Bright Data Proxy Networks

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Bright Data Proxy Networks

Bright Data Proxy Networks give developers access to one of the largest commercially available proxy pools, covering residential, datacenter, ISP, and mobile IP types. The network spans 195 countries with options for city, carrier, and ASN-level targeting, making it suitable for use cases ranging from high-volume crawling to geo-restricted content access.

General
Developer	Bright Data
Network Size	400M+ IPs
Countries	195
Type	Proxy Infrastructure
Documentation	docs.brightdata.com/proxy

Proxy Types

Residential Proxies: IPs sourced from real user devices. Best for accessing sites that block datacenter ranges. Supports unlimited concurrent sessions and rotates IPs automatically. Pricing starts at $5.04/GB.
Datacenter Proxies: Server-based IPs with high speed and low latency. Best suited for high-volume tasks where residential appearance is not required. Pricing starts at $0.066/GB.
ISP Proxies (Static Residential): Real residential IPs issued directly by ISPs, but static (non-rotating). Ideal for tasks that need a consistent IP address over time. Monthly plans start at $499.
Mobile Proxies: IPs from mobile carrier networks. Useful for tasks targeting mobile-specific content or requiring high anonymity. Pricing starts at $14.4/GB.

Proxy Variants

Type	Starting Price	Best For
Residential	$5.04/GB	Sites with strict bot detection
Datacenter	$0.066/GB	High-volume crawling at low cost
ISP (Static)	From $499/month	Sessions needing a fixed IP
Mobile	$14.4/GB	Mobile-targeted content, high anonymity

Core Features

Geo-targeting: Target by country, city, carrier, and ASN for precise location-based access.
Automatic rotation: Residential and mobile proxies rotate IPs per request or per session.
Concurrent sessions: Residential proxies support unlimited concurrent sessions.
99.99% uptime SLA: Infrastructure backed by a service level agreement across all proxy types.
Protocol support: HTTP, HTTPS, and SOCKS5 supported across all proxy types.

Tools and Resources

Proxy Manager: open-source proxy management tool for routing, authentication, and monitoring.
Pricing Pages: per-GB and monthly plan calculators for each proxy type.
Quick Start Guide: set up your first proxy zone in minutes.
Python SDK: route requests through Bright Data proxies from Python.

Ecosystem and Integrations

Available on Azure Marketplace and Microsoft Marketplace.
Works with Puppeteer, Playwright, Selenium, Scrapy, and any HTTP client library via standard proxy authentication.
Compatible with Bright Data's Web Unlocker and Scraping Browser for combined unblocking and proxy routing.

Get started at brightdata.com/proxy-types or explore the full documentation for zone configuration and integration guides.

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Bright Data Bright Data Proxy Networks AI technology Hackathon projects

Discover innovative solutions crafted with Bright Data Bright Data Proxy Networks AI technology, developed by our community members during our engaging hackathons.

RAYS Studio: Decentralized Federated OSINT Network

RAYS Studio is a revolutionary decentralized framework for open-source intelligence (OSINT) AI agents, built to solve the computational bottlenecks and privacy issues inherent in centralized LLM training. Standard agent architectures suffer from catastrophic forgetting when exposed to diverse localized datasets, while central servers struggle with massive training compute requirements. To solve this, RAYS Studio implements Federated Orthogonal Gradient Routing (FOGR) combined with Spectrally Bounded Zero-Gated Adapters (SB-ZGA). Local client devices execute complex OSINT workflows using RAYS-CORE, generating highly contextual, private task logs. When local fine-tuning is triggered, each edge client trains a low-rank adapter on its own hardware (utilizing GPU/MPS acceleration). We apply Singular Value Decomposition (SVD) constraints directly to the adapter weights, zeroing out dominant singular values to force the learning process into strict mathematical orthogonality. These localized, orthogonal adapter updates are securely submitted to the RAYS Studio daemon. The central daemon aggregates the weights, merges them with the base model, recompiles an optimized GGUF format, and hot-swaps the model instantly on a running llama.cpp server without downtime. By structuring learning spaces orthogonally, different clients can fine-tune on disparate OSINT targets simultaneously without erasing previously learned intelligence. The result is a highly scalable, privacy-first AI swarm capable of collective intelligence gathering across thousands of edge nodes.

Spec6: The enterprise intelligence operator

Spec6 uses Speechmatics in two distinct but connected ways: first as the live speech interface for the product, and second as a transcription engine for “spoken web” intelligence. That split is important, because this is not just “we added voice chat.” In the code, Speechmatics sits both at the front door of the system, where a user talks to Spec6 in real time, and deeper in the intelligence pipeline, where audio and video from the web can be converted into analyzable evidence. The live voice side starts in the frontend voice assistant at [/Users/nadhi/Desktop/win-win/src/frontend/react/chat/voice-assistant.tsx](/Users/nadhi/Desktop/win-win/src/frontend/react/chat/voice-assistant.tsx). The browser captures microphone input with `getUserMedia`, then converts the audio into the format Speechmatics expects: 16kHz mono PCM16. That matters because the browser does not just upload a blob after the user is done. It streams audio continuously in small chunks, which makes the experience feel like an actual assistant instead of a push-to-record form. The comment in that file describes the full loop clearly: mic input becomes PCM16, that stream goes to `/api/voice/transcribe/ws`, partial and final transcripts come back, and the final transcript is fed into the normal Spec6 agent loop. So the voice layer is not a separate AI product. It is an alternate input mode into the same reasoning engine that powers the rest of the app.

ROGUE: Open-web LLM Threat Intelligence Agent

A new way to jailbreak AI appears on Reddit, X, or arXiv almost every day. By the time a quarterly red-team catches it, it has already worked on a production chatbot. ROGUE closes that gap , the red-team that never sleeps. ROGUE is an autonomous red-team agent. It continuously harvests new LLM attacks from 19 live open-web sources — Reddit/X jailbreak communities, arXiv, GitHub (the Pliny umbrella), HuggingFace, MITRE ATLAS, OWASP, and vendor safety blogs — then reproduces each against YOUR deployment: your system prompt, your declared tools, your target model, scored together. Not a bare model. Not a frozen test bank. Your actual setup, against today's attacks. It's the only project here using Bright Data MCP on BOTH sides. As a consumer, the discovery agent reasons over Bright Data's MCP tools (Web Scraper, SERP, Web Unlocker, Scraping Browser) to reach sources that block bots. As a producer, ROGUE exposes its own MCP server. Try it now ,the dashboard has one-click "Add to Cursor / VS Code" buttons, and the hosted endpoint (rogue-api-mr5w.onrender.com/mcp) needs zero setup. Connect it and ask, from your own IDE, "what new attacks broke our support bot in the last 24 hours?" — live, during judging. The numbers are real, not a demo fixture. One live sweep: 8,321 breach trials across 6 deployment configs, a 16.5× vulnerability spread between weakest and strongest model. A separate judge scores every trial (REFUSED / EVADED / PARTIAL / FULL) and is calibrated against blind human labels, 98% breach-axis agreement, validated on WildGuardTest and StrongREJECT, not "trust the AI." Bright Data spend: $0.15 per detected breach. Publication-to-breach: ~2 minutes. It also red-teams multimodally, rendering text attacks as images and audio, because a jailbreak refused as text often succeeds as a picture of that text. Built solo in 6 days. Prior: GPTFuzz Grand Prize (Yonsei, 2024) and adversarial-ML research at AIM Intelligence.

C2 scraper for crypto currency futures

C2 is a scraper that pulls live crypto futures copy-trades off binance and feeds them into our ML pipeline, which ranks traders and decides which trades to copy. it runs on 166,000 reconstructed trades so far and that set keeps growing every cycle. binance is behind cloudflare and geo-fenced, so we use bright data geo proxies to reach each api cleanly and respect per-api rate limits with rotating exit ips. everything updates live on a dashboard. right now we're tracking 3,636 traders, 4,850 portfolios, 166,000 reconstructed trades, and about 49 million dollars in open positions. then can run machine learning

Apohara Synthex

AI agents now run on the live web, but prompt injection is the number-one risk on the OWASP LLM Top 10, and most teams cannot prove what their agents ingested, or that it was safe. Apohara Synthex fixes that. Synthex is the provenance and security layer for the web data an AI agent consumes. It fetches across the full Bright Data spectrum: Web Unlocker, the Web Scraper API, SERP API, Scraping Browser, and the MCP Server. We didn't just use Bright Data; we improved it, contributing PR #140 upstream. Every fetch runs a layered defense before anything reaches a model. A deterministic regex pass and Qwen3Guard on Featherless form a high-recall net; NVIDIA's NemoGuard, selected by a measured benchmark, is the low-false-positive block gate; and a reasoning model on the AI/ML API knows the difference between describing an attack and executing one. Clean content is classified across four lenses, then sealed into an enterprise Evidence Report. The seal is real and shipped: an Ed25519 signature, an RFC 3161 DigiCert timestamp, an offline-verifiable Sigstore Rekor transparency log, and C2PA Content Credentials. Anyone can verify it in seconds with openssl, the industry's own c2patool, and a public ledger. No trust required. Cognee adds memory across re-scrapes, TriggerWare turns it into an automated monitor, and Kiro runs our continuous test and QA hooks. Synthex spans all three tracks, Security & Compliance, Finance & Market Intelligence, and GTM Intelligence, built for the CISO, CFO, compliance lead, and underwriter who need evidence they can defend to a board or a regulator. The average data breach costs 4.44 million dollars; Synthex seals an evidence artifact for a fraction of a cent. Everything signed, nothing trusted, and every number ships with a command to reproduce it.

Sentra AI - Live GTM Intelligence OS

Enterprise GTM, strategy, and sales teams lose days stitching competitor pricing, SERPs, product pages, hiring signals, and macro news across spreadsheets, ad-hoc searches, and static battlecards. By the time a slide deck is updated, the market has already moved. Sentra AI is a GTM intelligence operating system built on Next.js and Supabase. Users describe what to watch in plain language; Sentra infers monitor intent, routes collection through Bright Data (SERP API, Web Unlocker, scraper/browser zones, and MCP search + scrape), and synthesizes evidence-backed risks, opportunities, and recommendations with AI/ML API and Featherless for document-heavy workflows. The dashboard surfaces live briefings and market context; chat answers GTM questions with visible provider attribution; Alerts run on-demand or on a schedule with executive reports, webhooks, and CRM-style export. Visual Forensics and Face Intelligence support image authenticity investigations. World Engine adds macro scenario views. Speechmatics enables spoken briefings. A unified History page stores every analysis run for review. Target audience: GTM leaders, competitive intelligence, revenue operations, and founders who need defensible, current external evidence—not generic LLM guesses. Production mode prioritizes live Bright Data collection so judges and users see real web evidence when zones are configured.