SERP API vs AI Search API: Differences and How to Choose
SERP APIs return a copy of a Google results page; AI search APIs return ranked, LLM-ready text. Compare output, use cases and cost model, and when to skip both.
Both kinds of product answer "search the web from code," but they return different things and solve different problems. Mixing them up is the most common mistake when adding search to an app or an agent.
What each returns
SERP API (SerpAPI, Serper and similar) scrapes a search engine results page and hands it back as structured data: organic results, ads, maps, "People also ask", knowledge panels, related searches, and each result's position. The point is to see what a search engine shows.
AI search API (Tavily, Exa, Parallel, Perplexity Search API and similar) returns ranked results plus cleaned text excerpts, sometimes with extracted page content or a short generated answer. The point is to give a language model material it can read and cite.
Side by side
| SERP API | AI search API | |
|---|---|---|
| Main output | Structured copy of a results page | Ranked excerpts or page text for prompts |
| Includes ads, maps, panels | Yes | No |
| Result position (ranking data) | Yes | Not the goal |
| Ready to paste into a prompt | Needs more processing | Yes |
| Typical users | SEO tools, rank trackers, price monitors | Agents, RAG, chat assistants |
| Cost model | Often cheap per request, but you fetch pages yourself | Usually higher per request, content included |
How to choose
- Tracking rankings, ads or local packs → SERP API. An AI search API does not report positions.
- Answering questions with fresh, cited information → AI search API, or a model that searches for you.
- Need both (for example, an SEO assistant that also summarizes competitors) → use a SERP API for the ranking data and a language model with search for the summaries.
The third option: let the model request the search
If your goal is only "answer with current information and cite sources," you may not need to run a search vendor at all. On BazaarLink, adding :online to the model name makes the request search first, then answer with url_citation annotations:
{
"model": "openai/gpt-6-luna:online",
"messages": [{"role": "user", "content": "Compare the current free tiers of three vector databases"}]
}
You can control depth (low, medium, high, fast), topic, time range, country and language, and searches are billed per search with failed ones not charged. See adding web search with :online.
A quick decision rule
Ask what the consumer of the results is. If it is a person or a report looking at rankings, choose a SERP API. If it is a model that has to read and cite, choose an AI search API or a request-level search option.
For the four tools and pricing, see Tavily-style AI search API: Search, Extract, Crawl & Map.
TWD billing · Taiwan invoices · leading AI models · OpenAI-compatible API