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

Linear 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 / 5 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 / 2 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

Linear mentions (162)

• The Tradeoff That Slows Production Teams Down: Flexibility vs Actually Shipping

  Speed matters. Not speed in sprint or linear dashboards. Not speed in story points. - Source: dev.to / about 2 months ago

• Freshworks Just Shipped an MCP Gateway Inside Its ITSM Platform. Here's What That Actually Changes.

  Model Context Protocol, for context, is the emerging standard for letting AI agents pull live data from external systems without custom integration code. Freshworks has implemented it as a native layer in Freddy AI, which means agents can now reach into Notion, ClickUp, Linear, Workday, Rippling, and the rest of the enterprise stack — not through brittle webhooks or bespoke connectors, but through a standardized... - Source: dev.to / about 2 months ago

• How to Document and Track Technical Debt

  Issue trackers: GitHub Issues, Linear, or Jira work well because technical debt records live in the same tool as feature work. This makes them easier to pull into sprint planning and keeps the debt backlog visible alongside the feature backlog. The main risk is that debt issues get buried under feature issues without careful labeling and triage discipline. - Source: dev.to / 2 months ago

• How to Write a Technical Debt Remediation Plan for Non-Technical Stakeholders

  Linear and similar tools can track velocity metrics per area of the codebase over time, making the before/after comparison straightforward to document. - Source: dev.to / 2 months ago

• Master the in demand of salary negotiation and system design: What Fails

  Most engineers fail salary negotiations because they use vague statements like "I work hard" or "I’m a good teammate" instead of quantified, verifiable impact. After 15 years of negotiating offers, I’ve found that engineers who tie their ask to concrete business outcomes land 30% higher offers than those who don’t. For example, instead of saying "I improved the API", say "I reduced API p99 latency by 400ms, which... - Source: dev.to / 2 months ago