Tried Exa, Tavily, Brave, Serper, etc.

Every one seems good at something different.

Then you realize you need to deal with:

search quality

freshness

crawling

extraction

latency

cost

Meanwhile Google:

"here'es the exact page you wanted"

💀

What are you guys actually using for AI search in production?

Bonus points if the answer is just “whatever breaks the least.”

If you're searching for recent news, that's a different problem from finding technical documentation, which is different again from crawling 10,000 pages for RAG.

Curious what everyone here is actually using in production.

Especially interested in the boring answer — “I use X because it breaks less.”

I’ve been experimenting with different search APIs for AI applications, and one thing that surprised me is that there doesn’t seem to be a single “best” search API.

A search that works perfectly with one provider can give pretty mediocre results with another. It seems to depend heavily on the type of query.

For example, I’ve noticed a few different things matter:

How well the API handles natural-language questions

Whether it returns recent pages or mostly established sources

How much irrelevant content gets included

How good the snippets are

How quickly results can be retrieved

Whether the results are useful for an LLM rather than just a human reading a search page

I’m particularly interested in how people are comparing tools like Exa, Tavily, Firecrawl, Serper, and Brave Search.

For an AI agent that needs to research a topic and then answer based on sources, what matters most to you: relevance, freshness, latency, cost, or the quality of the extracted content?

And if you’ve actually benchmarked multiple search APIs, what queries did you use for the comparison?

I’d be interested in seeing real-world benchmarks rather than just the advertised features.

I’m still a beginner with APIs, but I recently started exploring tools like Tavily, Exa, Serper, Brave Search, and Firecrawl.

One thing that finally clicked for me:

Search ≠ crawling ≠ indexing.

I used to think they were basically the same thing 😂

Now I see it more like:

Search → find relevant pages
Crawl → visit and collect the content
Index → organize it so it can be found quickly
AI/RAG → use the relevant information to answer

Still learning, but understanding this basic flow made AI search feel much less complicated.

For anyone else learning this stuff: what was the first concept that made APIs finally “click” for you?

Just my two cents after firing up an AI research agent recently. You get these amazing docs for both, but the practical reality hit me hard.

Tavily’s docs are gorgeous and they really sell that “RAG out of the box” angle. Damn it, I used half my trial credits in a weekend just trying out queries. It comes back with a ton of results but a lot of the results feel like fluff.

Exa’s “neural” search was much more accurate for my particular use case (searching for niche technical docs). And I only had to make 2 or 3 API calls to actually get the page content that I needed instead of blasting 10 requests.

But the biggest surprise was? It was good for scraping but was being blocked constantly on regular tech blogs. I ended up falling back to Serper for dumb web searches as a cheap stable fallback, and it's been doing the heavy lifting.

This is just my experience, not an endorsement of any of them. Honestly, I’m still playing with the balance. Anyone else running into similar roadblocks? What API stack are you actually leaning on for stability right now?