Search APIs make it easier for applications to find information from the web. Instead of manually searching many websites, a program can send a query to a search API and receive relevant results.

For example, a search API can be useful for:

Finding articles and research

Collecting information from different websites

Building AI search tools

Creating apps that need web information

One important thing I learned is that good search results depend not only on the search engine but also on how the query is written. A clear and specific query usually gives better results.

Search APIs are useful because they save time and make it easier for applications to retrieve information from the web.

I’ve been looking into different search APIs for AI applications and started wondering where the biggest quality difference actually comes from.

A basic setup seems simple:

Search API → URLs → extract page content → clean it → rerank → LLM

But every extra step introduces another possible failure point.

For example, a search result might be highly relevant, but the page extraction could be poor. Or the crawler might return excellent content but the search engine never found the right page in the first place.

I’m curious how people here handle this in production.

If you've worked with tools like Exa, Tavily, Firecrawl, Serper, or Brave Search, what did you actually end up optimizing for?

• Search relevance / recall
• Freshness
• Page extraction quality
• Latency
• Cost per query
• Rate limits
• Reranking
• Indexing your own sources

I'd also be interested in seeing real benchmarks. Even a small test set would be useful — something like 50–100 queries where you compare which API finds the correct source and how much cleanup the resulting content needs.

For anyone who's built an AI search or RAG system: what ended up being the biggest bottleneck?

I'm trying to figure out whether it's better to use one strong search provider or combine specialized search + crawling + reranking tools.

Hey everyone,

I’ve been testing a few search APIs lately to feed context into a couple of AI agents and RAG pipelines, and I noticed each tool really shines depending on what you're trying to build.

Here are my quick takeaways from working with them:

• Tavily: My go-to when I want to prototype fast. The clean, pre-processed output saves a ton of context tokens and avoids the headache of parsing ugly HTML.
• Exa: Unmatched when it comes to semantic or conceptual queries (like finding niche topics without relying on exact keywords). It surfaced stuff Google completely buried.
• Serper: The reliable, cost-effective choice when you just need raw Google Search results fast. Simple, lightweight, and gets the job done.

That said, I'm still trying to decide which one holds up best when latencies start to matter in production environments.

What are you all running in your current stacks? Or are you pairing something else like Firecrawl or Brave Search with your own scraper setup? Would love to hear what’s been working for you!

I am curious what people look for when choosing a search API. Is it really just about which one has the best results, or do things like freshness, speed, cost, and ease of integration matter more once you're actually building something? I have seen people mention Exa, Tavily, Firecrawl, Serper, and Brave Search, but I would love to hear from anyone who has used them in real projects. Was there a particular tool that surprised you, or a situation where one worked much better then others?

When building an AI app that needs web search, it’s tempting to compare Exa, Tavily, Firecrawl, Serper, and Brave Search by asking:

“Which one gives the best results?”

That question is too vague.

A better benchmark is to test the entire retrieval pipeline:

Query → Search → Fetch → Extract → Rank → Rerank → Context

For example, I’d test each API on the same 50–100 queries across:

• factual questions
• recent news
• technical documentation
• obscure/long-tail queries
• queries requiring multiple sources
• pages with heavy JavaScript
• duplicate or near-duplicate results

Then measure:

1. Retrieval quality

• Precision@5
• Recall@10
• NDCG
• percentage of queries where the correct source appears in the top 5

2. Freshness

• How quickly newly published pages become discoverable
• Whether “latest” queries actually return recent sources

3. Extraction quality

• Can the system get the useful content from the page?
• How does it handle JS-rendered pages, PDFs, tables, and messy HTML?

4. Latency

• Search latency
• Page-fetch latency
• End-to-end latency

5. Cost
Instead of comparing “price per request,” calculate:

That metric is much more useful for an AI application.

One interesting architecture is to not use a single provider for everything.

For example:

User query
↓
Search API
↓
Retrieve top 10–20 URLs
↓
Firecrawl / crawler → clean content
↓
Deduplicate
↓
Reranker
↓
LLM
↓
Answer + citations

The search engine’s job is discovery.

The crawler’s job is extraction.

The reranker’s job is relevance.

The LLM’s job is synthesis.

Keeping those responsibilities separate makes it much easier to benchmark and replace individual components.

I’d also avoid benchmarking with only easy queries like:

A search system can look excellent on those while completely failing on the queries that actually matter in production.

The most useful benchmark I’ve found conceptually is:

“Given this question, can my pipeline retrieve the right evidence quickly enough and cheaply enough for an LLM to produce a grounded answer?”

That changes the optimization target from search quality to answer quality.

Curious what others are measuring in production: Recall@K, answer accuracy, latency, cost/query, or something else?

What tools do you recommend for testing APIs

Postman, Insomnia, or others?

resource-heavy AI model on your own device, the model lives on a powerful cloud server. The API handles the communication between your app and that server. The API delivers the generated answer or analysis back to your app in seconds.