Show HN: Hyperbrowser – Scalable Browser Infrastructure for AI Apps (hyperbrowser.ai)
Excited to share a project we've been working on called Hyperbrowser. It’s a tool that makes scaling headless browsers ridiculously easy. It allows you to spin up hundreds of browser sessions in secure, isolated environments, with sub-second launch times. We automatically solve captchas, use residential proxies and manage concurrent sessions so that you can focus on your own business.
The idea for Hyperbrowser came from our own struggles building AI apps and agents like sales tools, automations, and AI editors. Every project seemed to hit the same roadblock: interacting with the web. Whether we needed web data as input or web browsing as output, we faced constant challenges—getting blocked, setting up proxy services, solving captchas, and scaling everything in Kubernetes.
On top of that, we had to build custom functions and services to convert websites into LLM-friendly markdown and crawl entire sites for relevant data. Keeping all of this running became a full-time job!
To make this easy for everyone else we built Hyperbrowser. It packages everything we learned and built, with a nice frontend that gets rid of the boilerplate and lets you hit the ground running. Hyperbrowser works seamlessly with tools you already know, like Puppeteer, Playwright, or Selenium, while removing the hassle of infrastructure and scaling.
If this sounds interesting, we’d love for you to give it a spin! You can sign up and start playing around with a free plan. Would love to hear your thoughts, feedback, or ideas! Check it out here at hyperbrowser.ai.
If you have any questions at all feel free to reach out to me at akshay@hyperbrowser.ai! Ideally share the website you'd like to scrape or automate. I can provide a script for it or we can create a custom API endpoint!
45 comments
[ 2.8 ms ] story [ 115 ms ] threadone quick nit on your docs: https://docs.hyperbrowser.ai/guides/scrape-site
```
import Hyperbrowser from "@hyperbrowser/sdk";
const client = new Hyperbrowser({ apiKey: process.env.HYPERBROWSER_API_KEY, });
(async () => {
})(); ```should be:
```
import Hyperbrowser from "@hyperbrowser/sdk";
const client = new Hyperbrowser({ apiKey: process.env.HYPERBROWSER_API_KEY, });
(async () => {
})();```
Do you have any restrictions on sites we can scrape? I am likely going to do some public LinkedIn scraping (logged out, legally compliant).
> spin up hundreds of browser sessions in secure, isolated environments, with sub-second launch times
What tech do you use for this, and where is this hosted?
2nd, the pricing also seems way way below what ScrapingBee offers.
You charge $100 for 60K credits. So assuming 18 credits for 1 page (3 MB per page) that allows me to crawl 3333 web pages. $250 would allow me to crawl under 10k pages
In comparison it costs $249 for 3 million credits in ScrapingBee which enable me to crawl 100,000 webpages (with JS and proxies enabled)
Am i missing something obvious here?
Dedicated endpoints for just scraping like scraping bee are coming next week at a discounted price! In the mean time let me know if there is anything you would like that scraping bee is missing :)
Things like captcha solving are nice no doubt but for many people, their use cases just aren’t compelling enough to make it worth the ROI. I would love captcha solving, but definitely not at your price point..
We aim to be pretty competitive on price across the board. Today we're just launching our headless browser service and I think we're significantly less expensive than competitors on that as dbmikus mentioned below
Totally hear you on the scraping specific use case without captcha solving etc though - we'll probably launch competitive pricing for this specific use case in the next few days. I'll make a note to leave a comment here when we do or feel free to email me at shri@hyperbrowser.ai and I can follow-up there :)
This looks very interesting and I’d love to try it.
We have data extraction and enrichment platform. We process over 12 million automations for our users, half of this is done on lambda and rest we try to process in a container.
We also utilize 10 GB+ in proxies.
I tried to do all the math on how much it would cost on your platform if we do a pilot project but I’m so confused with the pricing.
Can you please explain for 10,000 hours and 10 GB proxy usages what would the cost?
For your specific example, this is how the math works out:
- Browser hours: 10K hours * 60 minutes per hour * 1 credit / minute = 600K credits
- Proxy usage: 10GB proxy data * 1024 MB / GB * 6 credits / 1MB proxy data = 61,440 credits
The scale plan includes 60K credits for $100/mo and overage credits are ~0.2c each, so the total would be $100 + (601.44 * $0.002) = 1302.88
Sorry about the confusing pricing btw! As I wrote this out, clearly we have our work cut out for us to simplify pricing - we'll try to make it simpler over the next few days and add a calculator that let's you see how much this would cost.
Feel free to email me at shri@hyperbrowser.ai if I can help with anything!
This is also something I need btw so if you built a product on top of it, I'd be user #2 :)
(We have a 100K+/day scraping workload, and TBD full interactive automation)
This is such a new capability that I'm having a hard time getting a sense of interesting use cases. I'm quite sure this is more than just a shim for web services which don't expose APIs. But I also wonder whether LLMs are good enough to be trusted with more open-ended tasks at this stage.
The companies we’ve seen automatic agentic workflows well typically send a bunch of context to the LLM and somewhat constrain the actions that the model can take. Actually works better than you’d expect :)
Happy to help with anything :)
Of note: as Charity Engine is a general-purpose distributed compute platform, it's possible to run applications to post-process web content "at the edge" of the network, as the data is streamed back. Ex we're now running small LLMs along with the browsers, so it's possible to do things like run sentiment analysis, generate image descriptions, summarize content, etc – all at scale, enabled by hundreds of thousands of processors.
For more, see this talk: "Distributed Intelligence for Distributed Data" - https://youtu.be/YQe--AZFUuQ?si=_HoqkdooNPSR7dDQ (13mins)