You’re a technical leader or developer building next-gen AI applications, SEO tools, or robust RAG pipelines. Your project’s success hinges on reliable, real-time access to search engine results. However, Google Search data is notoriously expensive and often locked behind opaque, use-it-or-lose-it subscription models. Many teams allocate significant monthly budgets to SERP API providers, only to find themselves underutilizing credits or facing unpredictable costs.
This guide provides a definitive, data-driven analysis of SERP API pricing in 2026, cutting through the marketing noise to reveal the true Total Cost of Ownership (TCO). We’ll compare major players like SerpApi, Zenserp, and Google’s official offerings against the disruptive, pay-as-you-go model of SearchCans. By the end, you’ll understand how to significantly reduce your API spend while enhancing your data infrastructure for AI and SEO.
Here’s what we’ll cover:
- The evolving landscape of SERP API pricing and the shift from traditional models.
- A deep dive into the hidden costs and common pitfalls of established providers.
- A transparent comparison of SearchCans’ highly competitive, credit-based pricing model.
- Practical Python examples for integrating a cost-optimized SERP API and Reader API solution.
- Strategies to calculate your true TCO and make an informed build-vs-buy decision.
Let’s optimize your API budget.
The Evolving Landscape of SERP API Pricing in 2026
The demand for real-time web data has skyrocketed with the proliferation of AI Agents, advanced RAG pipelines, and sophisticated SEO automation tools. Yet, many legacy SERP API providers continue to offer pricing structures that penalize flexible usage and prioritize recurring monthly revenue over developer value.
Disconnect Between Demand and Supply
The core issue is a fundamental mismatch. Modern AI development thrives on agile iteration and unpredictable data needs. Legacy SERP API providers, however, often rely on subscription-based models with high monthly minimums and credit expiry. This creates significant budget strain and friction for innovation. In our benchmarks across various projects, we observed that developers frequently overpay by 30-50% due to unused, expired credits.
The Rise of “Pay-As-You-Go” & Value-Driven Models
This market inefficiency has paved the way for new entrants focusing on transparent, pay-as-you-go pricing. The goal is simple: allow developers to purchase credits once, use them as needed, and avoid the “use it or lose it” anxiety. This model significantly aligns API costs with actual consumption, a critical factor for startups and large enterprises alike focused on AI cost optimization.
Decoding Traditional SERP API Pricing: Hidden Costs & Pitfalls
Many developers fall into the trap of looking solely at the “price per 1,000 requests” without considering the broader implications of the billing model. Here’s what you need to scrutinize.
The Subscription Model Trap: Credit Expiry
The most significant pain point for developers is the monthly subscription with credit expiry. Providers like SerpApi and Zenserp, while offering extensive features, often bundle a fixed number of requests into a monthly fee. You effectively lose any unused credits at the end of the billing cycle. This forces developers to either over-subscribe to a higher plan or feel pressured to “burn” credits on non-essential tasks, leading to unnecessary expenditure.
Ambiguous “Request” Definitions
Not all “requests” are created equal. Some providers implement “per-page billing,” where fetching 100 results (e.g., 10 pages of 10 results each) counts as 10 separate requests. Other, more developer-friendly APIs charge a flat rate per query, regardless of the number of results returned (up to their maximum limit, often 100 results per page). Always clarify how a provider defines a “search” or “request” to avoid unexpected charges.
The True Cost of DIY Web Scraping
For those considering a “build vs. buy” approach, the allure of DIY web scraping can be deceptive. While seemingly “free,” the hidden costs are substantial and often underestimated.
Proxy Management
Acquiring, rotating, and maintaining reliable proxy pools to avoid IP bans and CAPTCHAs is a continuous, costly effort, easily running $50-100/month.
CAPTCHA & Anti-Bot Bypassing
Implementing sophisticated logic to solve CAPTCHAs or bypass advanced anti-bot measures requires significant developer time ($100/hr is a conservative estimate) and specialized tools.
Infrastructure & Maintenance
Running headless browsers (like Puppeteer for Node.js Google search scraping) or distributed scraping infrastructure incurs server costs and constant maintenance, especially when rate limits kill scrapers.
Pro Tip: When evaluating a “build vs. buy” scenario for SERP API access, always calculate the Total Cost of Ownership (TCO). This includes not only the direct API cost but also the indirect costs of developer salaries, infrastructure, maintenance, and the opportunity cost of resources diverted from core product development. Our experience supporting enterprises processing billions of requests shows that API solutions are almost always more cost-effective at scale.
Deep Dive: Competitor SERP API Pricing Landscape (2026)
Let’s examine the pricing models of some prominent SERP API providers based on their published 2026 plans.
SerpApi: The Feature-Rich, Premium Option
SerpApi offers a robust set of features, including broad search engine coverage and detailed structured JSON output. However, this comes at a premium price point.
SerpApi Starter Plan Breakdown
| Metric | Detail |
|---|---|
| Price | $25/month |
| Searches Included | 1,000 |
| Cost per 1k Requests | $25.00 |
| Billing Model | Monthly Subscription |
| Credit Rollover | No (resets monthly) |
For an AI agent requiring 50,000 searches per month, this translates to $1,250 monthly. The lack of credit rollover means any unused searches are forfeited, adding to the effective cost.
Zenserp: The “Affordable” Subscription
Zenserp positions itself as a more budget-friendly alternative to SerpApi, but still relies on a subscription model with monthly expiry.
Zenserp Small Plan Breakdown
| Metric | Detail |
|---|---|
| Price | $29/month |
| Searches Included | 5,000 |
| Cost per 1k Requests | $5.80 |
| Billing Model | Monthly Subscription |
| Credit Rollover | No (resets monthly) |
While cheaper per 1k than SerpApi, the $29 minimum monthly commitment for 5,000 searches means if you only use 1,000 searches, your effective cost is $29 per 1k. This “use it or lose it” model creates inefficiencies for fluctuating usage patterns.
Google Custom Search JSON API: The “Free Tier” Lure
Google’s official API for programmatic search access comes with significant limitations, making it unsuitable for most professional applications.
Google API Limitations
- Cost: ~$5 per 1,000 queries (after a very limited free tier).
- Query Limits: Maximum of 10,000 queries per day.
- Data Quality: Lacks rich SERP features and detailed organic ranking data crucial for SEO.
- Setup: Requires setting up a Custom Search Engine, adding an extra layer of configuration.
Verdict: While seemingly accessible, it’s primarily designed for internal site search or very low-volume, non-commercial projects.
SearchCans: Disrupting SERP API Pricing with a Value-First Model
At SearchCans, we built our platform specifically to address the pain points of traditional SERP API pricing. Our core philosophy: you should only pay for what you use, and your credits should be valid for a generous period. We offer a simple, pay-as-you-go credit system with no monthly subscriptions and 6-month credit validity.
Transparent Pricing Breakdown
All plans provide full access to both our SERP API and Reader API (URL to Markdown), creating a unified data acquisition platform.
SearchCans Pricing Tiers
| Plan Name | Price (USD) | Total Credits | Cost per 1k Requests | Best For |
|---|---|---|---|---|
| Standard | $18.00 | 20,000 | $0.90 | Developers, MVP Testing |
| Starter | $99.00 | 132,000 | $0.75 | Startups, Small Agents (Most Popular) |
| Pro | $597.00 | 995,000 | $0.60 | Growth Stage, SEO Tools |
| Ultimate | $1,680.00 | 3,000,000 | $0.56 | Enterprise, Large Scale AI |
New users also receive 100 free credits immediately upon registration to test the platform in our API Playground.
The Dual-Engine Advantage: SERP + Reader API
Beyond just competitive SERP API pricing, SearchCans offers a unique dual-engine platform that significantly reduces the complexity and cost of data pipelines for AI.
Traditional Data Pipeline
A typical workflow for an AI agent requiring web content involves two separate steps, often using two different API providers:
- Search: Use a SERP API to find relevant URLs.
- Read/Extract: Use a separate web scraping or URL to Markdown API (like Jina Reader or Firecrawl) to get clean content from those URLs.
This means managing multiple API keys, integration points, and billing cycles, leading to increased overhead and cost.
SearchCans Unified Data Pipeline
SearchCans integrates both capabilities into a single platform with one API key and unified billing. Our Reader API efficiently converts messy HTML/JS pages into clean, LLM-ready Markdown, perfect for RAG optimization.
This integrated approach translates to massive cost savings and reduced development complexity. When we analyze the best Jina Reader and Firecrawl alternatives, SearchCans consistently emerges as 10x cheaper for comprehensive web content extraction.
Practical Implementation: Cost-Optimized SERP Data Fetching with Python
Let’s illustrate how to integrate SearchCans for efficient SERP data fetching. This example focuses on batch keyword searching, a common task for SEO rank trackers or market intelligence platforms.
Setting Up Your Environment
First, ensure you have requests installed:
pip install requests
Next, prepare a keywords.txt file with one keyword per line:
# keywords.txt
latest AI trends
SERP API pricing 2026
SearchCans reviews
Python Script for Batch SERP Data Collection
This script fetches Google search results for a list of keywords and saves them, demonstrating robust error handling and retries—features typically associated with higher-priced APIs.
# serp_batch_search.py
import requests
import json
import time
import os
from datetime import datetime
# ======= Configuration Area =======
USER_KEY = "YOUR_SEARCHCANS_API_KEY" # Replace with your SearchCans API Key
KEYWORDS_FILE = "keywords.txt" # File containing keywords (one per line)
OUTPUT_DIR = "serp_results" # Directory to save results
SEARCH_ENGINE = "google" # "google" or "bing"
MAX_RETRIES = 3 # Number of retries on failure
# ================================
class SearchCansSERPClient:
def __init__(self, api_key):
self.api_url = "https://www.searchcans.com/api/search"
self.api_key = api_key
self.completed = 0
self.failed = 0
self.total = 0
def load_keywords(self):
"""Loads keywords from a specified file."""
if not os.path.exists(KEYWORDS_FILE):
print(f"❌ Error: Keyword file '{KEYWORDS_FILE}' not found.")
print(f"Please create '{KEYWORDS_FILE}' with one keyword per line.")
return []
keywords = []
with open(KEYWORDS_FILE, 'r', encoding='utf-8') as f:
for line in f:
keyword = line.strip()
if keyword and not keyword.startswith('#'):
keywords.append(keyword)
print(f"📄 Loaded {len(keywords)} keywords from '{KEYWORDS_FILE}'.")
return keywords
def search_keyword(self, keyword, page=1):
"""
Performs a search for a single keyword.
Args:
keyword (str): The search query.
page (int): The page number for results (default 1).
Returns:
dict: API response data, or None if failed.
"""
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
payload = {
"s": keyword,
"t": SEARCH_ENGINE,
"d": 10000, # 10 second timeout for API processing
"p": page
}
try:
print(f" Searching: '{keyword}' (page {page})...", end=" ")
response = requests.post(
self.api_url,
headers=headers,
json=payload,
timeout=15
)
result = response.json()
if result.get("code") == 0:
data = result.get("data", [])
print(f"✅ Success ({len(data)} results)")
return result
else:
msg = result.get("msg", "Unknown error")
print(f"❌ Failed: {msg}")
return None
except requests.exceptions.Timeout:
print(f"❌ Request timed out.")
return None
except Exception as e:
print(f"❌ Error: {str(e)}")
return None
def search_with_retry(self, keyword, page=1):
"""
Performs a search with retry mechanism.
Args:
keyword (str): The search query.
page (int): The page number.
Returns:
dict: Search results, or None if all retries fail.
"""
for attempt in range(MAX_RETRIES):
if attempt > 0:
print(f" 🔄 Retrying {attempt}/{MAX_RETRIES-1} for '{keyword}'...")
time.sleep(2)
result = self.search_keyword(keyword, page)
if result:
return result
print(f" ❌ Keyword '{keyword}' failed after {MAX_RETRIES} attempts.")
return None
def save_result(self, keyword, result, output_dir):
"""
Saves the search result to a JSON file and appends to a JSONL aggregate file.
Args:
keyword (str): The search query.
result (dict): The API response.
output_dir (str): The output directory.
"""
safe_filename = "".join(c if c.isalnum() or c in (' ', '-', '_') else '_' for c in keyword)
safe_filename = safe_filename[:50]
json_file = os.path.join(output_dir, f"{safe_filename}.json")
with open(json_file, 'w', encoding='utf-8') as f:
json.dump(result, f, ensure_ascii=False, indent=2)
jsonl_file = os.path.join(output_dir, "all_results.jsonl")
with open(jsonl_file, 'a', encoding='utf-8') as f:
record = {
"keyword": keyword,
"timestamp": datetime.now().isoformat(),
"result": result
}
f.write(json.dumps(record, ensure_ascii=False) + "\n")
print(f" 💾 Saved: {safe_filename}.json")
def extract_urls(self, result):
"""Extracts a list of URLs from the search results."""
if not result or result.get("code") != 0:
return []
data = result.get("data", [])
urls = [item.get("url", "") for item in data if item.get("url")]
return urls
def run(self):
"""Main execution function for batch searching."""
print("=" * 60)
print("🚀 SearchCans SERP API Batch Search Tool")
print("=" * 60)
keywords = self.load_keywords()
if not keywords:
return
self.total = len(keywords)
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
output_dir = f"{OUTPUT_DIR}_{timestamp}"
os.makedirs(output_dir, exist_ok=True)
print(f"📂 Results will be saved to: {output_dir}/")
print(f"🔍 Search Engine: {SEARCH_ENGINE}")
print("-" * 60)
for index, keyword in enumerate(keywords, 1):
print(f"\n[{index}/{self.total}] Keyword: {keyword}")
result = self.search_with_retry(keyword)
if result:
self.save_result(keyword, result, output_dir)
urls = self.extract_urls(result)
if urls:
print(f" 🔗 Found {len(urls)} links:")
for i, url in enumerate(urls[:3], 1):
print(f" {i}. {url[:80]}...")
if len(urls) > 3:
print(f" ... and {len(urls)-3} more links.")
self.completed += 1
else:
self.failed += 1
if index < self.total:
time.sleep(0.5)
print("\n" + "=" * 60)
print("📊 Execution Statistics")
print("=" * 60)
print(f"Total Keywords: {self.total}")
print(f"Successful: {self.completed} ✅")
print(f"Failed: {self.failed} ❌")
print(f"Success Rate: {(self.completed/self.total*100):.1f}%")
print(f"\n📁 Results saved to: {output_dir}/")
def main():
"""Main program entry point."""
if USER_KEY == "YOUR_SEARCHCANS_API_KEY":
print("❌ Please configure your SearchCans API Key in the script!")
return
client = SearchCansSERPClient(USER_KEY)
client.run()
print("\n✅ Task completed!")
if __name__ == "__main__":
main()
Python Script for URL Content Extraction (Reader API)
Once you have the URLs from the SERP results, you can use the SearchCans Reader API to extract clean, LLM-ready content.
# url_to_markdown.py
import requests
import os
import time
import re
import json
from datetime import datetime
# ================= Configuration Area =================
USER_KEY = "YOUR_SEARCHCANS_API_KEY"
INPUT_FILENAME = "urls_from_serp.txt"
API_URL = "https://www.searchcans.com/api/url"
WAIT_TIME = 3000 # Wait time for URL to load (ms)
TIMEOUT = 30000 # Max API waiting time (ms)
USE_BROWSER = True # Use full browser for complete content
# ====================================================
def sanitize_filename(url, ext="txt"):
"""Converts a URL into a safe filename."""
name = re.sub(r'^https?://', '', url)
name = re.sub(r'[\\/*?:"<>|]', '_', name)
return name[:100] + f".{ext}"
def extract_urls_from_file(filepath):
"""Extracts URLs from a .txt or .md file."""
urls = []
if not os.path.exists(filepath):
print(f"❌ Error: File '{filepath}' not found.")
return []
with open(filepath, 'r', encoding='utf-8') as f:
content = f.read()
md_links = re.findall(r'\[.*?\]\((http.*?)\)', content)
if md_links:
print(f"📄 Detected Markdown format, extracted {len(md_links)} links.")
return md_links
lines = content.split('\n')
for line in lines:
line = line.strip()
if line.startswith("http"):
urls.append(line)
print(f"📄 Detected plain text format, extracted {len(urls)} links.")
return urls
def call_reader_api(target_url):
"""Calls the SearchCans Reader API."""
headers = {
"Authorization": f"Bearer {USER_KEY}",
"Content-Type": "application/json"
}
payload = {
"s": target_url,
"t": "url",
"w": WAIT_TIME,
"d": TIMEOUT,
"b": USE_BROWSER
}
try:
response = requests.post(API_URL, headers=headers, json=payload, timeout=35)
response_data = response.json()
return response_data
except requests.exceptions.Timeout:
return {"code": -1, "msg": "Request timed out, consider increasing TIMEOUT parameter."}
except requests.exceptions.RequestException as e:
return {"code": -1, "msg": f"Network request failed: {str(e)}"}
except Exception as e:
return {"code": -1, "msg": f"Unknown error: {str(e)}"}
def main():
print("🚀 Starting SearchCans Reader API Batch Extraction Task...")
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
output_dir = f"reader_results_{timestamp}"
os.makedirs(output_dir, exist_ok=True)
print(f"📂 Results will be saved in: ./{output_dir}/")
urls = extract_urls_from_file(INPUT_FILENAME)
if not urls:
print("⚠️ No URLs found to process. Exiting.")
return
total = len(urls)
success_count = 0
for index, url in enumerate(urls):
current_idx = index + 1
print(f"\n[{current_idx}/{total}] Fetching: {url}")
start_time = time.time()
result = call_reader_api(url)
duration = time.time() - start_time
if result.get("code") == 0:
data = result.get("data", "")
if isinstance(data, str):
try:
parsed_data = json.loads(data)
except json.JSONDecodeError:
parsed_data = {"markdown": data, "html": "", "title": "", "description": ""}
elif isinstance(data, dict):
parsed_data = data
else:
print(f"❌ Failed ({duration:.2f}s): Unsupported data type {type(data)}.")
continue
title = parsed_data.get("title", "")
description = parsed_data.get("description", "")
markdown = parsed_data.get("markdown", "")
html = parsed_data.get("html", "")
if not markdown and not html:
print(f"❌ Failed ({duration:.2f}s): Returned data is empty.")
continue
base_name = sanitize_filename(url, "")
if base_name.endswith("."):
base_name = base_name[:-1]
if markdown:
md_file = os.path.join(output_dir, base_name + ".md")
with open(md_file, 'w', encoding='utf-8') as f:
f.write(f"# {title}\n\n" if title else "")
f.write(f"> {description}\n\n" if description else "")
f.write(f"**Source:** {url}\n\n")
f.write("-" * 50 + "\n\n")
f.write(markdown)
print(f" 📄 Markdown: {base_name}.md ({len(markdown)} characters)")
if html:
html_file = os.path.join(output_dir, base_name + ".html")
with open(html_file, 'w', encoding='utf-8') as f:
f.write(html)
print(f" 🌐 HTML: {base_name}.html ({len(html)} characters)")
json_file = os.path.join(output_dir, base_name + ".json")
with open(json_file, 'w', encoding='utf-8') as f:
json.dump(parsed_data, f, ensure_ascii=False, indent=2)
print(f" 📦 JSON: {base_name}.json")
print(f"✅ Success ({duration:.2f}s)")
if title:
print(f" Title: {title[:80]}..." if len(title) > 80 else f" Title: {title}")
success_count += 1
else:
msg = result.get("msg", "Unknown error")
print(f"❌ Failed ({duration:.2f}s): {msg}")
time.sleep(0.5)
print("-" * 50)
print(f"🎉 Task completed! Total: {total}, Successful: {success_count}.")
print(f"📁 Check folder: {output_dir}")
if __name__ == "__main__":
main()
Pro Tip: While SearchCans is designed for high concurrency, it’s crucial to implement robust error handling and retry logic in your applications. This ensures resilience against transient network issues or unexpected API responses. Always build in exponential backoff for retries to avoid hammering the API during temporary outages. For scaling AI agents with rate limits, intelligent queuing and request pacing are key.
Comprehensive SERP API Pricing Comparison (2026)
To fully grasp the value proposition, let’s look at a detailed comparison of key providers for a scenario requiring ~20,000 SERP requests per month—a common volume for growing SEO tools or active AI research agents.
| Provider | Plan Name | Price (USD) | Searches Included | Cost per 1k Requests | Billing Cycle | Credit Expiry | Dual-Engine (SERP+Read) |
|---|---|---|---|---|---|---|---|
| SearchCans | Standard | $18.00 | 20,000 | $0.90 | One-time | 6 months | ✅ Yes (Integrated) |
| SerpApi | Starter | $75.00/month | 5,000 | $15.00 | Monthly | Yes | ❌ No |
| SerpApi | Developer | $75.00/month | 5,000 | $15.00 | Monthly | Yes | ❌ No |
| Zenserp | Small | $29.00/month | 5,000 | $5.80 | Monthly | Yes | ❌ No |
| Serper.dev | Starter | $10.00 | 10,000 | $1.00 | Top-up | Limited | ❌ No |
| Oxylabs | Starter | $49.00/month | ~36,000 | ~$1.35 | Monthly | Yes | ❌ No |
| Bright Data | Pay as you go | ~$0.75 | Varies | ~$0.75 | Top-up | No | ❌ No |
| Google Official | Pay-as-you-go | ~$5.00 | 10,000/day limit | ~$5.00 | Monthly | No | ❌ No |
Note: Prices are approximate and based on publicly available data as of January 2026. “Dual-Engine” refers to integrated SERP + URL to Markdown/HTML extraction capabilities within the same platform.
The data clearly demonstrates that SearchCans offers a significantly lower cost per 1,000 requests, especially when combined with its flexible, long-validity credit system. For the same 20,000 searches, you pay $18 once with SearchCans versus recurring monthly fees from competitors, potentially saving hundreds or thousands of dollars annually.
Frequently Asked Questions (FAQ)
What is SERP API and how does it benefit AI applications?
A SERP API provides programmatic access to search engine results pages (SERPs), returning structured data (JSON) instead of raw HTML. This benefits AI applications by feeding them real-time, verifiable information from the internet, preventing LLM hallucinations, and enabling them to perform tasks like competitive analysis, market research, or content generation based on current facts. It acts as an invisible bridge connecting AI to the internet.
Why is SearchCans significantly cheaper than alternatives like SerpApi or Zenserp?
SearchCans achieves lower pricing by optimizing its infrastructure for scale and efficiency, focusing on a pay-as-you-go credit model rather than forcing expensive monthly subscriptions. Our approach eliminates credit expiry and the overhead associated with unused recurring billing, passing those savings directly to developers. Additionally, integrating both SERP and Reader API functionalities under one roof further reduces TCO by streamlining data acquisition.
Does SearchCans support various search engines and SERP features?
Yes, SearchCans’ SERP API supports real-time search results from major engines like Google and Bing. Our structured JSON output includes various SERP features, such as organic listings, knowledge panels, related questions, and more, all formatted for easy consumption by LLMs and data analysis tools. This ensures your AI agents receive rich, comprehensive data.
Can I use SearchCans for Retrieval-Augmented Generation (RAG) pipelines?
Absolutely. SearchCans is perfectly designed for RAG optimization. Our SERP API provides the initial search results (URLs), and our powerful Reader API transforms those web pages into clean, LLM-ready Markdown. This ensures that the context provided to your LLM is high-quality, relevant, and free from web clutter, improving the accuracy and relevance of your AI’s generations. For more, see our guide on clean Markdown for RAG.
What are the credit validity and billing terms for SearchCans?
SearchCans operates on a pay-as-you-go credit system. Once purchased, your credits remain valid for 6 months, providing ample time for usage without the pressure of monthly expiry. There are no monthly subscriptions or recurring charges, giving you complete control over your spending. This flexible model is ideal for projects with variable data needs or long development cycles.
Conclusion: Take Control of Your SERP API Costs
The landscape of SERP API pricing has long been dominated by models that prioritize vendor revenue over developer value. However, the rise of AI-driven applications demands a more flexible, cost-efficient, and integrated approach to web data acquisition.
SearchCans delivers on this promise. By offering significantly lower costs per 1,000 requests, a flexible pay-as-you-go model with long credit validity, and a powerful dual-engine (SERP + Reader API) platform, we empower developers and CTOs to:
- Slash their SERP API expenses by up to 96% compared to traditional providers.
- Eliminate credit expiry anxiety and gain budget predictability.
- Streamline their data pipelines for AI agents and RAG systems with a single, integrated platform.
- Focus resources on innovation, not on managing proxies, CAPTCHAs, or complex billing.
It’s time to stop overpaying for essential web data. Take control of your costs and accelerate your AI and SEO projects.
Ready to experience the difference?
Sign up for your free trial and get 100 credits today!
Or, explore our API Playground to test SearchCans in action.