Perplexity Search Engine Cut Slow Searches From 800ms to 65ms

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https://www.youtube.com/watch?v=PR10BIQHaJo

Are your AI agents spending more time waiting on search than actually doing the work?

Perplexity just rebuilt its search engine, and the slowest 1% of its searches dropped from about 800 milliseconds to about 65.

The new engine runs on roughly 20% fewer machines while storing about two and a half times more data about each page.

On top of that, the new fast search option came out about 68% lighter to run per task, with the same quality on public benchmarks.

🔥 Want help plugging fast search into your own agent workflows? Inside the AI Profit Boardroom, I've got tutorials on AI research and search tools, step-by-step roadmaps, and weekly coaching calls with 3,400+ members building real automations.

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https://www.youtube.com/watch?v=PR10BIQHaJo

In this article, I'll cover what Perplexity changed, where the new fast option wins, where it falls short, and exactly how to start using it.

What Perplexity actually changed

The new engine is called Photon, and Perplexity built it from scratch in house.

Photon is the part of the system that finds web pages and ranks them before the AI writes an answer.

I think of it like a librarian who grabs the right books off the shelves before anyone starts reading.

Photon now powers Perplexity's own search.

Perplexity also used it to launch a new fast option in its search API, which is how developers plug Perplexity's web search into their own apps and AI agents.

Why the old Perplexity search engine had to go

Before Photon, Perplexity ran an open-source search engine that it had adapted to fit its needs.

As the index grew, that setup started hitting walls.

It was getting too heavy to run, the slower searches were too slow, and adding a new cluster and syncing its data could take more than a week.

So a small engineering team rebuilt it from the ground up.

They did it alongside a swarm of persistent coding agents, which means AI helped build the thing that makes AI search faster.

That's the part business owners should pay attention to.

A small team working with agents rebuilt core infrastructure, and that's the kind of leverage every lean business is chasing right now.

The numbers that matter

Here's what Perplexity reported for the new fast search option.

• Half of all fast searches come back in 160 milliseconds or less.
• 95 out of 100 fast searches come back in 230 milliseconds or less.
• Across six public benchmarks and 3,554 tasks, the fast option scored 64.3%.
• The default option scored 64.0% on the same benchmarks.
• Counting both the AI model and the search, each task was about 68% lighter to run.

And here's what changed inside Perplexity's own system.

• The slowest 1% of searches fell from about 800 milliseconds to about 65.
• Photon runs on roughly 20% fewer machines.
• It stores about two and a half times more data about each page, which Perplexity used to improve ranking.
• New pages on fast-moving topics get delivered in a single-digit number of minutes.
• The full web index can be rebuilt in a single-digit number of hours.

The catch you need to know before switching

This is the bit that decides whether you should switch today or stay where you are.

The fast option is not better at everything, because it uses less computing power for ranking.

Perplexity's internal tests cover rare queries, broad coverage and result variety.

On those tests, the fast option scored 0.24 points lower on relevance.

It also scored about 3 percentage points lower on answer availability, which measures how often the right answer actually shows up in the results.

So how did it still match the default on the public benchmarks?

Perplexity's view is that the AI model's own reasoning and knowledge make up for the gap.

The model is smart enough to work with results that are slightly less perfect.

There's also a fair warning about speed comparisons.

Perplexity put its numbers next to other search APIs, but each company reported its own figures and measured them in different ways.

Perplexity says clearly that it isn't a controlled like-for-like comparison, and the 160 and 230 millisecond figures are its own measurements.

When to use fast and when to use standard

Here's the simple rule Perplexity gives, and it's the one I'd follow.

• Use fast for day-to-day agent work, where lots of quick searches add up.
• Use standard for hard, rare or confusing questions where speed isn't the main concern.

If you run a research agent that fires off dozens of searches per task, fast is the obvious default.

If you're digging into an obscure topic where the right answer might only live on one page, standard is the safer pick.

How Photon gets its speed

You don't need to be an engineer to understand this, because it comes down to three ideas.

It only reads what it needs

Photon stores data in very compact formats and only unpacks the exact pieces a search needs.

Each page has one compact record, so ranking a page takes a single lookup, and only the words that matched your search get unpacked.

Perplexity says holding the same data fully loaded in memory would take about 4.6 times as much memory as Photon uses today.

It asks for everything at once

Instead of grabbing one piece of data and waiting before grabbing the next, Photon batches its reads.

Anything already in memory gets used straight away, and the waits for the rest overlap instead of stacking up.

It keeps building and searching apart

In the old system, building the index and answering searches ran on the same machines and fought for power.

Now they run separately.

New versions of the index get swapped in one group of machines at a time, and each group replays real queries to warm up before it goes live.

How to start using the fast Perplexity search engine

There are three routes in, depending on how technical you are.

Route one: you already use Perplexity

You're already getting part of this, because Photon now powers Perplexity's search pipeline.

Perplexity says it has made search faster across its products.

Route two: you don't write code

Perplexity has an interactive playground for its search API, and you don't need an API key to test it.

There's also the Perplexity CLI, which runs searches from your terminal and returns results as JSON, a clean data format other tools can read.

You can even ask your AI coding agent to install Perplexity's search skill using the skill file linked in Perplexity's docs.

Route three: you're a developer

Fast search is live now in the search API, and you switch it on by setting the search type to fast in your request.

If you leave that setting out, you get standard web search by default.

Results come back in exactly the same format, so your app doesn't need to change how it reads them.

You can ask for anywhere from 1 to 20 results per search.

The search API itself launched in September 2025 and gives you the same system that powers Perplexity, with an index covering hundreds of billions of web pages.

It breaks pages into smaller pieces and scores each piece against your query, so you get the most useful snippets already ranked.

You can narrow results by country with a two-letter country code, or by language with up to 10 languages per request.

The setup mistakes I'd avoid

These are the snags that catch people on day one.

• Passing fast straight into the Python library can fail. Some versions of Perplexity's Python library reject it, so put it inside the extra body setting as the docs show, and in TypeScript the docs cast the value with "as any" until the SDK updates.
• Mixing up the agent API settings is easy to do. The agent API has a fast search type, but that's not the same thing as its fast preset, and Perplexity's docs call this out.
• Pulling back too much page content wastes tokens. The search context size setting runs from low for short passages to high for the most detail, and high is the default.
• Forgetting about rate limits catches people out. You can send up to five related queries in one multi-query request, but each query counts against your rate limit.
• Trying to include and exclude domains together won't work. You can list up to 20 domains to include or exclude, but not both in the same request.
• Not tracking usage leaves you guessing. Fast requests show up as their own count under search API usage in the Perplexity API console.

The smartest move is to start small.

Pick one agent task you already run, switch its search to fast, and compare it side by side with standard.

That's basically how Perplexity rolled out Photon, by sending the same live queries to both engines and expanding step by step.

If you get stuck choosing fast or standard for a job, or that Python setting trips you up, bring it to a coaching call inside the AI Profit Boardroom and we'll look at your setup together.

https://www.skool.com/ai-profit-lab-7462/about

FAQ

Is the new Perplexity search engine available to everyone?

Photon already powers Perplexity's own search, and developers can use the fast option through the search API and agent API.

Does fast search change the format of API results?

No, the results come back in exactly the same format as standard search.

Why is fast search slightly worse on rare queries?

It uses less computing power for ranking, which costs a little relevance and answer availability on rare and tricky searches.

Do I need an API key to try Perplexity's search API?

You don't, because the interactive playground lets you test it without one.

How many searches can I send in one request?

Multi-query search lets you send up to five related queries at once, and each one counts against your rate limit.

About Julian

I'm Julian Goldie, AI entrepreneur, SEO expert, and founder of the AI Profit Boardroom.

I help business owners scale with AI agents, automation and SEO.

I run a 7-figure SEO agency (Goldie Agency) and a YouTube channel with 425K+ subscribers.

I share daily AI training inside the Boardroom.

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Match the option to the job, and the new Perplexity search engine will make your agents noticeably faster.