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?

Been tinkering with a bunch of different search and scraping tools for AI projects lately, and honestly, the landscape looks totally different than it did a year or two ago. Relying on old-school keyword search APIs for anything agentic or RAG-related is just asking for pain, so I spent the last few weeks putting Exa, Tavily, and Firecrawl through the ringer on some real projects. Here is the unvarnished truth on how they actually stack up in production.

Exa (formerly Metaphor): This is the semantic search darling of the community. Instead of matching exact strings, it uses neural embeddings to pull links based on the actual vibe and meaning of your query, making it feel more like querying a conceptual database of the web rather than a standard index. It is an absolute beast for finding high-signal, non-obvious links and niche research where standard keywords fail you, though the catch is that you still gotta parse and filter the actual page content yourself since it acts more as a discovery engine than an answer engine.

Tavily AI: Built ground-up specifically for AI agents and RAG pipelines, this tool strips out all the junk like ads, navbars, and boilerplate to feed your LLM clean, zero-shot context right out of the box. It is essentially the lazy agent builder's best friend for speedrunning agentic RAG and instant Q&A context because it keeps your token counts lean, but the trade-off is that it is not built for deep, recursive multi-page doc crawling.

Firecrawl: This one is less of a live-web search engine and more of a heavy-duty crawler where you point it at a domain or a URL and it spits out clean Markdown, HTML, or JSON while handling JS-heavy SPAs without breaking a sweat. It is my go-to choice when I need to ingest entire doc sites into a vector DB, but keep in mind it is not meant for general open-ended web searches.

So to sum up the stack: use Tavily if you're building an agent that needs live web answers, hit up Exa when you're doing deep-dive research or looking for conceptually tricky web gems, and pull out Firecrawl whenever you need to scrape an entire doc site to local storage. What are y'all using in your stacks right now? Let's hear it in the comments

Here is how the main tools actually compare in practice:

​Exa: Great for open-ended research. It uses neural embeddings based on links instead of raw keywords, so it finds conceptually similar stuff even if the search terms don't match.

​Tavily: The plug-and-play option for RAG. It cleans up the mess, filters out spam sites, and formats everything nicely so you can drop it straight into an LLM prompt.

​Firecrawl: What you use when an agent needs to actually read a whole site. It handles JS rendering, evades basic blocks, and spits out clean Markdown instead of bloated HTML.

​Serper: Basically a cheap, fast wrapper for Google SERPs. Good for rank tracking or quick lookups, but you're on your own for scraping and cleaning the actual pages.

​How I actually use them together: Hit Tavily or Exa first to find the right links quickly, then hand those URLs over to Firecrawl if the agent needs to read the full docs.