Bright Data Reviews

1. Sam Mitchell

   · Owner at KittenProperties · over 2 years ago
   Mixed feelings

   We used their DC proxies and Residential proxies. Resi proxies were having quite low success rate. We had to use resi solution from other proxy providers. Unblocker didn't work well either also it was way too expensive.

   🏁 Competitors: Decodo, NetNut.io

   👍 Pros:    Cheap dc proxies

   👎 Cons:    Quite expensive|Residential proxies are worse than competitiors

llama.cpp Reviews

Bright Data mentions (45)

• Precursor

  Happy to offer a counter of some great products for anti-bot defeat: https://brightdata.com/ https://www.zenrows.com/ https://www.capsolver.com/ https://scrapfly.io/ hundreds of millions of residential ips, human browser fingerprints, custom browser binaries, auto solve of turnstyle, recaptcha v3, kasada, datadome, AWS WAF, etc if they come up. - Source: Hacker News / 9 days ago

• Best Web Scraping Tools in 2026: A Hands-On Comparison of the Top 10

  The best web scraping tools 2026 leaderboard hasn't changed; the gap has narrowed. Bright Data remains the safest bet for any team that wants to spend time on the data, not on the scraping. The 660-scraper library, 400M-IP network, pay-per-success pricing and unlimited concurrency are still uncontested at the high end. - Source: dev.to / 3 months ago

• The Economics of Web Scraping: How Consultancies Price Data Extraction and Manage Scope Creep

  Infrastructure Pass-Through (OpEx) Data extraction at scale is infrastructure-heavy. Bypassing modern Web Application Firewalls (WAFs) requires high-quality residential proxies, CAPTCHA solvers, and substantial browser-automation compute resources. Services like Bright Data charge significantly by the gigabyte for premium residential IPs. These variable infrastructure costs must be passed directly to the client,... - Source: dev.to / 3 months ago

• LinkedIn Scraping Is Dead: 5 Legal, ToS-Safe Alternatives That Actually Work in 2026

  Bright Data has successfully defended web scraping in U.S. Courts and offers LinkedIn datasets pre-collected and ready to download. LinkedIn profile data on their dataset marketplace runs around $250 per 100,000 records. The freshness caveat is real: bulk datasets are snapshots, not real-time. If you need current job titles on a rolling basis, you're better with an enrichment API than a one-time dataset pull.... - Source: dev.to / 3 months ago

• Building a Live AI Market Research Terminal: How Bright Data and Convex Replace Polling With Real-Time Everything

  Bright Data built an open-source demo that solves this. It's called the Signal Terminal, a financial research tool built around that problem. - Source: dev.to / 5 months ago

llama.cpp mentions (13)

• Ask HN: How close are we to local LLM models being useful? What's the impact?

  A good place to browse is the LocalLLaMa subreddit. [0] A good software to start is LM Studio [1]. Another popular alternative is Ollama [2]. A better software when you're used to it all is llama.cpp as it's usually a bit faster and more frequently updated [3]. A good place to get models is HuggingFace, particularly the Unsloth models [4] Most popular models lately to run on "regular" gaming PC's, workstations,... - Source: Hacker News / about 1 month ago

• llama-bench skipped FA on capable GPUs — b9437 corrects it

  Yes, for a local source build: pull the latest commit from ggml-org/llama.cpp and recompile. Tagged binary releases lag the continuous builds. Check the GitHub releases page for a pre-built artifact if you want to skip compilation, but verify the build number includes the b9437 changes before treating it as current. - Source: dev.to / about 1 month ago

• Introducing LlamaStash: a zero-overhead, terminal-native llama.cpp launcher

  That script grew up. Today I'm releasing LlamaStash, the first public release of a fast, cross-platform, terminal-native launcher for llama.cpp with zero overhead. - Source: dev.to / about 2 months ago

• How fast is LlamaStash? Overhead, throughput, and a fair comparison with Ollama and LM Studio

  LlamaStash spawns the unmodified upstream llama-server. So three different questions follow from that, and there is a benchmark suite for each. - Source: dev.to / about 2 months ago

• Why MTP doesn't speed up your llama.cpp inference (and how to actually fix it)

  Last week, I spent two days banging my head against a wall. I had just spun up a fresh llama.cpp build with multi-token prediction (MTP) support, loaded a quantized Qwen3 model, and ran my benchmark suite expecting that sweet 2-3x speedup everyone keeps talking about. - Source: dev.to / 2 months ago