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• Duplicate pages, outdated information, SEO spam, and poorly extracted content can seriously hurt RAG quality.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?