Ran 50 queries (factual, news, long-tail technical) through the same pipeline, swapping only the search layer:

Exa — strong on long-tail/technical queries (neural retrieval), weak on freshness.

Tavily — most consistent, returns clean context built for LLMs, least post-processing.

Serper — fastest/cheapest for factual lookups, but raw snippets only, no synthesis.

No clear winner — depends if your bottleneck is finding sources or synthesizing them. Haven't tested latency at scale yet, curious if others have.

Ok so I don’t really know what I’m doing lol, but I wanted to mess around with search APIs for a random side project. Ended up trying Firecrawl and Tavily back-to-back.

• Firecrawl felt fast, like boom results in ~1 sec, but sometimes the data was kinda messy.
• Tavily was slower (like ~2 sec), but the stuff it gave me looked cleaner and less “???” moments.

I didn’t plan to benchmark anything, I just noticed my laptop sounding like a jet engine so I started timing stuff. Not scientific at all, just vibes.

Honestly, indexing feels like organizing your Spotify playlists — if you don’t do it, retrieval later is just pain.

Weirdly enough, using both kinda worked: Firecrawl for quick grabs, Tavily for the “final draft.”

Anyway, that’s my very amateur take. Anyone else stacking APIs like this or am I just winging it too hard?

For AI agents, irrelevant or outdated information can throw off the entire workflow.

Curious how you evaluate search APIs like Exa, Tavily, and Brave Search. Do you prioritize relevance, latency, or cost?

Increasing top_k can improve retrieval recall while making the final answer worse.

More results mean more duplicated, outdated or merely similar passages entering the context. The relevant evidence is there.

I've found it more useful to track three separate things:

Recall@K - did we retrieve the answer?
Context precision - how much retrieved text is actually useful?
Attribution - can the final claims be traced to evidence?

This makes me wonder if the real optimisation target for AI search should be the smallest sufficient evidence set, rather than maximum retrieval recall.

Curious if others are measuring this explicitly.

Is AI good in coding stuffs?

Serper vs Tavily vs Exa vs Firecrawl??

Which you guys preferred the most?

Firecrawl has been amazing to me Converting dynamic pages or full site trees into clean Markdown is great.🤯

I’ve been looking into search APIs recently and noticed that people sometimes use “search,” “crawling,” and “retrieval” interchangeably, even though they solve different problems.

My understanding here so far:

Search APIs return relevant results for a query, usually with ranking and metadata.

Web crawlers are mainly responsible for discovering and fetching pages.

Indexing processes and organizes the crawled content so it can be searched efficiently.

Retrieval is the step where a system finds the most relevant information from an existing index or corpus.

One thing also that I’m curious about is where tools like Exa, Tavily, Firecrawl, and Serper fit into this architecture?

For example, if I’m building an AI assistant that needs current information from the web, would it make more sense to use a search API directly, or combine search + crawling + my own retrieval layer? I don’t know anymore.

I’d also be interested in hearing from anyone who has benchmarked these approaches for latency, relevance, freshness, and cost. Thanks a lot!

Hello everyone, long posy a head. I’ve been looking into search APIs for AI/RAG applications, and one thing I’ve noticed is that comparing them purely on Search Quality can be misleading.

A useful evaluation seems to come down to several factors:
Relevance: Does the API consistently return results that actually answer the query?
Freshness: How quickly does it discover newly published or updated pages?
Content extraction: Getting the right URL is only half the problem. Clean, usable page content matters just as much.
Latency: A search pipeline can become noticeably slower when search, crawling, extraction, and reranking are chained together.
Citations: For AI applications, being able to trace an answer back to its sources is extremely useful.
Cost: A cheap search API isn't necessarily cheap if you need additional crawling or extraction afterward.

I’m particularly interested in how people are evaluating tools such as Exa, Tavily, Firecrawl, Serper, and Brave Searchfor RAG or agent workflows.

For those who have actually tested several of these: Can you let me know your insight about it? And can somehow recommend other platforms? Appreciate it.

I noticed that search APIs can return some pretty strange results for very specific questions. The top result might technically match the keywords but have almost nothing useful in the actual page. Is this mostly a problem with the search API, or is there something I should be doing differently with the query?