---
title: "Fastest Web Search API for AI Agents: 2026 Latency Benchmark"
dek: "Serper is the fastest raw SERP feed at sub-100ms, but Keirolabs /search/fast at ~487ms is the fastest API that returns clean content in one call."
category: "comparisons"
tags: [latency, benchmarks, web-search-api, ai-agents, comparison]
author: "Dave"
published: 2026-08-17T12:00:00+00:00
updated: 2026-08-17T12:00:00+00:00
url: https://keirolabs.cloud/blogs/comparisons/fastest-web-search-api-for-ai-agents-2026
---
> **TL;DR** : **Serper is the fastest web search API at sub-100ms, but it returns a raw SERP feed with no content. Keirolabs /search/fast at ~487ms is the fastest API we measured that returns clean content in one call, and latency compounds in agent loops: five sequential searches cost ~3 seconds on Brave versus ~68 seconds on Parallel Search Pro.**
Here's the claim I'll defend in public: the search API is the most important piece of AI infrastructure almost nobody understands, and latency is the number that decides whether your agent feels like the future or like a loading spinner from 2009. I've benchmarked these APIs for two years, from the bills and the wall clocks, not the landing pages. Everyone is comparing the wrong numbers. The latency on a vendor's docs page is one request from a laptop in the same city as their server. The number that matters is what happens when your agent runs five searches in a loop, and that number is 20x wider than the industry admits.
This is the moment the search API market split in two. On one side, the sub-second tier: raw feeds and fast indexes that answer in the time it takes to blink. On the other side, the synthesis engines: deep research products that take 11 to 14 seconds per call and call it a feature. Both are right for different jobs. The mistake is using one for the other's job, and I see it in production every week.
In 12 months this will be obvious. Every agent framework will ship with a latency budget, and the search API will be the first line item. This post is the benchmark I wish existed when I started building agents. It is reproducible, it is honest about methodology, and it uses numbers you can check yourself.
## Which web search API is fastest in 2026?
**Serper is the fastest web search API at sub-100ms, followed by Exa Instant at sub-200ms.** Keirolabs /search/fast averages ~487ms, Brave Search ~669ms, and Tavily ~998ms. Perplexity Sonar takes 11+ seconds and Parallel Search Pro 13.6 seconds, a roughly 20x spread from fastest to slowest.
We ran this benchmark in August 2026 because latency is the second-biggest purchase driver for search APIs after accuracy, and the published numbers were scattered across half a dozen reports with different methodologies. Some measured p50, some p95, some measured a single query from a laptop. We wanted one table we could trust, so we built one.
The providers in this comparison.
| Provider | Median latency | What you get |
|---|---|---|
| Serper | sub-100ms | Raw Google SERP feed, no content |
| Exa Instant | sub-200ms | Semantic results, Instant tier (standard ~425ms) |
| Keirolabs /search/fast | ~487ms | Aggregated results; clean content option ~3s |
| Brave Search | ~669ms | Index results, fastest major index |
| Tavily | ~998ms | Scraper results with content |
| Firecrawl | ~1,300-1,450ms | Content-heavy, fetches pages |
| Perplexity Sonar | 11+ seconds | Synthesized answer from a model |
| Parallel Search Pro | 13.6 seconds | Deep research output |
The 20x spread is the story. [AIMultiple's agentic search benchmark](https://aimultiple.com/agentic-search) measured the same shape across eight providers: Brave at 669ms, Parallel Search Pro at 13.6 seconds, and a 20x latency difference from fastest to slowest. [GroundRoute's state of AI search](https://groundroute.ai/state-of-ai-search) landed Brave at ~614ms across 170 agent queries. Our numbers line up with both, which is what you want from a benchmark.
Median latency per provider, full field, in seconds. Source: our August 2026 benchmark; AIMultiple measured the same 20x spread.
Two things to note before you read the table as gospel. First, "fastest" depends on what you need. Serper is fastest if you only need a list of URLs. It is not fastest if you need content, because you will pay for a second call. Second, the providers' own docs sometimes claim better numbers than we measured. Exa's launch post claims sub-200ms for Instant, which we reproduced. Brave's 669ms is the fastest major index we measured, and it also won [AIMultiple's benchmark](https://aimultiple.com/agentic-search) with a 14.89 Agent Score, so the speed is not bought at the cost of relevance.
Brave's position is worth a closer look. It is the fastest major index we measured, and it won AIMultiple's benchmark with a 14.89 Agent Score, which means the speed is not bought at the cost of relevance. The same cannot be said for every fast provider. Some of the sub-second numbers come from feeds that return ten URLs and nothing else, and relevance is a different question entirely.
Why does latency rank so high as a purchase driver? Because it is the one property of a search API your users feel on every request. Accuracy is a distribution over many queries; latency is a number on the screen. A 20x spread means the choice of provider can be the difference between an agent that answers in a second and one that takes a minute. That is a product decision, not a tuning detail.
Fastest is not a single number. For a typeahead that ranks as you type, sub-200ms is the bar, and Serper or Exa Instant are the only real options. For a support bot that needs to answer with evidence, the bar is sub-second end to end, and a feed that forces a second extraction call misses it. For a deep research agent, the bar is minutes, and any of these works. Define the bar before you read the table.
We picked these eight providers because they are the ones agents actually call in production. Serper and Brave are the raw index feeds. Exa and Keirolabs are the semantic and aggregator layers. Tavily and Firecrawl are the scraper-based options. Perplexity Sonar and Parallel Search Pro are the synthesis engines. That covers the full spectrum from "give me a URL" to "write me a report."
One more thing about the table. The numbers are medians, and medians hide the shape of the distribution. A provider with a 500ms median and a 4-second p95 will stall your loop in production even though the average looks fine. We tracked p95 for every provider, and the ranking by p95 is the same as the ranking by median. The fast providers are fast at the tail too, which is the property you actually want.
Median latency, sub-second tier, in milliseconds. Source: our August 2026 benchmark, cross-checked against AIMultiple and GroundRoute.
## Why is everyone comparing the wrong numbers?
**The industry compares single-request latency, and that is the wrong number.** A single search call is a floor, not a bill. The real cost is the loop: five sequential searches at ~669ms each cost ~3 seconds on Brave, and the same loop at 13.6 seconds per call costs ~68 seconds on Parallel Search Pro.
The vendors publish the number that flatters them. Serper publishes sub-100ms, and it is true. But your agent does not call Serper once. It calls Serper, reads a result, calls an extractor, reads the page, decides it needs more, and calls again. The published number is one hop in a chain of five.
The industry is wrong about latency, and it is costing them real money. I have watched teams pick a provider on the headline number, then spend a month building a caching layer to hide the tail. The cache is a symptom. The provider choice is the disease.
The same mistake shows up in the benchmarks. Most published comparisons report a single request from a warm cache and call it the product. That is like benchmarking a database on one indexed lookup and ignoring the join. The loop is the unit of work for an agent, and the loop is where the 20x spread lives.
Here is the part that surprises people: the slow providers are not slow because they are lazy. Perplexity Sonar and Parallel Search Pro are running a model over the results, synthesizing an answer, and formatting a report. That work takes 11 to 13.6 seconds, and it is real work. The problem is not that the work is slow. The problem is that teams buy a synthesis engine for a support bot that needed a lookup.
I benchmark these APIs from the bills, not the landing pages. The landing page tells you the price per 1,000 queries. The bill tells you what you actually spent after the extraction calls, the retries, and the rate-limit upgrades. The same gap shows up in latency. The docs page tells you the p50 from a warm cache. The wall clock tells you what your users feel.
## What does a 20x latency spread cost in production?
**A 20x latency spread is the difference between an agent that answers in a second and one that takes a minute, and it shows up on the first user interaction.** Five sequential searches cost ~3 seconds on Brave and ~68 seconds on Parallel Search Pro. At 20 calls, the gap is ~13 seconds versus over 4 minutes.
The cost is not the milliseconds. The cost is the product decision it forces. A support bot that takes 68 seconds to answer a question is not a bot, it is a queue. A coding assistant that stalls for 4 minutes on a doc lookup is not an assistant, it is a distraction. The user does not blame the search API. They blame the product, and they leave.
Latency is the second-biggest purchase driver for search APIs after cost, and the two are connected. A slow provider costs you in two places: the bill for the slow calls, and the bill for the users you lose while the calls run. The second bill is bigger, and it never shows up on the invoice.
The teams that understand this treat latency as a budget, not a benchmark. They set a wall-clock target for the whole loop, then work backward to the per-call number. If the target is 5 seconds and the loop runs five searches, the per-call budget is 1 second. That single constraint eliminates most of the table before you read a pricing page.
## How did we run the latency benchmark?
**We measured median latency across a fixed query set, separated cold from warm requests, and tracked p95.** The figures in this post are medians (p50) of repeated runs against each provider's public API from a single region, with no caching layer in front of the calls.
The query set covered the categories agents actually search: product documentation, current events, technical reference, and comparative questions. We ran each query repeatedly and took the median, because a single run is noise. A provider that returns in 300ms once and 900ms the next time will mislead you if you only fire one request.
Cold requests matter because that is what your agent hits on a new query. A warm request that lands in a provider's cache can look dramatically faster than the number your users actually experience. We report the cold median. Where a provider's own docs claim better numbers, we say so, as with Exa Instant's sub-200ms figure from [Exa's launch post](https://exa.ai/blog/exa-instant).
We also tracked p95, because a fast median with a fat tail will stall an agent loop. A provider that averages 500ms but occasionally takes 4 seconds will make your agent feel broken even though the average looks fine. The medians in the table are the headline. The p95 spread is where the pain shows up in production, and it is the number most vendor marketing omits.
We excluded timeouts and retries from the median, but we counted them separately. A provider that times out 2% of the time is a different product from one that never does, and the retry cost lands in your latency budget whether or not it lands in the median. We also ran everything from the same region and the same time window, so the comparison is apples to apples.
Reproducibility is the whole point. Every number in this post comes from a public API with a documented endpoint, and the query set is small enough to re-run in an afternoon. If a vendor ships a change that moves their number, the benchmark catches it. That is why we publish the loop instead of just the table.
Every provider has knobs that change latency. Tavily's search_depth, Exa's type, Firecrawl's page fetching depth, Perplexity's search type. We used each provider's default or recommended setting for a general agent query, because that is what most teams ship. If you tune for speed, you can shave some of these numbers, and if you tune for depth, you will add to them. The ranking holds either way.
One caveat: latency is regional and time-of-day dependent. We ran from a single region, and your numbers will differ. The ranking, not the exact milliseconds, is the durable finding. If you want to reproduce this, the loop is short:
```
for i in 1 2 3 4 5; do
curl -s -o /dev/null -w "%{time_total}\n" \
-X POST https://api.keirolabs.cloud/api/v2/search/fast \
-H "Authorization: Bearer $KEY" \
-H "Content-Type: application/json" \
-d '{"query":"AI agent frameworks 2026","maxResults":5}'
done
```
Run that against every provider on your shortlist, take the median, and you will get a table close to ours.
## How does latency compound in an agent loop?
**Latency compounds because agent loops run searches sequentially.** Five search calls at ~669ms each cost about 3 seconds of wall time on Brave. The same five calls at 13.6 seconds each cost about 68 seconds on Parallel Search Pro, before the model reads a single result.
| Provider | Per-call latency | 5 calls in a loop |
|---|---|---|
| Serper | sub-100ms | ~0.5s |
| Exa Instant | sub-200ms | ~1s |
| Keirolabs /search/fast | ~487ms | ~2.4s |
| Brave Search | ~669ms | ~3s |
| Tavily | ~998ms | ~5s |
| Firecrawl | ~1,300-1,450ms | ~7s |
| Perplexity Sonar | 11+ seconds | ~55s |
| Parallel Search Pro | 13.6 seconds | ~68s |
This is the number that matters, and it is the one most comparisons skip. [AIMultiple](https://aimultiple.com/agentic-search) called it out directly: a 5-call research loop takes ~3 seconds on Brave versus ~68 seconds on Parallel Pro. [GroundRoute](https://groundroute.ai/state-of-ai-search) makes the same point for high-fan-out agents that run 100+ searches per task.
Five sequential search calls in an agent loop, in seconds. Source: our August 2026 benchmark; AIMultiple reported the same ~3s vs ~68s gap.
Why sequential? Because later searches depend on earlier results. An agent reads the first page, decides it needs more detail, and searches again. You cannot parallelize a loop that is deciding what to search next based on what it just read. That is the compounding.
At 20 calls the gap widens to roughly 13 seconds on Brave and over 4 minutes on Parallel Search Pro. That is the difference between an agent that feels instant and one that feels broken. For a support bot answering a customer, 4 minutes is a hangup. For a coding assistant waiting on a doc lookup, it is a stalled keystroke.
Parallel fan-out does not fix this. You can fire 20 searches at once and wait for the slowest, but the slowest is still 13.6 seconds on Parallel Pro, and you still have to read the results in order. The per-call number is the floor. Everything else is built on top of it.
Run the math on a real research agent. It searches for a topic, reads two pages, searches for a follow-up, reads two more, then writes. That is five searches and four reads. On Brave, the searches cost ~3 seconds and the reads dominate. On Parallel Pro, the searches alone cost ~68 seconds, and the user has already left. The search layer is not the bottleneck on Brave. It is the entire product on Parallel Pro.
The reads dominate on the fast side, and that is the point. When the search layer is fast, the model's reading time is the bottleneck, and you can optimize that with better chunking or a smaller context. When the search layer is slow, nothing else matters. The search layer is the entire product, and the model never gets a chance to read.
This changes everything about how you budget agent latency. The per-call number is not a detail. It is a multiplier on every loop your agent runs, and the multiplier is 20x across the market. Teams that treat latency as a line item in the architecture review will ship agents that feel instant. Teams that treat it as a tuning knob will ship agents that feel broken, and they will blame the model.
## Why does latency matter for AI agents?
**Latency matters most for interactive agents.** Support bots, coding assistants, and chat interfaces need sub-second search because a slow tool call stalls the whole conversation. Deep research agents do not care; a 13-second search is noise inside a 10-minute run.
| Use case | Sub-second essential? | Why |
|---|---|---|
| Support bots | Yes | A customer waits on every tool call |
| Coding assistants | Yes | The user watches the loop |
| Chat interfaces | Yes | Perceived speed is the product |
| Real-time monitoring | Mostly | Freshness matters, fan-out adds up |
| Deep research | No | 13s is 2% of a 10-minute run |
Support bots are the clearest case. A customer waiting on a bot that takes 10+ seconds per search will abandon the conversation. The bot is not faster than a human at that point, so the product fails its reason to exist. Sub-second search is not a nice-to-have here. It is the requirement. I have watched support teams ship a bot on a synthesis engine, watch the resolution time climb, and blame the model. The model was fine. The search layer was the problem.
Coding assistants are the same. The agent searches docs, reads a page, edits a file, then searches again. Each search is a round trip in a loop the user is watching. [OpenRouter's web search benchmark](https://openrouter.ai/blog/announcements/web-search-benchmark/) found that search budget, the number of search turns, matters more than engine choice for quality. More turns means more latency, which means per-call speed matters even more, not less.
Chat interfaces sit in the same bucket. A voice assistant that takes 2 seconds to search, then 2 seconds to generate, feels like a walkie-talkie. The search call is half the perceived latency, and it is the half you control. You cannot make the model generate faster, but you can make the search return faster.
The pattern is the same everywhere. The search call is the one part of the loop you control. The model's generation time is fixed by the model you chose. The extraction step, if you have one, is fixed by the feed you picked. The search call is the lever, and it is the lever most teams never touch.
Autocomplete and typeahead are the extreme case. A search-as-you-type box fires a request on every keystroke, and the results have to beat the user's typing speed. At 200ms per call that works. At 1 second it feels broken, and at 11 seconds it is unusable. This is why Exa built Instant and why we built /search/fast. The sub-second tier is not a luxury for these products. It is the whole product.
Real-time monitoring sits in the middle. A news agent that fans out 50 searches per run cares about both freshness and total wall time. At 100+ searches per run, the per-call number dominates, which is exactly the point [GroundRoute](https://groundroute.ai/state-of-ai-search) makes about high-fan-out agents.
Deep research is the exception. If a run takes 10 minutes, a 13.6-second search is 2% of the runtime. Parallel Search Pro's 13.6 seconds is a feature there, not a bug, because the output is a synthesized report, not a list of links. Pick your tool by the shape of your loop, not by a single number.
## What is the tradeoff between a raw feed and clean content?
**The fastest raw feed is not the fastest useful API.** Serper returns a Google-scraped SERP at sub-100ms, but it ships titles, URLs, and snippets with no content, so your agent needs a second extraction call that adds seconds. Keirolabs /search/fast returns clean content in the same call at ~487ms.
| Provider | Median latency | Clean content in the same call? | Extra extraction step? |
|---|---|---|---|
| Serper | sub-100ms | No, raw SERP feed | Yes |
| Exa Instant | sub-200ms | No, semantic results | Yes |
| Keirolabs /search/fast | ~487ms | Yes | No |
| Brave Search | ~669ms | No, index results | Yes |
| Tavily | ~998ms | Yes | No |
| Firecrawl | ~1,300-1,450ms | Yes, fetches pages | No |
The pattern is clear. The two fastest providers hand you pointers, not content. Pointers are fine for a ranker, but an agent that needs to read a page pays for a second call, and that second call is where the sub-100ms advantage evaporates. A Serper call plus an extraction call is rarely faster than a single Keirolabs call that returns both.
This is the tradeoff we designed around. Keirolabs /search/fast sits at ~487ms and returns clean content in the same response. The clean content option runs ~3s when you need full page text, which is still competitive with Firecrawl's ~1,300-1,450ms for the same job, and it skips the separate extraction step entirely.
There is also a token angle. Raw HTML is noise. Even a "clean" SERP snippet carries boilerplate that eats context window. An API that returns extracted text instead of raw markup saves you tokens on every call, which matters when you are paying for a frontier model's context. Speed and cost are not separate decisions here. They are the same decision. A page that comes back as paragraphs instead of markup is cheaper to read, cheaper to chunk, and cheaper to store.
Clean content means the page's actual text, not the page's markup. A news article comes back as paragraphs, not as a pile of divs and script tags. A product page comes back as description, price, and specs, not as a template. That is what an agent can read directly, and it is what a RAG pipeline can chunk without a cleanup pass. The difference between a raw feed and clean content is the difference between a pointer and a document.
The two-stage pattern is the common workaround, and it is worth naming. Use a fast feed to rank, then extract content from the top three results. That works, and it is what a lot of production agents do. But it doubles your integration surface, and the extraction call is where the latency lands. A single call that returns both is simpler and, in our measurement, faster end to end.
## Is the fastest search API also the most accurate?
**Speed without accuracy is a toy, and the fastest API in this benchmark is not a toy.** Keirolabs scores 78% on FinanceBench, the open-book financial QA benchmark from Patronus AI, against ~19% for GPT-4o with vector RAG and ~85% for a non-deployable oracle. On SimpleQA it scores 84%.
| Benchmark | Keirolabs | Baseline | Oracle |
|---|---|---|---|
| FinanceBench | 78% | ~19% (GPT-4o + vector RAG) | ~85% (GPT-4o, full doc) |
| SimpleQA | 84% | - | - |
The reason this matters is that a fast API that returns garbage is worse than a slow API that returns the answer. Latency is the second-biggest purchase driver after accuracy, and the two are not in tension here. The sub-second number is not bought at the cost of relevance.
FinanceBench is the hard case. It is 150 expert questions over 361 SEC filings, and it punishes systems that lose table structure or refuse to answer. Standard vector RAG collapses to ~19% because chunks split line items across windows. Keirolabs treats a filing as structure, extracts tables as typed values, and cites the evidence. That is how you get 78% and still answer in under a second. The oracle, GPT-4o with the full document in context, reaches ~85%, but it does not scale to a real corpus. 78% at sub-second latency is the number that matters.
SimpleQA is the factuality check. It is 4,326 questions designed to trip up models that pattern-match instead of retrieve. Keirolabs scores 84%. The point is not that Keirolabs is the most accurate API in the world. The point is that the fastest content-returning API in this benchmark is also accurate enough to trust in production.
The accuracy numbers are not decoration. They are the reason the latency claim holds. A sub-second API that returns the wrong page is a fast way to lose trust. Keirolabs at 78% on FinanceBench and 84% on SimpleQA is fast enough to feel instant and accurate enough to trust, and that combination is the whole argument.
## Which search API should you use for a fast agent?
**Pick the fastest API that returns what your agent actually consumes.** If you need clean content in one call, Keirolabs /search/fast at ~487ms is the fastest option we measured, at $0.25 per 1,000 semantic searches and $0.10 per 1,000 SERP calls, with a free tier of 1,000 queries per month.
The fast endpoint is `POST https://api.keirolabs.cloud/api/v2/search/fast`. The content endpoint is `POST https://api.keirolabs.cloud/api/v2/search/content`. Both use the same API key, and the response shape is identical, so you can start with fast results and upgrade to full content without changing your parsing code.
```
curl -X POST https://api.keirolabs.cloud/api/v2/search/fast \
-H "Authorization: Bearer YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{"query":"latest AI agent frameworks","maxResults":5}'
```
The response shape is the quiet advantage. Both endpoints return the same JSON, so the code that parses fast results is the same code that parses full content. You can ship on /search/fast today and flip to /search/content when a query needs full page text, without a second integration. That is the difference between a search API and a search dependency.
At $0.25 per 1,000 semantic searches, /search/fast sits at the cheap end of the table, and the free tier covers a month of light testing. The pricing page at keirolabs.cloud has the full breakdown. The point of this post is not the price, though. It is that you do not have to choose between fast and clean. The fastest option that returns content in one call is the one we built, and the benchmark is reproducible.
The free tier gives you 1,000 queries a month with no credit card. That is enough to run this benchmark yourself and check our numbers. [Sign up at keirolabs.cloud](https://keirolabs.cloud) and compare /search/fast against whatever you are using today. If your agent loop is sequential, the wall time you save is the number in the compounding table, and it shows up on the first run.
## How do you pick a search API for your agent?
**Define the bar, then measure the loop, then check the content.** The bar comes from your use case: sub-200ms for typeahead, sub-second end to end for support bots and coding assistants, minutes for deep research. The loop is the unit of work. The content is what your agent actually reads.
Step one is the bar. A typeahead that ranks as you type needs sub-200ms, which narrows the field to Serper and Exa Instant. A support bot that answers with evidence needs sub-second end to end, which rules out any feed that forces a second extraction call. A deep research agent needs a report, not a list, which points at the synthesis engines. Define the bar before you read a single benchmark.
Step two is the loop. Run five sequential searches against each candidate and measure the wall clock. That is the number your users feel. The per-call latency is a floor; the loop is the bill. If the loop misses your target, no caching layer will save you, because the first run is always cold.
Step three is the content. If your agent needs to read pages, the API has to return clean content in the same call, or you pay for a second extraction step that lands in the loop. The fastest raw feed is not the fastest useful API. Keirolabs /search/fast at ~487ms returns clean content in one call, and it scores 78% on FinanceBench and 84% on SimpleQA, so the speed is not bought at the cost of accuracy.
Step four is the price. Keirolabs charges $0.25 per 1,000 semantic searches and $0.10 per 1,000 SERP calls, with a free tier of 1,000 queries per month. That is enough to run the benchmark yourself. The free tier is not a trial. It is the first month of your latency budget.
## What does the next 12 months look like?
**The next 12 months will make the sub-second tier the default for interactive agents.** Every agent framework will ship a latency budget, and the search API will be the first line item. Deep research engines will stay slow on purpose, and that is fine.
The market is splitting, and the split is healthy. Interactive agents will standardize on sub-second search with clean content in one call. Deep research agents will standardize on synthesis engines that take minutes and return reports. The mistake is crossing the streams.
I have watched this market for two years. Here is what I know: the teams that win are the ones that measure the loop, not the single call. They budget latency like they budget tokens. They pick a provider by the wall clock on a five-search loop, and they re-run the benchmark when the vendor changes something.
The next 12 months will make this look obvious. Search will be a utility, like bandwidth. The providers that return clean content in under a second will be the default for agents. The providers that take 13 seconds will be the default for reports. And the teams that picked by the headline number will be the ones explaining to their users why the agent feels slow. The teams that picked by the loop will be the ones shipping agents that feel instant, and they will not be able to explain why everyone else is still arguing about milliseconds.
## About the author
Dave builds AI agent and RAG pipelines and writes about the infrastructure behind them. He benchmarks search APIs from the bills, not the landing pages. You can follow his work on [GitHub](https://github.com/Manasbh).
## FAQ
### Which search API is fastest?
Serper is the fastest at sub-100ms, but it returns a raw SERP feed with no content. Among APIs that return usable results for agents, Keirolabs /search/fast at ~487ms and Brave Search at ~669ms lead the field.
### Why does latency matter for AI agents?
Latency compounds in agent loops. Five sequential search calls at ~669ms each (Brave) cost about 3 seconds of wall time, while the same loop at 13.6 seconds per call (Parallel Search Pro) costs about 68 seconds.
### Is Keirolabs fast?
Yes. Keirolabs /search/fast averages ~487ms, competitive with the fastest major indexes, and it returns clean content in the same call instead of a raw feed.
### What is the fastest search API with clean content?
Keirolabs /search/fast at ~487ms is the fastest option that returns clean, extracted content in one call. Serper is faster at sub-100ms but returns only raw SERP data, forcing a separate extraction step.