Google Scholar data can unlock a goldmine of academic insights.
By analyzing this data, you can identify trending research topics, track citation growth over time, evaluate the impact of a paper or author, discover new publications in a specific field, or even map collaboration networks between researchers and institutions. For universities, it helps benchmark academic performance.
For developers, it powers tools like reference managers, citation analyzers, and literature review platforms.
Simply put, Scholar data helps you stay ahead in the research game, whether you’re publishing, studying, or building.
A Google Scholar API allows you to programmatically access academic research data such as article titles, author names, abstracts, citation counts, publication dates, journals, and even related works — all without manually browsing through the website.
It’s a game-changer for researchers, developers, and anyone who needs large volumes of scholarly information. Whether you’re building an academic search tool, analyzing trends in research papers, or simply pulling citation metrics for a report, a Google Scholar API helps you automate that process efficiently and at scale.
In this article, we will test 3 of the best Google Scholar APIs and rank them based on their performances. This study will help you select the best option for your next project.
Criteria To Test These Google Scholar APIs
Analysis will be made based on these five attributes.
- Scalability– Number of researches that can be scraped.
- Pricing– Per request cost.
- Developer-friendly– How easy is it for developers to integrate the API?
- Speed– How fast an API responds.
- Stability– Uptime of APIs.
I will be using this Python code to test the APIs.
import requests
import time
import random
# List of search terms
research_terms = [
"cancer",
"biology",
"NLP Papers 2024",
"machine learning in healthcare",
"quantum computing applications"
]
# Replace with your actual API endpoint
# Make sure it includes {query} where the search term should be inserted
api_key="your-api-key"
base_url = "https://api.example.com/google_scholar"
#
total_requests = 10
success_count = 0
total_time = 0
for i in range(total_requests):
try:
search_term=random.choice(research_terms)
params = {
"engine": "google_scholar",
"q": search_term,
"api_key": api_key
}
start_time = time.time()
response = requests.get(base_url, params=params)
end_time = time.time()
request_time = end_time - start_time
total_time += request_time
if response.status_code == 200:
success_count += 1
print(f"Request {i+1}: '{search_term}' took {request_time:.2f}s | Status: {response.status_code}")
except Exception as e:
print(f"Request {i+1} with '{search_term}' failed due to: {str(e)}")
# Final Stats
average_time = total_time / total_requests
success_rate = (success_count / total_requests) * 100
print(f"\n🔍 Total Requests: {total_requests}")
print(f"✅ Successful: {success_count}")
print(f"⏱️ Average Time: {average_time:.2f} seconds")
print(f"📊 Success Rate: {success_rate:.2f}%")
Scrapingdog Google Scholar API
Google Scholar API offered by Scrapingdog can be used for scraping Google Scholar data at scale. API will return structured JSON data, which will have all the basic details from the title to the link of the research.
Details
- You will get 1000 free credits on sign-up. You can use these credits to test the API completely before upgrading.
- The cost per API call starts at $0.001 and decreases to below $0.000058 as usage volume increases.
- The documentation is very clear and provides ready-made code snippets. One can easily integrate the API by directly copying and pasting the code from the dashboard. We regularly post new articles and videos on YouTube.
- You can contact them through email or on-site chat support. They are available 24*7 and will reply to your query within seconds.
Testing the API
We got amazing results.
With a 100% success rate and an average response time of just 1.26 seconds, Scrapingdog’s Google Scholar API proves to be incredibly fast and reliable for scraping Scholar at scale.
SerpAPI
SerpAPI also offers Google Scholar API, and I am sure you already know about them. API will return a beautiful, structured JSON response.
Details
- When you sign up, you get 100 credits for free. You can test any API before going for a bigger pack.
- Pricing for one API call starts from $0.015(15x of Scrapingdog😲) and drops below $0.0075 with higher volume.
- The documentation is clear and concise. Any developer can integrate the API in no time.
- You can reach out to them through on-site chat support or through email. They have a great support team and will help you out with any query.
Testing the API
We got a 100% success rate with an average response time of 2.51 seconds.
SearchAPI
SearchAPI is another choice if you are looking to scrape Google Scholar.
Details
- They provide 100 free credits for testing the API.
- Per API, call cost will start from $0.004(4x of Scrapingdog😲) and drop below $0.002 with a higher volume.
- Any developer can easily integrate their APIs into their working environment.
- You can contact them through chat or email.
Testing the API
SearchAPI also provided a 100% success rate with an average speed of 3.59 seconds.
Conclusion
If you compare each API based on speed, the results will look like this.
Here, it looks like Scrapingdog and SerpAPI can both be used for scraping Scholar data at scale. Now, let’s compare them based on pricing.
Here, things look very different. If you consider the price, then Scrapingdog becomes a clear choice.
Additional Resources
