---
title: "Search API vs Scraping API for LLMs"
category: "comparisons"
url: https://keirolabs.cloud/blog/search-api-vs-scraping-api-for-llms
---
## What Is the Difference Between a Search API and a Scraping API for LLMs?
A search API takes a query and returns a ranked list: URLs, titles, and snippets pulled from an index the vendor maintains or a SERP it fetches. A scraping API takes a URL and returns that page: raw HTML, cleaned markdown, a screenshot, or structured fields. The inputs are different nouns. The outputs are different products. That is the whole distinction, and most agent architecture mistakes trace back to blurring it.
Everything else follows from the input difference. Because a search API answers "what should I look at?", it needs an index. Brave crawls more than 30 billion pages itself and applies roughly 100 million page updates a day ([brave.com/search/api](https://brave.com/search/api/)). Exa maintains its own neural index. Keirolabs runs its own retrieval and extraction pipeline. Serper and SerpAPI skip the index and parse Google's results, which is why they are cheap and why [Google's SerpAPI lawsuit](https://keirolabs.cloud/blogs/guide/search-api-crackdown-2026-serpapi-lawsuit-bing-shutdown) matters to their customers. A scraping API answers "read this," so it needs no index. It needs proxies, a browser pool, and a tolerance for getting blocked.
The outputs differ in size, and size is where LLM bills are made. A search snippet runs about 50 words. Ten results cost a model somewhere around 700 to 900 tokens. A full article page converted to markdown runs 800 to 2,500 words, call it 1,200 on average, which is roughly 1,600 tokens. Ten full pages is about 16,000 tokens of context. At any frontier model's input price, the pages cost more than the retrieval line item, sometimes by an order of magnitude. That arithmetic is the real argument in this post, and it cuts both ways: snippets are cheap to read and often insufficient, pages are expensive to read and usually sufficient.
We have one measured number for "often insufficient." On our 1,000-question SimpleQA run, the same pipeline scored 72.4% when it could only see SERP snippets, 87.7% when it could fetch full pages, and 95.3% with the final judging layer on top ([the full methodology](https://keirolabs.cloud/Deep-search)). Fifteen points of accuracy lived in the pages, not the snippets. A search API sold you a map; the scraping layer is what read the terrain.
> A search API sells you a map. A scraping API sells you the territory. Agents that navigate need both.
So the honest answer to the title question: they are not competitors. They are two layers of one pipeline, priced separately, and the only real decision is which layer your workload leans on and what you pay for each.
## Web Scraping API vs Search API for LLMs
The two tools fail in opposite directions, which is why picking one and skipping the other tends to fail loudly. Here are the tradeoffs, stated as plainly as we can.
**Search API, for:**
- It discovers. An agent with no seed URLs gets ten ranked candidates for one call, which no scraper can do at any price.
- The token payload is small. Snippets cost a few hundred tokens per task, so a search-first agent can run hundreds of tasks inside one context budget.
- It is cheap per call. Keiro's /search/lite bills a $0.25 per 1,000 headline and 0.1 credit per request on monthly plans, $0.08 to $0.24 per 1,000 ([keirolabs.cloud/pricing](https://keirolabs.cloud/pricing)). Serper runs about $1.00 per 1,000 Google queries ([ColdIQ's verified breakdown](https://coldiq.com/blog/serper-pricing)).
- Ranking is someone else's problem. You get relevance tuning, freshness pipelines, and spam filtering as a service.
**Search API, against:**
- Snippets truncate. The 72.4% SimpleQA ceiling above is the measured version of that problem.
- You inherit index lag. Every index is a cache, and none of them publish their TTL for your query.
- The pricing has stairs. Exa's $7.00 per 1,000 covers 10 results and results 11 and above add $1.00 per 1,000 each, so a 30-result search bills $27.00 per 1,000 ([Exa docs](https://docs.exa.ai/reference/pricing)). Serper doubles to $2.00 per 1,000 when a query asks for 11 to 100 results.
- On scraped-SERP vendors, the ranking is Google's, and the permission to resell it is not yours.
**Scraping API, for:**
- It returns the document. Full text, tables, and markup survive, which is what grounded answers and RAG ingestion actually need.
- It works on known URLs. A fixed source list, a competitor's changelog, a supplier catalog: no query will get you there more reliably than the URL itself.
- Per-page pricing is legible. Firecrawl bills 1 credit per scraped page on a plan that runs $83 to $99 per month for 100,000 credits, so roughly $0.83 to $0.99 per 1,000 pages ([firecrawl.dev/pricing](https://www.firecrawl.dev/pricing)).
- Extraction quality is yours to verify page by page, not buried in someone's ranking model.
**Scraping API, against:**
- It cannot discover anything. Point a scraper at a question and it waits for a URL that never arrives.
- Blocking is a cost line, not a bug report. ScraperAPI's own price table charges 25 credits for a Google page, 30 for LinkedIn, and 10 extra credits per request whenever a Cloudflare, Datadome, or PerimeterX wall has to be bypassed ([scraperapi.com/pricing](https://www.scraperapi.com/pricing/)). A "standard page costs 1 credit" headline can quietly become 25x on the targets you actually care about.
- Anti-bot maintenance is a treadmill, and it runs at night.
- ToS and legal exposure sit with you, not with the index that already did the crawling.
The pattern we keep seeing in production: teams that start search-only hit a quality wall, teams that start scrape-only hit a discovery wall, and both end up building the hybrid in the same quarter.
## AI Agent Web Scraping API Cost Comparison
Prices first, all checked September 2026, each linked to its source. On the scraping side, per 1,000 standard pages:
| Scraping API | Entry paid rate | Per 1,000 standard pages | The catch | Source |
|---|---|---|---|---|
| ScrapingBee | $49/mo, 250,000 credits | $0.20 | JavaScript rendering and premium proxies cost extra credits; top self-serve tier is $599/mo for 8M | [ScrapingBee pricing](https://www.scrapingbee.com/pricing/) |
| ScraperAPI | $49/mo, 100,000 credits | $0.49 | Google 25 credits, LinkedIn 30, bot-wall bypass +10; $1,225 per 1,000 Google pages | [ScraperAPI pricing](https://www.scraperapi.com/pricing/) |
| Firecrawl | $99/mo ($83 annual), 100,000 credits | $0.83-$0.99 | JSON extraction adds 4 credits per page; Hobby works out to $3.20 per 1,000 | [Firecrawl pricing](https://www.firecrawl.dev/pricing) |
| Keiro /extract | 3 credits/request | $2.40 (Startup) to $7.20 (Essential) | Extraction only; it does not crawl sites for you | [Keiro pricing](https://keirolabs.cloud/pricing) |
| Tavily basic extract | $0.008/credit pay-as-you-go | $8.00 | 1 credit per URL; advanced extract is 2 | [Tavily docs](https://docs.tavily.com/documentation/api-credits) |
| Crawl4AI | Open source, free | Your compute | You own the proxies, the browser pool, and the pager | [Crawl4AI repo](https://github.com/unclecode/crawl4ai) |
On the search side, per 1,000 queries: Keiro /search/lite $0.08 to $0.25, Serper $1.00, Firecrawl Search $1.98 (2 credits per 10 results on Standard), Parallel $1.00 to $5.00, Brave $5.00, Linkup $5.00 to $6.00, Exa $7.00, Tavily $8.00 basic and $16.00 advanced, SerpAPI $15.00. Every one of those is sourced in [our Tavily alternatives breakdown](https://keirolabs.cloud/blogs/comparisons/tavily-alternatives-2026) and the pricing pages it cites.
Now the number that actually decides things: what does one grounded answer cost, at 100,000 tasks per month? State the assumptions first, because this is a model, not a quote. One task equals one question that needs current web grounding. Pure search reads snippets only. Pure scrape assumes the URL is already known. Hybrid equals one search plus reading the top 3 pages. Token math uses 1.3 tokens per word.
| Architecture | Cheapest realistic stack | Cost per 100k tasks | Tokens shipped to the model per task |
|---|---|---|---|
| Pure search, snippets only | Keiro /search/lite on Startup ($0.08/1k) | $8 | ~800 |
| Pure search, snippets only | Tavily basic ($8/1k) | $800 | ~800 |
| Pure scrape, known URLs | ScrapingBee standard pages ($0.20/1k) | $20 | ~4,800 |
| Pure scrape, known URLs | Keiro /extract on Startup (3 credits) | $240 | ~4,800 |
| Hybrid, one call | Keiro /search/content, Startup (3 credits) | $240 | ~5,500 |
| Hybrid, two vendors | Serper search + 3 Firecrawl pages | $349 | ~5,500 |
| Hybrid, search-first index | Firecrawl search + 3 scrapes (5 credits) | $415 | ~5,500 |
| Hybrid, index + extractor | Brave search + 3 Firecrawl pages | $749 | ~5,500 |
| Hybrid, contents bundled | Exa search + contents | ~$1,000 | ~5,500 |
| Hybrid, premium bundle | Tavily advanced ($16/1k, content included) | $1,600 | ~5,500 |
| Hybrid, unbundled | Tavily basic + 3 basic extracts ($32/1k) | $3,200 | ~5,500 |
Three things to take from the table. First, the spread within one architecture is 10x to 40x, so picking the vendor matters more than picking the architecture. Second, the hybrid is not the expensive option; on Keiro's Startup plan a full search-plus-read answer costs $2.40 per 1,000 tasks, which is less than a bare snippet search on Exa, Tavily, Brave, or SerpAPI. Third, snippet-only search is the cheapest row and the weakest answer, and the 72.4% SimpleQA score is what that cheapness buys.
One warning about the model: it assumes every page fetch succeeds. In production, blocked requests, paywalls, and layout rewrites push the scraping rows toward their vendor's retry pricing, which is why the anti-bot multipliers in the next section deserve a look before you trust the cheapest row.
## How Should I Compare Search APIs on Predictable Pricing for Agent Workloads?
Predictability is a feature you buy, not a property you get for free. Four tests, in the order we apply them.
First, what is the billing unit? The market sells three units: requests, results, and credits. A request-priced API (Brave at a flat $5.00 per 1,000, Keiro's per-endpoint credit table) behaves like a meter you can read from across the room. A results-priced API (Exa's 10 included results, Serper's 10-results-per-credit tiers) lets the model's curiosity set your price: ask for 30 results and the rate more than triples. A credit-priced API (Tavily, Firecrawl, ScraperAPI) is honest only after you price the credit table, not the credit.
Second, what does the credit actually convert to? This is where the invoices live. Keiro's /search/lite costs 0.1 credit per request on monthly plans and 0.5 credit in one-time packs, which is the difference between $0.24 per 1,000 on the $30 Essential plan and $2.50 to $3.33 per 1,000 on a prepaid pack ([Keiro pricing](https://keirolabs.cloud/pricing)). Firecrawl's JSON format adds 4 credits to a 1-credit scrape, so structured extraction is 5 credits a page and Hobby's 5,000 credits cover 1,000 JSON pages instead of 5,000 ([Firecrawl pricing](https://www.firecrawl.dev/pricing)). Tavily's basic search is 1 credit and advanced is 2, which doubles the per-search price the moment an agent starts reading pages ([Tavily docs](https://docs.tavily.com/documentation/api-credits)).
Third, does the price move with your inputs? ScraperAPI's domain cost table is the most honest version of this in the industry: a standard page is 1 credit, Amazon is 5, Google is 25, LinkedIn is 30, and bypassing Cloudflare, Datadome, or PerimeterX adds 10 credits a request ([ScraperAPI pricing](https://www.scraperapi.com/pricing/)). Most vendors bury the same economics. An agent pointed at protected targets burns 10x to 30x the sticker rate, and it does so exactly when the workload is most valuable.
Fourth, what happens at the free-to-paid boundary? Serper's 2,500 free queries are a one-time batch, and pack credits expire in 6 months ([ColdIQ](https://coldiq.com/blog/serper-pricing)). Brave's free tier was removed in February 2026 and replaced with $5 in monthly credits that require a card on file. Keiro's 1,250 credits a month recur with no card and cover 12,500 lite searches. For an agent workload, a recurring free tier is worth more than a bigger one-time grant, because agents do not stop after the demo.
The quotable version: predictable pricing is per-request pricing on a plan, with the credit table published and the surcharges listed. Everything else is a quote in disguise.
## Brave Search API vs Proxy Scraper Latency
Latency is where the two architectures feel most different from inside an agent loop, because one of them is a lookup and the other is a fetch.
| API | Published latency | Type | Source |
|---|---|---|---|
| Brave Search API | LLM Context endpoint adds under 130 ms to a pipeline | Index | [Brave Search API](https://brave.com/search/api/) |
| Tavily | 180 ms p50 on /search (vendor claim) | Hybrid | [Tavily](https://www.tavily.com/pricing) |
| Parallel | 200 ms to 3 s synchronous, priced by band | Own infra | [Parallel pricing](https://parallel.ai/pricing) |
| Keiro /search/lite | Fast tier, 0.1 credit per request on plans | Index + extraction | [Keiro pricing](https://keirolabs.cloud/pricing) |
| Serper | 1 to 2 s for Google results | SERP scrape | [ColdIQ breakdown](https://coldiq.com/blog/serper-pricing) |
| Linkup | Under 2 s synchronous | Own index | [Linkup pricing](https://www.linkup.so/pricing) |
| Firecrawl scrape | Seconds per page, render-dependent | Live crawl | [Firecrawl pricing](https://www.firecrawl.dev/pricing) |
The pattern is structural. Search is a cache lookup, so its p50 sits in the tens or hundreds of milliseconds. Scraping waits on a remote origin, proxy rotation, and often a headless browser, so its p50 is measured in seconds and its tail is where the money and the timeouts live. Brave's own numbers make the split concrete: its index-side endpoint adds under 130 ms, while any live fetch adds seconds.
Two practical notes. First, p50 is marketing and p99 is the invoice: agent loops chain calls, so one 8-second scrape in a 3-step loop can cost you the user's attention, and retries on blocked pages double it. Second, latency and correctness trade against each other in a specific way. The Fastest claimed search (Tavily's 180 ms) still tops out at snippet quality, and the 15.3-point SimpleQA gap between snippets and full pages is paid in fetch time. If your agent answers from snippets at 180 ms, budget for the 27.6% of questions it gets wrong that full pages would have caught.
Keiro's answer to the chaining tax is batching: /batch runs many searches async at 1 credit per query, so a research agent can fire its discovery layer wide and read pages in parallel instead of serially ([Keiro pricing](https://keirolabs.cloud/pricing)).
## Scraping API for Real Time AI Research Agents
Freshness is where the two architectures genuinely split, and the split is sharper than any pricing table.
A search index is a cache with an unpublished TTL. Brave refreshes roughly 100 million pages a day across a 30-billion-page index, which is impressive scale and still no promise that the page your agent needs was among them in the last hour ([Brave Search API](https://brave.com/search/api/)). Every own-index vendor has the same shape of answer: fresh on average, stale at the edges, and silent about which edge your query landed on.
Live scraping has the opposite failure mode. Firecrawl crawls on demand, so what comes back is minutes old, and it will wait for JavaScript rendering that a cached index never saw ([Firecrawl pricing](https://www.firecrawl.dev/pricing)). But a live fetch is slower, rate-limited, and can be refused, and it costs a page credit whether or not the page changed. On Firecrawl's Monitor endpoint, watching a page costs a flat 7 credits per page per check ([Firecrawl pricing](https://www.firecrawl.dev/pricing)), which prices a 100-page watchlist at 700 credits per cycle: about $0.58 to $0.69 on Standard, $2.24 on Hobby, before any extraction add-ons.
So the workload decides the tool:
- **Monitoring a fixed URL list** (competitor pricing pages, changelogs, a regulator's feed): scraping on a schedule. The URLs are known, freshness is the whole point, and search adds nothing but cost. This is the pure-scrape row of the cost model: $20 to $240 per 100,000 page checks on ScrapingBee or Keiro Startup.
- **Monitoring a topic** ("what is being published about this vendor"): search, because the result set changes by the hour and no URL list covers it. Snippets carry most of the signal for "something happened"; you scrape only when something did.
- **Real-time research agents** (answering "as of today"): hybrid, with one extra rule. Search first, then fetch pages only for results whose timestamp or freshness the answer depends on. On our 100-question FreshQA harness, retrieval scored 91 with full reads against 83 for Perplexity and 77 for Tavily's snippet-first pipeline ([methodology](https://keirolabs.cloud/blogs/comparisons/top-8-ai-search-apis-compared-2026)).
The failure mode to design for is stale-page confidence: an index returns a cached version, the agent quotes a price that changed this morning, and nothing in the pipeline flags it. If your agent's answers have money attached, add a freshness check on the pages that carry the numbers, whatever API you buy them from.
## Clean Architecture for Agents That Need Browser + Scraping APIs Together
The architecture that survives contact with production has three layers, and the discipline is in what each layer is allowed to do.
**Discovery layer: search.** Queries in, ranked URLs out. Cheap, small-payload, run it wide. This is /search/lite territory, 0.1 credit a request on Keiro plans, or Serper at $1.00 per 1,000 if you want raw Google.
**Reading layer: extraction.** URLs in, clean text out. This is the scraping API's job, and it should be the only component in the system that knows what a proxy is. Keiro's /extract runs 3 credits a page and returns markdown; Firecrawl runs 1 credit a page with rendering built in.
**Interaction layer: browser.** Pages that require clicks, logins, or state get a real browser, and nothing else gets one. Firecrawl's Interact endpoint prices this honestly at 2 credits per browser minute ([Firecrawl pricing](https://www.firecrawl.dev/pricing)). A browser is a tool for a minority of pages, not a default reading strategy; it is your slowest and most expensive per-page path.
Here is the pipeline in TypeScript, the shape we run ourselves:
```typescript
// hybrid.ts: search to find, extract to read, snippets as the fallback.
const API = "https://api.keirolabs.cloud/api/v2";
type Result = { url: string; title: string; snippet?: string };
const pageCache = new Map(); // swap for Redis in production
async function search(q: string, key: string): Promise {
const r = await fetch(`${API}/search/lite`, {
method: "POST",
headers: { "content-type": "application/json", authorization: `Bearer ${key}` },
body: JSON.stringify({ query: q, num_results: 10 }),
});
if (!r.ok) throw new Error(`search failed: ${r.status}`);
const data = await r.json();
return data.results ?? [];
}
async function extract(url: string, key: string): Promise {
const hit = pageCache.get(url);
if (hit) return hit; // a cached page costs 0 credits, the cheapest line in the budget
const r = await fetch(`${API}/extract`, {
method: "POST",
headers: { "content-type": "application/json", authorization: `Bearer ${key}` },
body: JSON.stringify({ url }),
});
if (!r.ok) throw new Error(`extract failed: ${r.status} ${url}`);
const data = await r.json();
pageCache.set(url, data.markdown ?? "");
return data.markdown ?? "";
}
// One grounded task: 0.1 (search) + 3 x 3 (reads) = 9.1 credits worst case.
// On Keiro Startup at $0.0008/credit that is $7.28 per 1,000 tasks.
export async function groundedContext(task: string, key: string, maxPages = 3) {
const results = await search(task, key);
const picks = results.slice(0, maxPages);
const reads = await Promise.allSettled(picks.map((p) => extract(p.url, key)));
const parts: string[] = [];
for (let i = 0; i < picks.length; i++) {
const read = reads[i];
if (read && read.status === "fulfilled" && read.value.length > 0) {
parts.push(`# ${picks[i].title}\nURL: ${picks[i].url}\n${read.value.slice(0, 6000)}`);
} else {
// Settle for the snippet so the model at least sees the claim.
parts.push(`# ${picks[i].title} (snippet only)\nURL: ${picks[i].url}\n${picks[i].snippet ?? ""}`);
}
}
return parts.join("\n\n---\n\n");
}
```
Five decisions in that code are load-bearing. The cache runs before the credit meter, because re-reading a page you already have is the most common wasted spend in agent systems. Reads run in parallel with `allSettled`, so one blocked page does not sink the task. Every page is truncated before it reaches the model, because a 16,000-token context of raw page text is a tax you pay on every turn. Failed reads fall back to snippets instead of failing the task. And the per-task credit cost is written in a comment next to the code that spends it, so the finance conversation happens in the same file as the engineering.
One architectural rule worth writing on the whiteboard: if your agent reads nearly every result it fetches, collapse the two layers into one endpoint (Keiro's /search/content at 3 credits, Tavily's basic search, Firecrawl's search) and pay one meter. If it reads 2 results out of 20, keep the layers apart, because the combined endpoint makes you pay for content you throw away.
## How Do I Control Which Sources an AI Agent Can Search for Web Grounding?
Source control is a compliance feature before it is a quality feature, and the two API types give you different levers.
With a search API, you are filtering someone else's index. The practical tools: domain include and exclude filters (both Exa and Tavily expose them; check the parameter names in your provider's docs), query shaping, and post-filtering on the returned URLs before anything reaches the model. What you cannot do is guarantee the index contains or excludes anything the vendor has not documented. An index owner can drop a domain, re-rank a vertical, or change snippet lengths, and your grounding policy changes with it. That is not a reason to avoid search APIs; it is a reason to log the URLs you grounded on.
With a scraping API, source control is total, because you name every URL. The allowlist lives in your code, and nothing outside it is ever fetched. That is the architecture compliance teams actually want: a fixed corpus of permitted domains, re-fetched on demand, with provenance that never surprises. The costs are the scraping costs: discovery is your problem, freshness is your cron job, and the anti-bot layer is a real operating expense, not a line item. ScraperAPI's published multipliers (Google 25 credits, LinkedIn 30, Cloudflare bypass +10) are the visible tip of that ([ScraperAPI pricing](https://www.scraperapi.com/pricing/)).
The anti-bot reality deserves its own paragraph, because listicles skip it and budgets do not. Public web targets fall into three bands. Docile targets (docs sites, blogs, most SaaS marketing pages) scrape at the base rate. Defensive targets (Google, LinkedIn, Amazon, anything behind Cloudflare's bot management) price at a multiplier or refuse anonymous traffic outright, which is precisely why ScraperAPI can charge 25 credits for what costs 1 credit elsewhere. And the middle band moves: a target that cost 1 credit last quarter can sit behind Datadome this quarter, and your unit economics change without a line item changing on any invoice. A search API's own index absorbs that volatility; a scraping pipeline hands it to you.
On the ground rules: respect robots.txt where it exists, prefer APIs whose vendors take the legal position themselves (SerpAPI sells a U.S. legal shield; Brave and Keirolabs operate licensed, owned pipelines), and keep an audit log of which URL produced which claim. An agent that cites its sources can be audited. An agent that paraphrases a snippet cannot.
> Grounding policy is easier to enforce with a URL list than with a query. It is also 10x more work. Pick per workload, not per ideology.
## AI Search API vs Building My Own Crawler and Index Whats the ROI
One of the more honest queries we see in search console is people weighing a search API against building the crawler and index themselves. Here is the ledger, with as few adjectives as we can manage.
Building means owning all of this: a fetch frontier with politeness and retries, proxy and browser infrastructure for the defensive targets, robots.txt handling, HTML-to-text extraction quality, deduplication and storage, an index or embedding store with a re-crawl cadence, and the operational reality that sites change layouts at inconvenient hours. Tools like Crawl4AI make the software free and open source ([the repo](https://github.com/unclecode/crawl4ai)); nothing on that list goes away. You inherit it as an ops burden.
The buy side, priced from the tables above: a full hybrid answer on Keiro's Startup plan is $2.40 per 1,000 tasks, so 100,000 grounded tasks a month costs $240, or $2,880 a year. A hybrid on Firecrawl's Standard plan is $415 a month. Building only wins on money when your volume is high enough that the ops team is already paid for, your target set is small and stable, or your data residency rules rule out shared infrastructure. A fixed intranet of 200 domains with a compliance mandate: build, obviously. An open-web research agent: the crawler is a second product, and it will not be your product.
The ROI question resolves into three honest buckets. If your workload is open-web discovery at agent volume, a search API wins because discovery is the hard part and it is already built. If your workload is a fixed source list with strict residency or quality control, building (or Crawl4AI plus a proxy pool) wins because the vendor's index never had your corpus anyway. And if your workload is "some of both," which is most teams, buy both layers from someone whose bill you can read, and spend the engineering time on the agent, which is where your differentiation actually lives.
One more number for the build case, in our own favor and against it: Keiro's Startup plan is $100 a month, $1,200 a year. One week of an engineer's time spent maintaining a proxy fleet costs more than that. If your team is genuinely at zero marginal engineering cost, ignore this paragraph. Most teams are not.
## Web Data API vs Scraping for Research Agents
Deep research agents, the ones that answer a question by reading many sources and synthesizing, are where the search-versus-scrape split shows up as an accuracy number rather than an opinion.
The SimpleQA build-up on our 1,000-question run: SERP snippets alone scored 72.4%. Adding full-page fetches lifted the same pipeline to 87.7%. The full deep search stack, which adds candidate judging, reached 95.3% judged retrieval accuracy with no reader model, on a deliberately strict check that gives no credit for almost ([full methodology and third-party leaderboard](https://keirolabs.cloud/Deep-search)). For calibration against other vendors' published SimpleQA runs: Firecrawl 94.7, Tavily 93.3, you.com 92.1, Exa 91.9, Parallel 91.0, Perplexity 85.9, Google 82.2, Brave 76.1.
The number that should change how you build research agents is the first gap, not the last: 15.3 points of accuracy came from reading the pages instead of the snippets. No re-ranking, no better model, no prompt engineering produced that gain. The scraping layer did. That is why a research agent wired to a snippet-only search API hits a quality ceiling that no amount of tuning breaks, and why "web data API" vendors that bundle search with extraction (Tavily, Firecrawl, Keiro) keep showing up well in agent benchmarks.
What does the bundled version cost? Keiro's deep search bills $4.44 per 1,000 requests at list pricing, one meter for the whole pipeline: live-web resolution, full-page reads, and scoring ([the benchmark page](https://keirolabs.cloud/Deep-search)). The unbundled version of the same pipeline costs whatever your two meters sum to: Brave's $5.00 per 1,000 searches plus roughly $2.49 in Firecrawl page reads for a 3-page read is $7.49 per 1,000, and Exa's search plus contents lands near $10.00 per 1,000. Exa's dedicated deep research endpoint runs $12.00 to $15.00 per 1,000 ([Exa docs](https://docs.exa.ai/reference/pricing)), Parallel's task tiers run $0.005 to $2.40 per request ([Parallel pricing](https://parallel.ai/pricing)), and Linkup's deep research runs $0.25 to $2.50 per request ([Linkup pricing](https://www.linkup.so/pricing)).
Two caveats we owe you. The 95.3% is a retrieval check: the gold answer had to appear in the returned data. It is not an end-to-end QA score with a reader model on top, and the page explains the judging in detail. And benchmark scores transfer imperfectly; your queries are the eval that matters, which is the one piece of advice in this post that survives every repricing.
## Search API for RAG Agent Cost Comparison
RAG splits the decision cleanly, because the architecture you need depends on where the documents live.
**Corpus you own (docs site, help center, intranet, PDFs):** you do not need a search API at all. You need extraction, run once or on a schedule, into your vector store. The cost line is page credits: ScrapingBee at $0.20 per 1,000 standard pages, Firecrawl at $0.83 to $0.99, Keiro's /extract at $2.40 to $7.20, Tavily extract at $8.00. A 10,000-page corpus, re-crawled monthly, costs $2 to $80 a month depending on the vendor and the page types. The search API's ranking layer adds nothing here; the retrieval is yours.
**Open-web RAG (answers grounded in whatever the web ranks):** this is the hybrid, and the cost model from the earlier section is the decision. Snippet-only RAG is cheap and shallow, and on FinanceBench, where questions have financial numbers attached, thin snippets are exactly what falls apart: Keiro 78%, Valyu 73%, Parallel 67%, Exa 63%, Google 55%. Full-page RAG on Keiro's /search/content costs $2.40 per 1,000 tasks on Startup, content included, and the endpoint returns markdown built for chunking rather than HTML needing cleanup.
**Hybrid corpus (owned docs plus web citations):** both layers, with the web side capped by a domain allowlist and a per-task read budget. This is the architecture in the code section above.
Two token notes for RAG specifically. First, chunking happens downstream of the API, and the API's output format decides how much of your token budget survives: markdown with headings maps to chunks cleanly, raw HTML burns tokens on markup. Firecrawl, Tavily, and Keiro all return markdown by default for this reason. Second, the payload you embed does not need the model at all, so extraction-only workloads can run without any LLM in the loop, which is why the per-page price is the number that matters there and the per-search price is the number that matters everywhere else.
| RAG shape | Architecture | Stack to price | Cost per 100k tasks |
|---|---|---|---|
| Owned corpus, monthly refresh | Extraction only | ScrapingBee or Firecrawl Standard | $20-$83 |
| Open web, snippet-first | Search only | Keiro /search/lite (Startup) | $8 |
| Open web, full-page grounding | One-call hybrid | Keiro /search/content (Startup) | $240 |
| Open web, full-page, two vendors | Search + scrape | Serper + Firecrawl | $349 |
| Open web, premium bundle | Bundled advanced | Tavily advanced | $1,600 |
## What Is the Most Accurate Search API for AI Agents in 2026?
Accuracy depends on which scoreboard you trust, so here are the public ones. On AIMultiple's 2026 agentic search benchmark (score out of 20): Keirolabs 15.2, Brave 14.89, Firecrawl 14.58, Exa 14.39, Tavily 13.67, Perplexity 12.96 ([aimultiple.com](https://aimultiple.com)). On our own 100-query harness: SimpleQA Keiro 94, Perplexity 86, Tavily 78; HotpotQA 82, 74, 68 ([methodology](https://keirolabs.cloud/blogs/comparisons/top-8-ai-search-apis-compared-2026)). And in a head-to-head against Parallel's turbo tier, /search/lite won 91 of 100 queries with a composite of 341.6 to 170.4 ([the full run](https://keirolabs.cloud/bench/keiro-lite-vs-parallel-turbo)).
We build Keirolabs, so weight that disclosure however you like; the links above are the receipts either way.
The decision, workload by workload:
| Workload | Right architecture | Cheapest honest stack | Why |
|---|---|---|---|
| RAG over open web | Hybrid: search, then read top 3 | Keiro /search/content, $240/100k | Snippets ceiling at 72.4% on SimpleQA; pages add 15.3 points |
| RAG over owned corpus | Extraction only, no search | ScrapingBee or Firecrawl | You already know the URLs; ranking adds cost, not recall |
| Monitoring fixed URLs | Scraping on a schedule | Firecrawl Monitor or ScrapingBee | Freshness of known pages is a fetch, not a query |
| Monitoring a topic | Search, snippets first | Keiro /search/lite at $0.08-$0.25/1k | The result set changes; the index finds what is new |
| Deep research | Bundled deep search | Keiro deep search, $4.44/1k, 95.3% SimpleQA | One meter for resolve, read, and judge |
| Lead gen / enrichment | Scraping with a search kick | ScrapingBee ($0.20/1k) + Keiro lite discovery | Lists and profiles are known URLs; discovery finds them once |
| SERP data / SEO tooling | SERP scraper | Serper $1.00/1k, SerpAPI $15.00 with legal shield | You want Google's ranking itself, with its risks |
And the failure modes, one line each, because they decide more deployments than the pricing tables do. Search-only pipelines hallucinate plausible summaries off truncated snippets, and the 72.4% number is what that looks like measured. Scrape-only pipelines starve: they never discover a source they were not handed. Hybrid pipelines burn money reading pages the answer did not need, which is why the read budget belongs in code, not in the vendor's dashboard. And every pipeline that skips the cache pays twice for the same page, at both meters.
The short version, and the honest one: buy search to find and scraping to read, price both layers per 1,000 before you write the integration, and treat any vendor (including us) whose cheapest-looking number is the one they publish first with the same suspicion you would apply to a snippet that answers your question a little too neatly.
> A search API returns what the index remembers. A scraping API returns what the page says today. Agents that answer with money attached need the second one, and they need the first to find it.
Every price and benchmark in this post links to its source, and every source was checked on September 23, 2026. Prices change; check them again before you sign anything, including with Keirolabs.