Search API for LLM — Common Questions Answered

LLMs are powerful, but they're only as good as the context you give them. A search API bridges the gap between a model's training data and what's happening right now — current prices, recent news, live documentation. This FAQ answers the most common questions developers have about choosing and integrating a search API for LLM applications.

Key Takeaways

• Search APIs give LLMs access to real-time information they can't get from training data alone
• Key factors to evaluate: latency, result quality, pricing, and ease of integration
• SearchHive offers search, scraping, and research in one API — more complete than search-only providers
• RAG (Retrieval Augmented Generation) is the primary pattern — search retrieves context, the LLM generates answers
• Most providers offer free tiers — test before committing

Q: Why do LLMs need a search API?

LLMs have a knowledge cutoff. Even models with recent training data can't tell you what a competitor's pricing page says today, what was in this morning's news, or what the current API documentation looks like.

A search API solves this by:

• Providing real-time context — current data injected into the LLM prompt
• Reducing hallucinations — grounding responses in actual web sources
• Enabling research tasks — the LLM can search, read, and synthesize information
• Supporting citations — every claim can reference a real source

Q: What should I look for in a search API for LLM applications?

The key evaluation criteria:

Result quality — Are results relevant to your domain? Do they include the information your LLM needs?

Latency — Sub-200ms for interactive chat, up to 5 seconds for background research

Content extraction — Does the API return just links, or actual page content? Clean text extraction matters for LLM context windows

Structured output — free JSON formatter responses with title, URL, snippet, and metadata make parsing easier

Pricing — LLM apps can burn through search credits fast. Calculate your per-query cost

Rate limits — Sufficient headroom for your expected traffic

Q: How does SearchHive compare to other search APIs?

SearchHive stands out because it combines three capabilities:

SearchHive's advantage is completeness. Instead of chaining three separate APIs (search + scraping + summarization), you get everything from one platform with one API key and one credit pool.

import requests

API_KEY = "your-searchhive-key"

# Step 1: Search for relevant sources
search_resp = requests.get(
    "https://api.searchhive.dev/v1/swiftsearch",
    headers={"Authorization": f"Bearer {API_KEY}"},
    params={"query": "Python web scraping best practices 2026", "limit": 5}
)

results = search_resp.json().get("results", [])

# Step 2: Extract content from top results
contexts = []
for r in results[:3]:
    scrape_resp = requests.post(
        "https://api.searchhive.dev/v1/scrapeforge",
        headers={"Authorization": f"Bearer {API_KEY}"},
        json={"url": r["url"], "render_js": True}
    )
    contexts.append(scrape_resp.json().get("content", ""))

# Step 3: Build LLM context
context_text = "\n\n---\n\n".join(contexts)
# Feed context_text into your LLM prompt

Q: What's the best approach for RAG with a search API?

RAG (Retrieval Augmented Generation) is the standard pattern:

1. Retrieve — Use the search API to find relevant documents/pages
2. Augment — Extract content from those pages and add it to the LLM context
3. Generate — The LLM generates a response grounded in the retrieved content

Implementation tips:

• Limit context — Don't dump everything into the prompt. Use snippets or summaries for top results, full content for the top 1-2
• Include metadata — URLs and titles help the LLM cite sources
• Re-rank results — If the search API supports it, use relevance scoring to prioritize
• Handle errors — Search APIs fail. Have fallbacks and graceful degradation

Q: How much does it cost to add search to an LLM app?

It depends on your usage pattern:

SearchHive's credit system gives you the best economics at every scale. The Builder plan ($49/month, 100K credits) covers search, scraping, AND research — while competitors charge separately for each capability.

Q: Which search API has the lowest latency?

For real-time chat applications, latency matters:

• SearchHive SwiftSearch: Sub-200ms for standard queries
• SerpAPI: 1-3 seconds (depends on Google SERP complexity)
• Tavily: 500ms-2s
• Exa: 180ms-1s (configurable)

If you're building an interactive chatbot where users wait for responses, SwiftSearch and Exa offer the fastest options. For background research tasks where latency doesn't matter, any provider works.

Q: Can I use multiple search APIs together?

Yes, and some advanced implementations do. Common patterns:

• Primary + fallback — Use SearchHive as primary, fall back to SerpAPI if it's down
• Domain-specific routing — Use SwiftSearch for general queries, Exa for semantic/conceptual queries
• Ensemble ranking — Query multiple APIs and merge/rank results

For most applications, one good search API is sufficient. The complexity of managing multiple providers rarely justifies the marginal quality improvement.

Q: How do I handle rate limits and errors?

Best practices:

• Implement exponential backoff — Retry failed requests with increasing delays (1s, 2s, 4s, 8s)
• Cache results — Don't re-search for the same query within a short window
• Use async requests — For batch operations, use asyncio or concurrent threads
• Monitor credit usage — Track consumption against your plan limits
• Graceful degradation — If search fails, let the LLM respond with its training knowledge and note that live data wasn't available

# SearchHive usage monitoring
import requests

resp = requests.get(
    "https://api.searchhive.dev/v1/account/usage",
    headers={"Authorization": f"Bearer {API_KEY}"}
)
usage = resp.json()

if usage["credits_remaining"] < 100:
    print(f"WARNING: Low credits ({usage['credits_remaining']} remaining)")

Q: Do I need a search API if my LLM has web browsing built in?

Some LLMs offer built-in web browsing (ChatGPT with browsing, Claude with tool use). But for programmatic applications, a dedicated search API gives you:

• Control — You decide what to search, how many results, and what to extract
• Consistency — API responses have a stable schema; browser-based approaches vary
• Cost efficiency — API calls are cheaper than LLM tokens for the same retrieval
• Reliability — APIs don't get distracted by ads, popups, or navigation

Built-in browsing is fine for demos and interactive use. Production applications need dedicated search APIs.

Summary

A search API is essential for any LLM application that needs real-time, accurate information. Evaluate based on result quality, latency, pricing, and the breadth of capabilities you need.

SearchHive offers the most complete package — web search, scraping, and deep research from one API with the best per-query pricing in the market. Start with 500 free credits (no credit card required) and see how it fits your stack. Check the docs for integration guides.

Related: /compare/serpapi | /compare/tavily | /compare/exa