9 device fingerprinting solutions for developers in 2025
As IP addresses and user agents become less reliable and cookie-based tracking continues to degrade, device fingerprinting remains one of the few techniques capable of consistently tying activity to a specific device. A robust fingerprint can help detect automation, multi-accounting, or account sharing, even when traditional identifiers are missing or deliberately masked.
But the fingerprinting landscape has shifted. Privacy-preserving browser updates like Safari’s Intelligent Tracking Prevention (ITP) and Chrome’s Privacy Sandbox are reducing the entropy that developers can access. At the same time, fraudsters increasingly rely on tools like Puppeteer Extra Stealth, Nodriver, and anti-detect browsers like Hidemium to inject noise or spoof fingerprinting attributes. These tools are designed to lie about properties like canvas fingerprint, user agents, and screen resolution, making fingerprint stability and resilience more critical than ever.
In this post, we examine nine developer-accessible fingerprinting solutions, both open-source and commercial, that stand out in 2025. Whether you're testing client-side identifiers or building a production-grade fraud prevention pipeline, we’ll help you evaluate trade-offs around accuracy, evasion resistance, privacy compliance, and long-term maintainability.
Open-source vs. commercial device fingerprinting
Choosing the right fingerprinting solution starts with a core trade-off: cost and flexibility versus resilience, accuracy, and support.
Open-source tools are often lightweight, browser-side scripts that don’t require backend integration. They’re well-suited for experimentation, learning, or non-critical use cases. But in adversarial environments, their limitations quickly become apparent:
- They operate entirely client-side, with no server-side correlation
- They lack protection against spoofing or fingerprint tampering
- They offer no visibility into changes introduced by evasive automation tools
This matters because bots and fraudsters increasingly lie about their environment. Tools like Puppeteer Extra Stealth, Nodriver, and Hidemium can artificially alter canvas fingerprints, spoof navigator properties, or swap user agents on the fly. Without robust fingerprint construction, these changes can break continuity, causing the same attacker to appear as multiple devices or multiple attackers to appear as the same one.
Commercial solutions, by contrast, typically combine client- and server-side signals, machine learning, and cross-session correlation. These systems are built to recognize manipulated fingerprints and maintain stability even when attributes like canvas or user agent are spoofed. They also provide developer support, documentation, and observability, key when you need to understand why a device was flagged or allowed.
In this post, we compare both categories with a focus on developer-accessible options: clear APIs, transparent pricing, and trial plans that make testing easy.
Open-source device fingerprinting solutions
1. CreepJS
CreepJS is a feature-rich open-source fingerprinting website built to audit modern browsers and uncover inconsistencies, what it calls “browser lies.” By analyzing deviations in API behavior, it helps reveal environments that have been manipulated using tools like puppeteer-extra-plugin-stealth. While it's not optimized for deployment, CreepJS is an excellent resource for understanding how stealth automation works and exploring the limits of fingerprint reliability.
2. FingerprintJS (Open Source)
This is the original version of FingerprintJS, designed to create browser-based identifiers by combining multiple device and environment attributes. It’s straightforward to implement and widely adopted for educational or experimental use. However, it lacks server-side correlation and is susceptible to client-side spoofing.
⚠️ License note: The library is released under the BSL, which prohibits use in production. It's intended for personal and non-commercial evaluation only.
3. Broprint.js
Broprint.js is a minimalistic fingerprinting library that combines canvas and audio signals to generate client-side identifiers. Its lightweight footprint makes it easy to plug into projects, but the project is no longer actively maintained, and its effectiveness against spoofed environments hasn’t been validated. It’s best suited for experimentation or instructional purposes.
4. thumbmarkjs
Thumbmark.js is a compact, MIT-licensed library designed to generate stable browser fingerprints using entropy from WebGL, canvas, and fonts. It aims to balance performance with practical uniqueness, reporting 90.5%–95.5% accuracy in real-world tests. The project promotes responsible use and is suitable for lightweight anti-abuse integrations. However, it doesn't include basic bot detection signals like navigator.webdriver, and as with other client-only tools, it can be fooled by anti-detect automation.
5. detectIncognito
DetectIncognito is a specialized utility that flags whether a user is browsing in private mode. While it’s not a fingerprinting library on its own, it can enhance a broader fingerprinting strategy by providing session context. Still, detection techniques can break as browsers evolve, so this type of signal should be used cautiously and monitored for reliability.
Commercial device fingerprinting solutions
For production environments—especially those dealing with fraud, automation, or large-scale abuse—commercial fingerprinting solutions offer the stability, accuracy, and support that open-source tools generally can’t match. These platforms combine client- and server-side data, apply machine learning to reduce fingerprint collisions, and often include defenses against fingerprint spoofing and manipulation.
6. Fingerprint.com
Fingerprint.com provides a dedicated fingerprinting API that generates persistent visitor identifiers using a mix of browser attributes, device signals, and network data. It also returns risk signals and historical context such as behavioral patterns and event metadata, helping teams identify repeat visits and potential anomalies over time.
The platform includes detection capabilities for incognito mode, bots, and some spoofing techniques. It’s developer-friendly, with a free tier (up to 1,000 API calls/month), a 14-day trial of the Pro Plus plan, and paid plans starting at $99/month for 20,000 API calls.
That said, Fingerprint.com is still a narrowly scoped tool. While it enriches fingerprints with metadata and risk context, it doesn’t include a built-in rule engine or decisioning framework. Teams must define thresholds and implement their own allow/block logic, which adds complexity when integrating it into real-time fraud prevention workflows.
7. Castle
Castle goes beyond basic fingerprinting by integrating device identifiers into a broader fraud detection and analytics platform. It supports real-time bot detection, account abuse prevention (like fake signups and credential stuffing), and behavioral profiling. Castle helps teams detect and respond to fraud by combining device signals with other risk factors, such as session behavior, velocity patterns, and historical device relationships.
Castle includes tooling to define rules, test them against historical data, and investigate incidents. It lets you fingerprint 10,000 requests per month for free, then starts at $200/month for 100,000 requests. Its multi-layered approach is well-suited for teams looking to operationalize fingerprinting as part of a full fraud prevention strategy.
8. IPQS
IPQS provides a broader set of fraud signals through an API-first model. In addition to device fingerprinting, it offers IP reputation, bot detection, email validation, and stolen credential checks. This makes it particularly appealing to high-risk verticals like fintech or gaming, where layered checks are needed at scale. While it has a small free tier, pricing starts at $999/month, which places it at the higher end of the market.
9. Seon
Seon combines fingerprinting with identity enrichment, helping teams link device activity to real-world identifiers such as email addresses or phone numbers. It aggregates social signals and other metadata to build richer risk profiles. Seon is well-positioned for companies focused on KYC, account opening fraud, or transaction monitoring.
Its free plan includes 500 manual checks, and paid plans begin at $699/month. While it offers strong enrichment capabilities, it may require more effort to integrate into real-time workflows.
Each of these tools has developer-friendly documentation and trial access. Choosing between them depends on how much infrastructure you want to own versus outsource, and whether your use case requires standalone fingerprints or broader fraud detection capabilities.
The ideal device fingerprinting solution for developers
The best fingerprinting solution depends on your environment, risk tolerance, and resource availability.
If you’re just beginning to explore the space, open-source tools like FingerprintJS (open source) and CreepJS are approachable and easy to integrate. FingerprintJS offers a clean API and quick setup, while CreepJS provides deeper visibility into how stealth tools manipulate browser APIs. That said, neither solution is built to handle evasive attackers or fingerprint spoofing in production. And importantly, FingerprintJS's open-source version is licensed under the BSL, which restricts use in production settings.
As soon as evasion resistance, accuracy, or operational visibility becomes important, commercial tools become the more practical choice.
If you're looking for a narrowly scoped, high-performance fingerprinting API, Fingerprint.com and IPQS are solid starting points. They offer raw identifiers and some bot detection capabilities, but leave decisioning and logic up to your team.
For teams that want more than just a fingerprint, such as aggregation, analytics, and layered detection, Castle and Seon provide broader solutions. Castle focuses on real-time risk scoring and bot detection, helping security teams catch fake signups, account sharing, and credential stuffing through a combination of device data, behavioral patterns, and session analytics. Seon, by contrast, leans into enrichment and identity linkage.
Ultimately, fingerprints alone are rarely sufficient to detect fraud. What matters is how those fingerprints are contextualized: correlated across sessions, evaluated for consistency, and paired with behavioral signals. That’s where platforms like Castle add the most value—by transforming raw device data into actionable risk intelligence.
What’s next?
Fingerprinting remains a foundational signal in modern fraud prevention, but its role is changing. As (anti-detect) browsers and privacy countermeasures evolve, developers can no longer rely on deterministic device IDs alone.
Here are three trends shaping the future of fingerprinting in 2025:
1. Declining signal quality due to privacy hardening
Major browsers continue to reduce access to high-entropy fingerprinting surfaces like canvas, audio, and system fonts. Chrome’s Privacy Sandbox, Safari’s ITP, and Firefox fingerprinting protection now limit or randomize key attributes, making it harder to generate unique, stable fingerprints. These changes especially impact client-only tools, which can’t correlate device activity with backend signals like session behavior or IP history.
Open-source libraries are often the first to break when these changes roll out, either generating unstable identifiers or failing entirely in hardened environments.
2. Increased spoofing via automation tools
Bots and fraudsters don’t just try to evade detection; they actively lie about their environment. Stealth automation frameworks like Puppeteer Extra Stealth, Nodriver, and anti-detect browsers like Hidemium inject noise into fingerprints or spoof browser APIs to appear as fresh, distinct devices.
This raises the bar for fingerprinting systems: it's no longer enough to gather data; the system must be able to detect manipulation and still generate a stable identifier. That means checking for inconsistencies, correlating server-side context, and evaluating fingerprint integrity across time.
3. Fingerprints as part of a layered defense
The industry is shifting away from “fingerprint-or-nothing” approaches. High-confidence detection now comes from layering device fingerprints with behavioral signals, velocity checks, user segmentation, and historical device relationships.
Platforms like Castle are built around this principle. Rather than treating fingerprints as a final verdict, Castle uses them as one component in a multi-layered decisioning system, one that can detect fake signups, credential stuffing, and account takeovers with high precision. This allows teams to act on risk in real time, test hypotheses, and investigate fraud patterns as they evolve.