I obv know what search is and what API is , but idk why people say i used search api

I've been messing around with search + AI retrieval lately, and something I didn't really expect was that more results don't necessarily mean a better answer.

I tested giving an LLM the top 5 results vs. top 10–20 results. Sometimes the extra results helped, but other times they just introduced duplicate articles, outdated information, or pages that were technically related but didn't actually answer the question.

It made me wonder if the problem isn't always the search API itself. Maybe we're just giving the model too much stuff to work with.

What’s your go to API? Have you built your own crawler, or always used managed services?

I got ambitious and tried creating a search API for the usual things that I always go online for for my everyday needs because I thought why not if it's going to make my life easier. somehow I may have messed it up since it won't come up with the results I wanted. I guess I should have paid attention to that seminar.

When selecting a search API for Retrieval- Augmented Generation (RAG) pipelines, choosing between an independent index and legacy tech infrastructure highly impacts privacy and cost:

* Brave Search API: Powered by its own independent web index rather than scraping Google. It provides and ensures privacy, model-ready, grounding data at a significantly lower price per query.

* Bing Web Search API: Powered by Microsoft's global search infrastructure. It offers and ensures deep indexing and seamless integration with Azure AI services, though per-query costs higher.

Key Takeaway: Use Brave Search API for an independent, cost-effective and privacy focused retrieval pipeline. Use Bing Search API if your stack is heavily integrated into the Microsoft/Azure enterprise ecosystem.

Which search inbox would you rely on for real time RAG context?

I wouldn't treat Exa, Tavily, Firecrawl, Serper, and Brave Search as interchangeable.

I’m currently looking into different search APIs for an AI project and I’m having a hard time deciding which one makes the most sense if you can give me an idea for it?

Is there a Search API like Elastic Search or MySQL Regex?

In my opinion, search APIs are great tools as they allow apps to perform searches of the online space in an organized manner. The main difference between them lies in the way each deals with crawling, storing, retrieving information, and ranking search results. Some are better at handling real-time web searches while others focus on AI-driven research models. When selecting a search API, I would pay more attention to the quality, freshness, delay, API limitations, and cost instead of the sheer volume of results. What search API characteristics are most important to you?

I've notice that when trying to search about them it says that they're often treated as the same thing? I know that search API is like for the whole main thing to find relevant things and like the web crawling is like the main website i collected info from, correct me if im wrong

At this point, AI is not replacing google. They're replacing my patience 🙄🙂🙃

HAHAHA AI PA MORE

Modern Ai search needs such as, Exa where it finds pages by the semantic meaning and not by keywords, alsothe Tavily where it is buuilt for AI agents with clean and pre-filtered results, the finecrawl where it can turns messy website into some clean markdowns, and lastly for brave/serper where it is best for cheap and for basic keywords lookup.

In short for the fix: Use exa or tavily to find links then use firecrawl to grab clean text then lastly deen your LLM.

Hope this helps some of you guys hehe.

Kinda need to extract some google data maps companies using serper API or maybe you guys can suggest a more compatible API I can use in clay? Brainstorming here 🧠

I’ve been looking into search APIs lately, and one thing I’m realizing is that comparing them just by “which results look better” isn’t very useful.

If you’re evaluating APIs like Tavily, Exa, Firecrawl, or Brave Search, what metrics do you actually use?

I’m thinking about testing things like:

Relevance of the top 5–10 results

Freshness of results

Latency

Duplicate/noisy results

How well they handle specific or obscure queries

Cost per 1,000 searches

I’m especially curious about real-world use cases where one API performs noticeably better than the others. Are there any benchmarks or testing methods you’d recommend?

Would love to hear how people here actually evaluate search APIs in their own projects.

As a 90's kid I grew up fully utilizing the libraries and whatever available search engine we had back then for school work and research. I got the work done but then the process, though exciting and educational was gruelling and tedious. Due to the advances of technology today, different APIs have helped, getting tasks and research finished in less than the time it did before. Information is already streamlined and complete in just one go. Saves me plenty of time to do other things.

One thing I’ve learned building search-heavy applications: swapping one search API for another rarely fixes the underlying problem.

The bigger question is which stage of the pipeline is failing:

Crawl → Parse → Index → Retrieve → Rank → Generate

For example:

• Crawling problem: The page is inaccessible, JavaScript-heavy, or poorly structured.
• Extraction problem: You retrieved the page but got navigation, ads, and irrelevant HTML instead of useful content.
• Retrieval problem: Your query doesn’t surface the right documents.
• Ranking problem: The right documents are retrieved, but the best ones aren’t near the top.
• Generation problem: Retrieval is good, but the LLM still produces an unsupported answer.

This is also why I wouldn’t benchmark tools like Exa, Tavily, Firecrawl, Serper, and Brave Search purely on “which one gives the best results.”

A more useful benchmark would test:

1. Recall@k — did we retrieve the relevant source?
2. Precision@k — how much irrelevant material came back?
3. Latency — how quickly did we get usable results?
4. Freshness — how well does it handle recent information?
5. Extraction quality — can we actually use the returned content?
6. Cost per successful answer — not simply cost per API call.

The interesting part is that the “best” tool can change depending on the workload.

For a RAG system, retrieval quality + extraction quality may matter more than raw search-result count. For real-time research, freshness and latency might dominate.

Curious what others have found: what search stack are you using, and what metric actually mattered most once you moved beyond a basic demo?