Excellent (built-in)
Multi-language queries	Strong	Moderate
API simplicity	Very simple (1 operation)	Multiple operations

Cost comparison

Usage	Cloudsway monthly	Exa monthly	Combined
50 queries/day	~$30–50	~$30–50	~$60–100
200 queries/day	~$80–150	~$80–150	~$160–300
1000 queries/day	~$300–500	~$200–400	~$500–900

Both are priced per-query through SandBase. The combined cost of using both strategically is often less than doubling, because you use each where it’s most effective — reducing wasted queries.

Agent architecture recommendations

For a research agent

User question → Cloudsway (broad search, 10 results)
             → LLM identifies knowledge gaps
             → Exa (precise content for gaps, 3 results each)
             → LLM synthesizes final answer with citations

For a RAG augmentation agent

User query → Exa (find semantically relevant content, 5 results)
          → Inject as context into LLM
          → If LLM confidence is low:
            → Cloudsway (broaden search, verify facts)
            → Re-generate answer

For a monitoring/alerting agent

Scheduled: Cloudsway (daily search for competitor news)
         → LLM classifies: signal vs noise
         → If signal detected:
           → Exa (find similar analysis/commentary)
           → Generate alert with context

Summary

Cloudsway and Exa are complementary, not competing. Think of them as two lenses:

Cloudsway = wide-angle lens. See the full landscape, find what’s happening now, rank by authority.
Exa = macro lens. Zoom into exactly the content that matches your specific concept, with precision.

The best agent search architectures use both. Cloudsway for breadth and freshness, Exa for precision and depth. The unified SandBase API makes switching between them trivial — same client, same authentication, same billing.

Your agent’s search quality directly determines its output quality. Investing in the right search strategy — choosing the right API for each query type — is one of the highest-leverage improvements you can make to any agent system.