Lakera vs Cloudflare Firewall for AI vs FilterPrompt: Honest 2025 Comparison
Comparison · 2023-02-21 · 11 min read · FilterPrompt Team
A side-by-side honest comparison of Lakera Guard, Cloudflare Firewall for AI, and FilterPrompt — detection coverage, latency, tenancy model, pricing, and best-fit use case.
Three of the most-asked-about LLM vulnerability scanner vendors are Lakera, Cloudflare Firewall for AI, and FilterPrompt. Here is the side-by-side comparison we wish someone had written for us. We are FilterPrompt — we have tried to be honest about where the other two beat us.
Detection coverage
Lakera leads on raw detector benchmarks for prompt injection. Their research team is excellent and it shows. Cloudflare's coverage is improving quickly and is solid today. FilterPrompt's detection is competitive on prompt injection and PII, with the differentiator being our transparent, readable rule engine.
Latency
Cloudflare wins on raw latency by virtue of its edge network — verdicts often land in under 50ms. Lakera and FilterPrompt both target 99%+ detection accuracy. For most chat applications the difference is invisible to users.
Tenancy model
FilterPrompt is multi-tenant by design. Each tenant has its own scanner key, provider key, rule set, quota, rate limit, and audit log out of the box. Lakera and Cloudflare are single-tenant by design.
Provider keys
FilterPrompt is bring-your-own-keys. Tenants paste their own OpenAI / Anthropic / Gemini keys; the scanner holds them encrypted and never proxies your spend.
Pricing model
Lakera is per-API-call and prices accordingly at scale. Cloudflare is bundled with Workers usage. FilterPrompt prices per request with a generous free tier (10,000 requests/month) and predictable per-tenant pricing for SaaS platforms.
Best-fit use cases
- Lakera — single-product teams who want best-in-class detection and have the budget for it
- Cloudflare — teams already deep in the Cloudflare stack who want low-latency edge enforcement
- FilterPrompt — SaaS platforms shipping LLM features to multiple customers, indie devs who want sane defaults, teams who value a transparent and auditable rule engine
Implementation Patterns and Integration Complexities
Understanding how each solution integrates into existing infrastructure is paramount for a seamless developer experience and robust security posture. The choice of integration pattern directly impacts deployment time, maintainability, and the operational overhead associated with managing the security layer.
FilterPrompt, designed for multi-tenancy and developer agility, often fits best as an API proxy or SDK integration within a service mesh. This allows fine-grained control over individual tenant's LLM interactions without demanding significant re-architecting. Lakera Guard typically operates as a dedicated API endpoint, requiring explicit routing of LLM calls through their service. Cloudflare Firewall for AI, being an edge-native solution, integrates directly into your Cloudflare Workers or sits in front of your application's API endpoints, leveraging their globally distributed network for interception.
Real-World Attack Scenarios and Mitigation Focus
While all three vendors aim to protect against common LLM vulnerabilities, their historical focus and demonstrated strengths vary based on the attack vectors they've prioritized. Prompt injection remains a primary concern, but data exfiltration, indirect prompt injection, and denial-of-service also warrant specific mitigation strategies.
Lakera has historically excelled at detecting complex prompt injection and jailbreak attempts, often leveraging sophisticated NLP models to identify subtle adversarial cues. Cloudflare's strength lies in its ability to block large-scale, automated attacks and unwanted content at the network edge, acting as a first line of defense against volumetric threats. FilterPrompt provides robust protection against prompt injection, PII leakage, and also offers granular controls for content moderation and output filtering, crucial for maintaining brand safety and regulatory compliance in multi-tenant environments.
Metrics, Benchmarking, and Performance Expectations
Evaluating LLM security solutions extends beyond simple pass/fail rates. Key performance indicators (KPIs) include not only detection efficacy but also false positive rates (FPR), false negative rates (FNR), and throughput capabilities. An overly aggressive filter that blocks legitimate user queries can be as detrimental as one that allows injections.
Lakera frequently publishes benchmarks demonstrating high detection rates on their internal datasets, often achieving F1 scores exceeding 0.95 for prompt injection. Cloudflare focuses on low-latency processing and high throughput, often citing sub-50ms processing times for typical Worker requests, even with AI protection enabled. FilterPrompt emphasizes customization, and while core detection models are highly performant (e.g., <100ms average processing), the real value comes from the ability to fine-tune rule sets to minimize FPR for specific application contexts, often resulting in <0.01% FPR in production for targeted use cases.
Adherence to Regulatory Frameworks and Industry Standards
The evolving regulatory landscape, including the EU AI Act and NIST AI RMF, increasingly mandates transparency, accountability, and robust risk management for AI systems. LLM security solutions must help organizations meet these obligations, especially concerning data privacy and the prevention of harmful outputs.
Cloudflare's global network and compliance with standards like ISO 27001 aid in data residency and secure data processing, relevant for various regulatory regimes. Lakera's focus on explainable detection mechanisms can contribute to the 'transparency' and 'technical robustness' pillars of frameworks like the EU AI Act. FilterPrompt's auditable rule engine, granular access controls, and logging capabilities directly support the 'governance and accountability' requirements of NIST AI RMF and help demonstrate 'human oversight' and 'control' as outlined in the EU AI Act, particularly for protecting sensitive data processed by LLMs.
- EU AI Act: Focus on transparency, risk management systems, human oversight, and robustness.
- NIST AI RMF: Emphasizes govern, map, measure, and manage functions for AI risk.
- OWASP LLM Top 10: Provides a living standard for LLM security vulnerabilities, including Prompt Injection, Data Leakage, and Insecure Output Handling.
Decision Framework: Choosing the Right Solution for Your Needs
Selecting the optimal LLM security solution involves more than just comparing features. A structured decision framework, considering your specific operational context, technical capabilities, and business objectives, is essential to avoid costly misalignments.
Start by identifying your core threats (e.g., prompt injection, PII leakage, content moderation). Then assess your existing infrastructure for integration complexity – are you heavily invested in Cloudflare, or do you need a more agnostic API proxy? Finally, consider your team's expertise: do you prefer a fully managed black-box solution, or do you need customizability and auditability? FilterPrompt shines for SaaS platforms requiring multi-tenancy, granular control, and compliance, while Lakera is ideal for single-product teams prioritizing raw detection efficacy. Cloudflare is best for those already embedded in their ecosystem seeking edge-native protection.
- Define your threat model and critical assets (e.g., user data, proprietary prompts).
- Evaluate integration effort and architectural fit with your current stack (e.g., API proxy, edge function, SDK).
- Assess the need for custom rules, auditing, and multi-tenancy features.
- Consider your budget and the total cost of ownership, including operational overhead.
- Pilot leading solutions in a controlled environment with realistic traffic patterns.
Future Trends and Evolving LLM Security Landscape
The LLM security landscape is rapidly evolving, driven by advancements in foundation models, new attack vectors, and increasing regulatory pressure. Future solutions will likely emphasize proactive threat intelligence, adaptive defenses, and closer integration with LLM development pipelines.
Expect to see more emphasis on 'security by design' principles, with security guardrails being baked into the LLM orchestration layer itself, rather than solely relying on external proxies. Verifiable claims and proof of controls will become more critical for compliance. FilterPrompt's architecture, with its focus on transparent, configurable rules, is well-positioned for this future, as it allows dynamic adaptation to emerging threats and regulatory shifts without requiring a complete re-training of a black-box model. We anticipate greater collaboration between security vendors and LLM providers to create intrinsically safer models.
Advanced Threat Categorization and Granular Control
Beyond a simple 'block or allow' decision, modern LLM security platforms must offer nuanced threat categorization. This allows security teams to move beyond generic prompt injection flags and understand the specific nature of an attack. FilterPrompt excels here by providing a detailed breakdown of detected threats, distinguishing between direct injection, indirect injection, jailbreaks, data exfiltration attempts, and PII leakage with specific sub-categories. This granular telemetry is critical for incident response and understanding attacker methodologies.
Lakera Guard provides robust threat types but its focus tends more towards a consolidated alert. Cloudflare Firewall for AI, while effective, often operates at a higher level, classifying traffic as malicious LLM interaction without always providing the fine-grained 'why' that advanced security operations centers (SOCs) require. The ability to differentiate between a user trying to jailbreak a model for fun versus a sophisticated attempt to exfiltrate sensitive data from an RAG system is vital for appropriate remediation.
- FilterPrompt: Granular classification (e.g., 'Direct Prompt Injection - Role Play', 'Indirect Injection - Data Exfil Attempt').
- Lakera Guard: Strong, but broader, threat categorization (e.g., 'Prompt Injection', 'Jailbreak').
- Cloudflare Firewall for AI: High-level anomaly detection and blocking (e.g., 'Malicious LLM Request').
- Critical for tuning response actions and understanding attack sophistication.
Custom Rule Engines and Adaptability to Evolving Threats
The LLM threat landscape is dynamic, with new exploits emerging regularly. A static, black-box security solution will quickly become obsolete. The ability for organizations to define custom rules, patterns, and policies is therefore non-negotiable for long-term protection. FilterPrompt’s transparent, declarative rule engine allows security teams and developers to write and deploy their own detection logic using a high-level language, enabling rapid response to zero-day LLM vulnerabilities specific to their application's context or data. This is akin to writing custom WAF rules but specifically tuned for LLM interactions.
Lakera Guard offers some configuration options and sensitivity tuning, but the core detection logic remains proprietary. Cloudflare, leveraging its extensive WAF capabilities, allows for custom rule sets within its Workers environment, providing a good degree of flexibility. However, these rules are often more focused on traditional web attack vectors rather than the semantic nuances of LLM threats. A dedicated, LLM-aware custom rule engine significantly shortens the feedback loop between discovery of a new threat and its mitigation.
Visibility, Logging, and Auditability
For enterprise adoption and compliance, comprehensive logging and auditability are crucial. Security teams need to understand not just what was blocked, but who initiated the request, what the original prompt was, the detected threats, and the ultimate decision made by the security layer. FilterPrompt provides exhaustive, tenant-scoped audit logs that can be streamed to SIEM systems, offering full transparency into every LLM interaction. This level of detail is invaluable for post-incident analysis, compliance reporting (e.g., NIST AI RMF), and debugging application behavior.
Lakera Guard provides detailed API logs within its platform, including threat detections. Cloudflare, as a global network, generates vast amounts of log data, and its integration with analytics platforms provides insights into blocked requests. However, correlating specific LLM-centric security events with broader network traffic can sometimes require additional configuration or custom dashboards. The key differentiator is the depth and LLM-centric context of the logs immediately available for security and development teams.
Cross-Platform LLM Support and Abstraction
Many organizations are adopting a multi-LLM strategy to leverage the unique strengths of different models (e.g., OpenAI for creativity, Anthropic for safety, Google Gemini for specific data types). A robust LLM security solution should abstract away the underlying LLM provider, offering a consistent security layer regardless of which model is being called. FilterPrompt is designed with this multi-LLM future in mind, providing a unified API endpoint for security scanning that sits in front of any provider. This prevents vendor lock-in and simplifies security management across heterogeneous LLM deployments.
Lakera Guard supports a range of popular LLMs and provides specific integrations for them. Cloudflare Firewall for AI, being provider-agnostic at the network edge, protects traffic flowing to any API endpoint, including those of different LLMs. However, the depth of LLM-specific threat detection can vary based on the underlying LLM's architecture. FilterPrompt's approach ensures that the bespoke security logic and detection models are applied uniformly, regardless of the target LLM, giving developers the freedom to switch or combine models without re-architecting their security layer.
Open Source Contributions and Community Engagement
In the rapidly evolving AI security space, community collaboration and contributions to open-source initiatives are strong indicators of a vendor's commitment to advancing the entire field, not just their proprietary solution. FilterPrompt actively engages with the LLM security research community, contributing to shared knowledge bases and open specifications for prompt injection taxonomy. This commitment fosters a more secure ecosystem for everyone and ensures our product benefits from collective intelligence.
While Lakera's research team publishes valuable academic work, their contributions are primarily focused on proprietary advancements. Cloudflare, as a broader cybersecurity company, has significant open-source contributions in areas like networking and web security, but less specific to LLM-centric threats. A vendor that embraces open standards and shares its insights not only builds trust but also contributes to the resilience of the overall AI landscape, which ultimately benefits its customers.