Search API for AI -- Common Questions Answered

If you are building an AI application that needs to search the web, you need a search API. But choosing the right one -- and integrating it correctly -- raises a lot of questions. This FAQ covers the most common ones, with pricing comparisons and code examples.

Key Takeaways

• Search APIs for AI are different from general search APIs -- AI apps need structured free JSON formatter, fast response times, and reliable parsing
• Pricing ranges from $0.0001/credit (SearchHive) to $0.025/search (SerpAPI) -- a 250x difference at scale
• Not all search APIs support the same engines -- Google, Bing, DuckDuckGo coverage varies by provider
• Rate limits matter more for AI than for traditional search -- AI agents often make many sequential queries
• SearchHive SwiftSearch provides multi-engine search at the lowest price point with 500 free credits

Q: What is a search API for AI and how is it different from a regular search API?

A search API for AI is a web service that returns search results in a structured format (JSON) that an AI agent or LLM can parse and reason over. The key differences from "regular" search APIs:

• Structured output: AI agents need clean JSON with title, URL, snippet, position -- not HTML
• Multiple engines: AI agents benefit from querying multiple search engines (Google, Bing, DuckDuckGo) for diverse results
• Low latency: Every API call adds latency to the agent's response, so sub-2-second response times matter
• High reliability: AI agents often run unattended, so consistent uptime and error handling are critical

Most "search APIs for AI" are the same underlying technology as general SERP APIs. The difference is in how you use them -- AI agents typically consume the data programmatically rather than displaying it to users.

Q: Which search API is cheapest for AI agents?

Here is a per-query cost comparison at common volume levels:

SearchHive is the cheapest at every volume level. At 100K searches, it costs $49 vs $725 for SerpAPI -- roughly 15x cheaper. The universal credit system ($0.0001/credit) means you pay the same rate regardless of which search engine you query.

Q: Can I use a free search API for AI?

Yes, several options exist:

• SearchHive: 500 free credits (enough for ~500 searches or a mix of search/scrape/extract)
• Serper.dev: 2,500 free queries on signup (one-time, not monthly)
• Tavily: 1,000 free credits per month
• Brave Search: $5 worth of free credits per month (~1,000 searches)
• DuckDuckGo (unofficial): Free but unreliable, no API guarantee, can break anytime

Free tiers are great for prototyping. For production, you will hit limits quickly. SearchHive's Starter plan ($9/month for 5K credits) is the cheapest production option.

Q: How do I integrate a search API with an LLM?

The standard pattern is: search the web, inject the results into the LLM's context, and let the model reason over them.

import requests
import openai

# Step 1: Search the web
def web_search(query):
    resp = requests.post(
        "https://api.searchhive.dev/v1/search",
        headers={"Authorization": "Bearer your_searchhive_key"},
        json={"query": query, "engine": "google", "num_results": 5}
    )
    results = resp.json().get("data", {}).get("organic", [])
    return results

# Step 2: Build context from search results
query = "Python web scraping best practices 2025"
results = web_search(query)

context = "\n".join([
    f"Title: {r['title']}\nURL: {r['url']}\nSnippet: {r.get('snippet', '')}"
    for r in results
])

# Step 3: Send to LLM with search context
response = openai.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "Answer questions based on the provided web search results."},
        {"role": "user", "content": f"Search results:\n{context}\n\nQuestion: {query}"}
    ]
)
print(response.choices[0].message.content)

This pattern is the foundation of Retrieval-Augmented Generation (RAG) for web data. SearchHive's SwiftSearch API returns results in under 2 seconds, keeping the total agent response time reasonable.

Q: Should I use a search API or a scraping API for AI?

Use both. They serve different purposes:

• Search API (SwiftSearch): Discover what exists. Find relevant URLs, get snippets, understand what information is available.
• Scraping API (ScrapeForge): Get the full content. Read the actual page, extract details, get data beyond what search snippets show.

The typical flow: search first to find relevant pages, then scrape the top results for full content. SearchHive provides both through a single API, so you don't need separate accounts or billing.

Q: What search engines can I access through an API?

SearchHive supports Google, Bing, and DuckDuckGo -- the widest engine coverage among affordable options. This matters for AI agents because different engines return different results, and cross-referencing improves accuracy.

Q: How do I handle rate limits with search APIs?

Different APIs handle rate limits differently:

import time
import requests

def search_with_retry(query, max_retries=3):
    for attempt in range(max_retries):
        try:
            resp = requests.post(
                "https://api.searchhive.dev/v1/search",
                headers={"Authorization": "Bearer your_key"},
                json={"query": query, "engine": "google"},
                timeout=10
            )
            if resp.status_code == 429:
                wait = 2 ** attempt
                print(f"Rate limited, waiting {wait}s...")
                time.sleep(wait)
                continue
            resp.raise_for_status()
            return resp.json()
        except requests.exceptions.Timeout:
            if attempt == max_retries - 1:
                raise
            time.sleep(1)
    raise Exception(f"Failed after {max_retries} retries")

SearchHive's rate limits scale with your plan. The Builder plan ($49/month) provides higher throughput than the Starter ($9/month). For high-volume use, the Unicorn plan ($199/month) offers dedicated throughput.

Q: Can I use a search API for real-time AI agents?

Yes, but latency matters. Here are typical response times:

• SearchHive SwiftSearch: 1-2 seconds per query
• Serper.dev: 1-2 seconds per query
• SerpAPI: 2-5 seconds per query (higher at "Ludicrous Speed" tiers)
• Tavily: 2-4 seconds per query
• Brave Search: 1-2 seconds per query

For real-time chatbots, keep total response time under 5 seconds. That means your search + LLM inference combined should be under 5 seconds. SearchHive's 1-2 second search time leaves 3-4 seconds for LLM inference, which is comfortable for most models.

Q: What happens when search APIs return no results?

Handle empty results gracefully in your AI pipeline:

results = web_search("obscure topic with no results")

if not results:
    # Fallback 1: Try a different search engine
    results = web_search_with_engine("obscure topic", engine="bing")

if not results:
    # Fallback 2: Broaden the query
    results = web_search("obscure topic general overview")

if not results:
    # Fallback 3: Tell the LLM to use training data
    context = "No web results found. Answer from your training data."

Always include a fallback path. AI agents that fail silently when search returns nothing provide a terrible user experience.

Summary

For AI applications that need web search, SearchHive SwiftSearch offers the best combination of price, engine coverage, and developer experience. The universal credit system means you can mix search, scraping, and extraction without managing separate billing.

Start with the free tier -- 500 credits, no credit card required. Test your integration, measure your costs, then scale to the Starter ($9/month) or Builder ($49/month) plan as your usage grows.

Related: /blog/complete-guide-to-ai-agent-web-access | /compare/serpapi | /compare/tavily