r/SearchAPIs

Many people think all search APIs work the same, but they don’t.

•	Serper – Good for fast Google-style search.

•	Brave Search – Uses its own search results.

•	Tavily – Built for AI apps.

•	Exa – Finds pages by meaning, not just keywords.

•	Firecrawl – Gets clean content from web pages (it’s not a search engine).

A simple AI workflow:

Search → Get page content → Save it → Find the best answer

Using the right tool for each step can give better results than using just one tool for everything.

Which search API do you use most?

https://preview.redd.it/a73u9gzdoyeh1.png?width=1186&format=png&auto=webp&s=18432d5b398f5892e9426857ad5a426e0067c4cb

If you've ever built an AI app or RAG pipeline, you know the pain: you ask a question, and your AI gives a totally confident, completely wrong answer. Half the time, it’s not even the AI’s fault—it’s because the search tool you gave it returned garbage, popup text, or empty code.

Your AI is only as smart as the data you feed it. But choosing a search API right now is confusing because everyone claims to have "the best web fetcher."

I spent the last few weeks testing five popular search tools (Exa, Tavily, Firecrawl, Serper, and Brave Search) on real, messy web pages.

Here is the honest breakdown of what actually works and when to use each.

Quick Breakdown
Firecrawl: Best for turning messy websites into clean text. If a site relies heavily on JavaScript or complex layouts, Firecrawl opens it up and turns it into clean, readable Markdown that won't waste your AI tokens.

Tavily: Best for AI agents that need fast answers. Instead of just giving you a list of links, Tavily searches the web, reads the pages, and hands your AI a neat summary of the facts in one step.

Exa: Best for finding content by meaning, not just keywords. Traditional search looks for exact words. Exa looks for concepts. If you search for "tools that help developers organize code," it understands what you mean even if those exact words aren't on the page.

Serper: Best for raw Google speed. If you just want quick Google search results sent to your code fast and cheap, Serper is hard to beat. (Keep in mind: it gives you brief snippets, not full web pages).

Brave Search: Best for budget and privacy. Brave uses its own index instead of relying on Google or Bing. It’s fast, reliable, affordable, and great for standard web searches.

The 3 Golden Rules I Learned the Hard Way:

1. Combine two tools if you have to: Use a fast, cheap tool like Serper or Brave to find the top 3 links, then use Firecrawl to actually read those specific pages.

2. Clean up before sending to the AI: Never send raw HTML code directly to an LLM. It wastes money, fills up your prompt limit, and confuses the model.

3. Plan for broken pages: Websites go down or block bots all the time. Always write a simple backup rule in your code in case a page fails to load.

What’s your current setup for getting web data into your projects? Are you using one tool for everything or mixing them up?

I recently started comparing a few search APIs while experimenting with a simple AI assistant. My goal was to find which service provided the most useful results for retrieval rather than just traditional web search.
One thing I noticed was that Tavily often returned more focused results for my prompts, while Brave Search generally surfaced a broader range of web pages. I also found that the developer experience differed between services, especially when it came to reading the documentation and getting started with the APIs.
I’m still experimenting and haven’t reached any final conclusions, but it’s been interesting to see how each service has its own strengths depending on the use case.
For those who have used these APIs in production, what differences have you noticed in terms of relevance, latency, or overall developer experience?

I'm evaluating search APIs for an AI agent that needs reliable, real-time web retrieval instead of relying only on vector databases. The biggest issue I've found is that benchmark scores don't always translate to better retrieval quality in production.

My priorities are:

• High-quality search results for technical topics
• Fast response times (under 500 ms if possible)
• Predictable rate limits
• Transparent pricing at high request volumes
• Easy integration into an existing RAG pipeline

I've compared Exa, Tavily, Brave Search API, SerpAPI, and Bing Search, but they all seem to have different strengths. Some focus on semantic search, while others provide more traditional SERP results. It's difficult to tell which one consistently returns the most useful documents for downstream reranking and LLM generation.

For teams running AI agents or production RAG systems:

• Which search API has given you the best retrieval quality?
• Did you notice a significant improvement after adding a reranker, or was the search provider itself the biggest factor?
• Is it worth combining multiple search providers, or does the added complexity outweigh the benefits?
• At what scale did pricing become a deciding factor instead of retrieval quality?

I'm looking for real-world experiences rather than benchmark comparisons, especially from teams handling thousands of searches per day.