Keiro API vs Tavily: Which Web Search API Is Right for You?

Introduction

Tavily has been a popular choice for AI developers needing web search capabilities. However, with Keiro now available, there's a compelling alternative worth considering. Let's dive into a detailed comparison.

The Pricing Reality

Let's address the elephant in the room: pricing.

Yes, you read that right. Tavily costs over 13 times more than Keiro for the same API calls.

Monthly Cost Comparison

For an AI agent making 100,000 requests/month:

That's nearly $9,000 in annual savings.

Feature Analysis

Core Capabilities

API Richness

Keiro provides 6 distinct endpoints vs Tavily's 2:

Keiro:

1. /search - Standard search

2. /search-pro - Advanced filtering

3. /research - Multi-source research

4. /research-pro - Deep research

5. /answer - Direct answers

6. /web-crawler - Full page extraction

Tavily:

1. /search - Basic search

2. /extract - Content extraction

Real-World Performance

Speed Test Results

We benchmarked both APIs with identical queries:

Query: "Latest developments in quantum computing 2026"
Iterations: 500

Keiro:
- Mean: 234ms
- P50: 215ms
- P99: 489ms

Tavily:
- Mean: 298ms
- P50: 276ms
- P99: 612ms

Keiro is consistently 20-25% faster.

Accuracy Assessment

Using our benchmark suite across 5 categories:

Developer Experience

Keiro SDK

# Python
import requests

# Keiro Search API
API_URL = "https://kierolabs.space/api/search"

# Request payload (apiKey goes in the body)
payload = {
    "query": "what is python",
    "apiKey": "YOUR_API_KEY"
}

# Make request
response = requests.post(
    API_URL,
    json=payload,
    headers={"Content-Type": "application/json"}
)
result = response.json()

# Access the data
data = result.get("data", {})
print(f"Credits remaining: {result.get('creditsRemaining')}")

# Process results
for item in data.get("extracted_content", [])[:3]:
    print(f"- {item.get('title', 'Untitled')}")
    print(f"  {item.get('url', '')}")

// JavaScript/TypeScript
const API_URL = "https://kierolabs.space/api/search";

// Request payload (apiKey goes in the body)
const payload = {
    query: "what is python",
    apiKey: "YOUR_API_KEY"
};

const response = await fetch(API_URL, {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify(payload)
});

const result = await response.json();
console.log("Credits remaining:", result.creditsRemaining);

// Process results
result.data?.extracted_content?.slice(0, 3).forEach(item => {
    console.log(`- ${item.title}`);
    console.log(`  ${item.url}`);
});

Tavily SDK

# Python
from tavily import TavilyClient

tavily = TavilyClient(api_key="your-key")
results = tavily.search("AI news")

Both offer clean SDKs, but Keiro provides more endpoints and better TypeScript support.

Use Case Analysis

RAG Pipelines

Both work well for RAG, but Keiro's research endpoints provide better multi-source aggregation:

# Keiro research for comprehensive RAG
const API_URL = "https://kierolabs.space/api/research-pro";

const payload = {
    query: "future of ai agents",
    apiKey: "YOUR_API_KEY",
    cache_search: true
};

// Make request (Latency ~30-60s)
const response = await fetch(API_URL, {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify(payload)
});

const data = await response.json();

// Process results
console.log(`Credits remaining: ${data.creditsRemaining}`);
data.data?.extracted_content?.slice(0, 3).forEach(item => {
    console.log(`- ${item.title}`);
    console.log(`  ${item.url}`);
});

AI Agents

For autonomous agents, Keiro's unlimited batch processing is a game-changer:

# Process thousands of queries without per-request charges
import axios from 'axios';

const batchSearch = async (query) => {
  const response = await axios.post(
    'https://kierolabs.space/api/batch-search',
    {
      query,
      apiKey: 'your_api_key_here'
    }
  );

  // Note: Request queued for ~7 seconds
  const { data, creditsRemaining } = response.data;
  console.log(`Found ${data.results?.length || 0} results`);
  console.log(`Credits Remaining: ${creditsRemaining}`);
  
  return data;
};

With Tavily, this would cost significantly more.

Migration Path

Moving from Tavily to Keiro is simple:

# Tavily (before)
from tavily import TavilyClient
client = TavilyClient(api_key=TAVILY_KEY)
results = client.search(query)

# Keiro (after)
const API_URL = "https://kierolabs.space/api/search";

// Request payload (apiKey goes in the body)
const payload = {
    query: "what is python",
    apiKey: "YOUR_API_KEY"
};

const response = await fetch(API_URL, {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify(payload)
});

const result = await response.json();
console.log("Credits remaining:", result.creditsRemaining);

// Process results
result.data?.extracted_content?.slice(0, 3).forEach(item => {
    console.log(`- ${item.title}`);
    console.log(`  ${item.url}`);
});

Response formats are compatible for easy migration.

The Verdict

Choose Keiro if you want:

• 13x cost reduction

• More API endpoints

• Faster response times

• Free batch processing

• Cache discounts

Choose Tavily if you:

• Have deep Tavily integrations you can't change

• Don't care about costs

For 99% of use cases, Keiro is the better choice.

Get Started

Ready to save thousands on your AI infrastructure?

1. Sign up for free

2. Get your API key

3. Start making requests

No credit card required. See why developers are switching.