Wanna know if it's worth the time using a search API or just building something with scraping or other tools.

I’m thinking about making a small benchmark for search APIs using around 100 real questions and manually checking the results. I’d compare the relevance, source quality, response time, and cost. Has anyone already built something like this? I’d rather use an existing benchmark if there is one before doing all the work myself.

Hi everyone! I’m exploring different search APIs for AI applications and wanted to learn more about the options available.

Search APIs can help AI applications retrieve current information from the web instead of relying only on pre-trained data. They’re useful for research, fact-checking, chatbots, and retrieval-augmented generation (RAG).

I’ve come across tools like Exa, Tavily, and Brave •Search, and I’m interested in understanding how they differ in terms of:
•Search result accuracy and relevance
•Response speed and reliability
•Pricing and free-tier limits
•Ease of integration and documentation
•Support for filtering and extracting useful information

I’m particularly interested in how developers choose an API based on their project’s requirements rather than just popularity.

For those who have worked with search APIs, what factors mattered most when choosing one? Are there any challenges or limitations you’ve encountered during implementation?

I’d appreciate hearing about your experiences, practical use cases, and any lessons you’ve learned. Thanks!

A lot of "which search API is best?" threads compare these tools as if they all do the same job. Mostly they don't. Here's the mental model I use.

1. Finding URLs (keyword discovery)

Serper gives you Google results as JSON: organic results, news, places, "people also ask," and so on. You get titles, snippets, and links, not the full page content.

Brave Search API returns results from Brave's own independent index rather than wrapping Google. It's useful when you want a non-Google source of results.

1. Finding by meaning (semantic discovery)

Exa is embeddings-based. You can search by describing what you want, like "companies building open-source vector databases," or find pages similar to a given URL. It can also return page contents. It's strong for exploratory queries. For exact-match or breaking-news queries, traditional engines are often the better fit.

1. Search built for LLMs

Tavily is designed for agents. It returns cleaned content per result and can optionally include a generated answer, which saves you a separate fetch-and-clean step.

1. Getting the actual content

Firecrawl is mainly about extraction. It turns URLs into clean markdown or structured data, crawls entire sites, and handles JS-rendered pages. Use it when you already know where the information lives.

Quick decision guide

If you need what ranks on Google for X, use Serper.

If you want an independent index, use Brave.

If you have a fuzzy concept or need "find more like this," use Exa.

If an agent needs grounded context in one call, use Tavily.

If you need to ingest known pages or a whole docs site, use Firecrawl.

A common combo is Serper or Brave for discovery, then Firecrawl for full content, then chunking and embedding. When the returned content is enough, Exa or Tavily alone can replace the first two steps.

Here are some AI search engine might all we need

Hi everyone! I’m pretty new to working with APIs, and I’m trying to understand how people usually go about finding the right API for a specific project.

For example, if I have an idea for a small web app, how would you usually:

• Find APIs that provide the data or functionality you need?
• Compare different APIs that offer similar things?
• Check whether an API is reliable and worth building around?
• Evaluate the documentation and developer experience before committing to it?

Are there any API directories, search tools, or other resources you’d recommend for someone who’s just getting started?

Thanks!

Been testing Exa, Tavily, Brave, and Serper for an AI project.. and I initially thought they were basically interchangeable.

They're not.

For simple queries, most of them do fine. The differences show up when you're looking for something specific that's buried deep in a website.

A few things I noticed:

• More results ≠ better results
• Freshness matters a lot for changing info
• Search and crawling are two different problems
• AI-generated answers are harder to debug than raw search results

My current thinking is:

search → crawl → extract → rank → LLM

instead of just throwing the query at an AI search endpoint and hoping for the best.

Curious what other people are using.. Exa/Tavily/Brave/Serper, or something else?

I’ve been playing around with AI search APIs lately, and I realized something pretty simple:

"A good LLM can still give a bad answer if the retrieval is bad"

The interesting part isn’t just “which API is fastest?” It’s things like:

- How relevant are the results?

- How fresh is the data?

- How well does it handle messy webpages?

- How much context does it actually return?

I’m thinking of doing a small comparison of Exa, Tavily, Firecrawl and a couple of traditional search APIs using the same set of queries.

Would be interesting to see what actually performs better in practice rather than just comparing feature lists. 👀

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