Kasada vs WebDecoy
Kasada vs WebDecoy. Adversarial ML challenges vs multi-signal detection with honeypots, TLS fingerprinting, and vision AI detection.
Kasada vs WebDecoy
Kasada and WebDecoy take fundamentally different approaches to bot mitigation. Kasada uses adversarial ML with dynamic challenges—effective but creates user friction. WebDecoy uses multi-signal detection including honeypots, TLS fingerprinting, and vision AI detection—zero friction for legitimate users.
Detection Philosophy
Kasada: Challenge-Based Detection
User Request
↓
Kasada Edge
├── Initial Challenge
│ └── Proof-of-work computation
├── Behavioral Analysis
├── Device Fingerprinting
└── Adversarial ML
└── Adapts to evasion attempts
↓
Challenge / Allow / BlockKasada’s philosophy: Force clients to prove they’re legitimate through computational challenges. Continuously adapt challenges based on evasion attempts.
Target market: High-value targets (financial services, gaming, ticketing) where sophisticated attackers justify user friction.
WebDecoy: Frictionless Multi-Signal Detection
User Request
↓
WebDecoy Detection Stack
├── TLS Fingerprinting (JA3/JA4)
├── IP Enrichment (AbuseIPDB, GreyNoise, IPQS)
├── Geographic Consistency
├── Honeypot Detection
│ ├── Decoy Links
│ └── Endpoint Decoys
├── Behavioral Analysis (Bot Scanner)
└── Vision AI Detection (FCaptcha)
↓
Threat Score (0-100) → Allow / Challenge / BlockWebDecoy’s philosophy: Layer multiple detection signals that work invisibly. Honeypots provide high-confidence signals; other layers catch what honeypots miss.
Target market: Organizations wanting effective bot detection without user friction.
Detection Method Comparison
| Capability | Kasada | WebDecoy |
|---|---|---|
| Primary Method | Adversarial challenges | Multi-signal detection |
| User Friction | Yes (challenges) | No (invisible) |
| TLS Fingerprinting | Included | JA3 + JA4 + JA4H |
| Honeypots | No | Decoy Links + Endpoint Decoys |
| IP Intelligence | Kasada network | AbuseIPDB + GreyNoise + IPQS |
| Proof-of-Work | Yes (core feature) | Optional (FCaptcha) |
| Vision AI Detection | No | FCaptcha (GPT-4V, Claude Computer Use) |
| AI Crawler Detection | Via challenges | Purpose-built (15+ crawlers) |
| SIEM Integration | Enterprise | All tiers |
| JavaScript Required | Yes | Core detection: No |
Key Differences
Kasada’s Strengths
Adversarial ML
Kasada’s challenges adapt to evasion:
- Dynamic challenge generation
- Proof-of-work requirements increase with suspicion
- Continuous learning from attack patterns
- Effective against well-funded attackers who solve static challenges
Pre-Interaction Protection
Challenges happen before bots interact with your application:
- Protection at the edge
- Bots can’t even access content without passing challenges
- Catches reconnaissance attempts
High-Security Focus
Built for industries with sophisticated threats:
- Financial services
- Gaming and ticketing
- Licensed content protection
WebDecoy’s Strengths
Zero User Friction
Honeypots and multi-signal detection are completely invisible:
- No challenges for legitimate users
- No abandonment from frustrated users
- Works without JavaScript for core detection
Honeypot Detection (Kasada doesn’t have this)
// Endpoint Decoy catches attackers
{
"endpoint_decoy": {
"path": "/api/admin/config",
"attack_patterns": [
{ "type": "sql_injection", "severity": "critical" }
],
"score_impact": +50
}
}No legitimate user accesses honeypots—high-confidence signals.
Vision AI Detection (Kasada doesn’t have this)
// FCaptcha detects vision AI agents
{
"vision_ai": {
"screenshot_loop_timing": true,
"pixel_perfect_clicks": true,
"movement_entropy": 0.01,
"classification": "vision_ai_agent"
}
}Kasada’s challenges are designed for traditional bots. Vision AI agents that control real browsers need different detection methods.
Multi-Signal Detection
Multiple independent layers catch different threats:
{
"tls": { "mismatch": true, "score": +40 },
"ip": { "datacenter": true, "score": +25 },
"honeypot": { "triggered": true, "score": +50 },
"behavioral": { "mouse_entropy": 1.2, "score": +20 }
}Real-World Scenarios
Scenario 1: Sophisticated Bot with Challenge Solving
Threat: Bot service with CAPTCHA-solving capability.
Kasada’s Detection:
- Proof-of-work: Bot solves challenges (some cost)
- Adversarial ML: May adapt to detect patterns
- Result: Cat-and-mouse game, effectiveness varies
WebDecoy’s Detection:
- Challenge not required for detection
- Decoy Link: Bot follows hidden link ✅
- TLS fingerprint: Automation signature ✅
- Result: Blocked via multiple signals, no challenge required
Scenario 2: Vision AI Agent (OpenAI Operator)
Threat: AI agent using screenshots and vision models to navigate.
Kasada’s Detection:
- Challenges: Agent can read and describe challenges
- Proof-of-work: Agent’s browser can compute
- Result: Vision AI may solve challenges designed for traditional bots
WebDecoy’s Detection:
- FCaptcha: Pixel-perfect click patterns ✅
- FCaptcha: Screenshot loop timing (2-3s intervals) ✅
- FCaptcha: Zero movement during “thinking” ✅
- Result: Classified as vision AI agent
Scenario 3: User Experience Impact
Scenario: Legitimate user visiting your site.
Kasada:
- User sees challenge
- Must wait for proof-of-work computation
- Some users abandon
WebDecoy:
- User sees nothing
- Detection happens invisibly
- Zero friction
Pricing Comparison
Kasada
- Enterprise pricing with custom quotes
- Typically thousands to tens of thousands per year
- Custom implementation required
- Enterprise support included
WebDecoy
| Plan | Price | Domains | Detections | Key Features |
|---|---|---|---|---|
| Starter | $59/mo | 1 | 5,000/mo | Bot Scanner, Decoy Links, FCaptcha |
| Pro | $149/mo | 5 | 100,000/mo | + Endpoint Decoys, TLS fingerprinting |
| Agency | $449/mo | 50 | 500,000/mo | + All SIEM integrations |
When to Choose Each
Choose Kasada If:
- You face sophisticated, well-funded attackers
- Challenge-based friction is acceptable
- You protect high-value assets (financial, gaming)
- Pre-interaction blocking is required
- You have enterprise budget
Choose WebDecoy If:
- User experience is a priority (zero friction)
- You want multi-signal detection
- You need vision AI agent detection
- You prefer honeypot-based detection
- You have budget constraints
- You need SIEM integration without enterprise pricing
What WebDecoy Provides
- Zero Friction - Invisible detection, no challenges for users
- Multi-Signal Detection - TLS + IP + Geo + Behavioral + Honeypots
- Vision AI Detection - FCaptcha catches GPT-4V, Claude Computer Use, Operator
- Honeypot Technology - Decoy Links and Endpoint Decoys
- IP Enrichment - AbuseIPDB, GreyNoise, IPQualityScore
- Transparent Detection - See exactly which signals triggered
- SIEM Integration - All tiers include Splunk, Elastic, CrowdStrike
- Accessible Pricing - $59-449/month vs enterprise quotes
Get Started
Try WebDecoy: Start Your Free Trial and see frictionless bot detection.
Questions? Contact us to discuss your threat model.
Frequently Asked Questions
What is Kasada's approach to bot detection?
Kasada uses adversarial ML with dynamic challenges. Challenges adapt based on evasion attempts, requiring computational proof-of-work. Designed for high-security environments with sophisticated attackers.
How does WebDecoy differ from Kasada?
WebDecoy uses multi-signal detection (honeypots, TLS fingerprinting, behavioral analysis, IP enrichment) without requiring user-facing challenges. Zero friction for legitimate users while catching sophisticated bots through multiple detection layers.
Which creates more user friction?
Kasada's challenge-based approach creates friction (users must complete challenges). WebDecoy's approach is frictionless—honeypots and multi-signal detection work invisibly in the background.
Can WebDecoy detect vision AI agents?
Yes. WebDecoy's FCaptcha detects vision AI agents (GPT-4V, Claude Computer Use, OpenAI Operator) by analyzing screenshot loop timing and pixel-perfect click patterns. Kasada's challenges are designed for traditional bots, not vision AI.
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