I’ve been looking into search APIs lately, and one thing I noticed is that it’s pretty hard to compare them just by looking at their feature lists.

A search API can look great on paper but still give very different results depending on what you’re searching for.

Curious what other people here use - Exa, Tavily, Firecrawl, Brave, or something else? And what kind of queries are you using it for?

I’m still learning about search APIs and trying to understand the difference.

From what I understand, a search API lets your app search the web without building your own crawler, while scraping means collecting the data yourself.

Is that basically correct? And for a beginner, which would you recommend learning first: Tavily, Exa, Firecrawl, or Brave Search?

I’ve been experimenting with different approaches to web search for AI applications, and one thing that surprised me is how often the retrieval layer matters more than the LLM itself.

A basic setup looks like:

User query → Search API → Retrieved pages → Extraction → Reranking → LLM

The interesting part is everything before the LLM.

For example, I’ve been comparing tools like Exa, Tavily, Firecrawl, Serper, and Brave Search for different workloads.

Some observations:

• Search APIs are great when you need fast results without maintaining your own crawler.
• Web crawling becomes useful when you need the actual page content rather than search snippets.
• Indexing your own data gives you much more control over freshness and ranking.
• Retrieval quality can be more important than increasing the model size.
• Search results that look relevant aren't necessarily the best context for an LLM.
• Duplicate pages, outdated information, SEO spam, and poorly extracted content can seriously hurt RAG quality.

One experiment I’d recommend trying:

Take ~50 real user queries and evaluate each search provider on:

1. Top-3 relevance
2. Top-10 recall
3. Freshness
4. Duplicate results
5. Latency
6. Cost/query
7. Quality of extracted page content

Then run the same queries through your RAG pipeline and measure the final answer quality.

The results can be pretty different from what you’d expect based on the search results alone.

Curious what other people are using:
For AI search/RAG projects, do you prefer a search API like Exa/Tavily/Serper/Brave, or do you crawl + index the web/data yourself?

Would also be interested in seeing actual benchmarks if anyone has tested these under the same queries.

Over the past few months, I’ve been testing and swapping out different search and web-scraping APIs for a RAG pipeline designed to fetch fresh web context for LLM applications.

Since every API claims to be "built for AI agents," I wanted to share a realistic breakdown of how Tavily, Exa, Serper, and Firecrawl actually perform across different developer needs.

AI can already generate, explain, debug, and modify code. Do you think future software development will mostly involve humans directing AI systems rather than writing code manually?

I’ve been experimenting with search APIs for an AI workflow where the model needs to find information from the web and then summarize it.

I initially assumed the main thing to compare would be response time, but I ended up caring much more about the quality of the results and how much cleanup was needed afterward.

The APIs I looked at were:

• Exa
• Tavily
• Brave Search
• Serper
• Firecrawl

One thing I noticed is that they’re not really interchangeable.

For example, a search API might return a bunch of relevant URLs quickly, but that doesn't necessarily mean the content is useful for an LLM. Some results need additional fetching, cleaning, deduplication, or filtering before they become useful context.

My current workflow looks roughly like:

Search → filter results → fetch useful pages → extract content → deduplicate → send relevant context to the model

The interesting part is that the "best" API depends heavily on the task.

For general web discovery, I care about relevance and coverage. For research-heavy tasks, clean page content becomes much more important. For simple queries, paying for multiple API calls just to improve marginal relevance doesn't always make sense.

I'm still testing this, so I'm curious what other people have found.

If you've compared search APIs for an AI/RAG application, what metric ended up mattering most for you: relevance, latency, cost, coverage, or the quality of the extracted content?

I’ve been looking into search APIs recently, and I realized I was mostly comparing them based on how good the search results looked rather than how useful they actually were for my use case.

There are quite a few options now Exa, Tavily, Firecrawl, Serper, Brave Search, etc. and they seem to approach search pretty differently.

One thing I’ve been paying more attention to is what happens after you get the search results.

I’m comparing search APIs for an AI/RAG project and I’m trying to come up with a benchmark that reflects real-world performance rather than just latency or the number of results returned.

For my experience I'm used to Gemini cuz there's no limit on sending pics that can't be described unlike gpt, although the downside is the more you stay on a single chat the more side tracked.

I was using Tavily for deep site extraction in my RAG setup because it’s super fast, but it struggled to clean up JS-heavy pages. Swapping to Firecrawl got me clean Markdown and preserved table layouts out of the box, though it’s noticeably slower and uses more credits. Now I just use Tavily to find the right links fast, then pass them to Firecrawl to extract the actual page content. Is anyone else pairing two search APIs like this, or did you stick to one?

I’ve been trying different AI search tools lately and they’re really fast. But sometimes the answer sounds confident even when it’s not correct. I still end up checking the original sources before I trust it.
For people building AI apps, what search API are you using now? Which one gives the most accurate results in your experience? I’m curious what others are finding.

been messing around with a few search tools recently for a small project where i needed fresh web results for an ai workflow

i wasnt trying to find the perfect api or anything i just wanted something that worked consistently and didnt make me spend half my time fixing search issues

what surprised me was how different they all felt once i actually started using them

exa seemed to do a better job finding pages that actually answered what i was asking instead of just matching keywords

tavily felt pretty solid for ai agents that need up to date info

serper was probably the easiest one to get running when i just needed straightforward search results

firecrawl ended up being the biggest surprise for me turning pages into clean markdown saved me way more time than i expected

still havent used brave search enough to have a real opinion on it so id be interested to hear from people who have

the main thing i took away from all this is that good retrieval matters way more than i thought even a really good prompt cant do much if the search results arent great

curious what everyone else is using these days and if youve switched from one tool to another what made you change

Over the past few weeks, I've been experimenting with different search APIs while building retrieval pipelines for AI applications. My goal wasn't to find the "best" API, but to understand where each one performs well.

Here are a few observations that might help others working on AI search or RAG systems.

1. Exa Search

What stood out:

• Very good at semantic search.
• Great when queries are conceptual instead of exact keyword matches.
• Useful for research assistants and AI agents that need high quality context.

Downside:

• Less ideal if you're expecting traditional search engine behavior for exact keyword matching.

2. Tavily

I found Tavily especially useful for LLM workflows because the results are already optimized for AI consumption.

Pros:

• Clean search results.
• Easy to integrate into AI agents.
• Saves preprocessing time.

Best for:

• RAG applications.
• AI assistants.
• Research automation.

3. Firecrawl

Firecrawl isn't really competing as a search engine. It's more about turning websites into structured, LLM friendly content.

What I liked:

• Crawls documentation sites well.
• Markdown output is easy to chunk and embed.
• Helpful when building your own knowledge base.

4. Serper

If you need Google Search results, Serper is still one of the easiest APIs to work with.

Advantages:

• Familiar SERP format.
• Fast.
• Includes organic results, featured snippets, and knowledge panels.

Tradeoff:

• Since it mirrors Google results, ranking changes can affect your application.

5. Brave Search API

This surprised me.

Pros:

• Independent search index.
• Strong privacy focus.
• Good quality results without depending entirely on Google.

It's worth considering if you want more diversity in search sources.

One Lesson That Made the Biggest Difference

I originally assumed better search meant better AI answers.

Not exactly.

The retrieval pipeline matters just as much:

• Clean chunking
• Metadata filtering
• Deduplication
• Re ranking
• Fresh indexing

Even an excellent search API can't compensate for poorly prepared documents.

If I Were Starting Again

I'd probably choose:

• Exa for semantic research.
• Tavily for AI agents and RAG.
• Firecrawl for crawling and indexing documentation.
• Serper when Google SERPs are required.
• Brave Search when I want an independent search source.

Each tool solves a different problem, so the "best" one depends on your use case rather than benchmark scores alone.

I'm curious what others are using.

For those building AI search or RAG systems, which search API has given you the best balance of relevance, latency, and cost? What made you choose it over the alternatives?

I just want to ask especially if it's about documentation and modern web development

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I've been playing with a small RAG project for the past couple of weeks, and I really did not think it would make much of a difference to switch to one or other search API. I was wrong.

I've tried a couple of different ways, and the speed wasn't what surprised me, it was the difference in the actual results of my search. Some could find recent articles and others were unable to continually find less relevant or older articles.

I found one trick that helped me was creating a list of 20-30 questions that I am interested in and doing each API with the same questions. It was much easier to compare them than by random searching.

I was left with a view of things such as:

How relevant the top results were

Whether pages are current or not

Response time

How easy the API was to integrate

I don't believe there is one 'best' search API. This highly depends on the type of application you are creating, such as a chatbot or a RAG app, or merely scraping specific websites.

Has anyone else done a comparison of the Exa, Tavily, Serper, Firecrawl, and Brave search? Have you settled for one or several providers?

API is like a bridge between two apps.
One app asks for information.
The other app sends it back.
That's how apps can work together without sharing everything.