ShieldLabs Reviews & Product Details

ShieldLabs is a fraud detection and prevention platform built on visitor identification and anonymity signal detection. One JavaScript snippet collects more than 100 device and network signals on every visit, and the platform returns a risk score with the named signals behind it through an API and signed webhooks. The customer's own code decides what to do with it. WHO IT IS FOR Two groups, arriving through different doors. Developers and technical founders, most often the CTO at a team of five to fifty people. The product is API-first, so whoever evaluates it is usually whoever integrates it, and the decision logic stays in their own codebase. Growth, marketing and analytics people, who may never open the API. Once the snippet is installed the dashboard is theirs: which traffic sources bring anonymised and high-risk visitors, what share of sessions is clean, and how that moves week to week. By sector it clusters where a free tier, a promotion or a signup carries real marginal cost: SaaS and AI products with usage-based infrastructure bills, ecommerce, marketplaces, fintech, gaming, iGaming, crypto, media streaming, ticketing and travel. WHAT IT DETECTS Detection covers the full spectrum of anonymity rather than a single flag. Network Intelligence covers VPNs, proxies, Tor, Apple Private Relay, datacenter IP ranges, IP reputation and timezone mismatch against the IP location. Device Intelligence covers anti-detect browsers, browser automation, bot traffic, operating system mismatch and undetectable operating systems. Everything is drawn from more than 100 signals collected on each visit and interpreted by a combination of deterministic rules and AI. Each finding arrives as a separately named signal, so the kind of anonymity present is visible rather than hidden behind one number. IDENTIFICATION THAT PERSISTS A returning visitor is recognised with up to 99% accuracy despite cleared cookies, incognito mode, IP rotation and months between visits. Device and network signals are collected together and cross-checked, which is what exposes deep masking. EXPLAINABLE RISK SCORING Every visit receives a risk score together with the named signals that produced it. Individual decisions stay auditable: when a legitimate customer is caught by a rule, the reason can be looked up rather than guessed. Scores group into clean, low, medium and high risk bands, and the customer sets the threshold that matters for their business. PATTERNS Correlation across accounts, devices and identities is pre-computed in the dashboard rather than left as an exercise. Patterns point to multi-accounting, account sharing, account takeover and account farms operating behind a single connection, and give an investigation its starting point. TRAFFIC AND RISK ANALYTICS Alongside the score for an individual visit, the dashboard gives an overall traffic risk score for the whole property, with the split across risk bands and how it moves over time. The same breakdown runs per source, including channels, referrers and campaigns, so it becomes visible which acquisition channels bring anonymised and high-risk visitors. For anyone buying traffic, that turns a monthly ad invoice into something that can be argued with. AI TRAFFIC AND FRAUD COPILOT The Copilot turns traffic data into clear summaries, investigations, answers and ready-to-run actions against fraud, tailored to the customer's business type. WHAT IT IS USED FOR Multi-accounting: one person running many accounts to farm a benefit. Free-trial abuse: endless new trials from the same person. Bonus abuse: signup bonuses claimed repeatedly by the same person. Promo abuse: discount codes redeemed far beyond intent. Referral fraud: self-referrals and fake invitees. Loyalty fraud: points and rewards farmed illegitimately. New-account fraud: fraudulent signups at registration. Account takeover: someone else logging into a real user's account. Account sharing: one subscription used by many people. Ban evasion: banned users returning under new identities. Paywall bypass: reading paid content without paying. Payment fraud: fraudulent transactions. Ad fraud: paid traffic that does not match what was paid for. Location spoofing: faked geography to reach restricted content or pricing. Sybil attacks: many fake identities used to sway a system, common in crypto and Web3. Returning visitor recognition: the same capability pointed the other way, identifying a good returning customer without asking them to prove who they are again. INTEGRATION Setup is one JavaScript snippet with about five minutes to the first signal. Ready-made install guides cover React, Next.js, Vue, Angular, Svelte, Preact and plain JavaScript, as well as WordPress, Shopify and Tilda, where the snippet goes into the platform's existing custom-code field and needs no developer. Server-side SDKs are available for Node.js, Python, Go and PHP, with an OpenAPI specification for everything else. Results arrive through a public API and signed webhooks, available on every plan including the free tier. PRICING Pricing is published and flat, with no per-seat charges and no sales process. Every plan includes the full detection stack; higher tiers add volume, not features. The free tier is 5,000 identifications, granted once, with no credit card and no time limit. Paid plans are $99 per month for 25,000 identifications, $399 for 150,000 and $999 for 500,000. Because allowances grow faster than price, the effective cost per identification falls as volume grows, from $0.00396 on the entry plan to $0.00200 at the top. Yearly billing saves 20 percent: $950, $3,830 and $9,590 per year respectively. Traffic above a plan allowance continues to be identified and scored rather than being cut off. The excess is billed at the plan's own rate as a line item on the next renewal invoice, because detection that switches off when a quota runs out is not detection.