# Equixly vs Mindgard

Equixly records 13,245 monthly visits with -34.3% month-over-month growth and Contact for Pricing pricing. Mindgard records 153,455 monthly visits with +1.3% growth and Contact for Pricing pricing.

| Metric | [Equixly](https://openaitools.app/tool/equixly) | [Mindgard](https://openaitools.app/tool/mindgard) |
| :--- | :--- | :--- |
| Website URL | https://equixly.com | https://mindgard.ai |
| Categories | AI Agent, AI Testing, AI API, AI Developer Tools | AI Testing, Large Language Models (LLMs), AI Developer Tools |
| Pricing Model | Contact for Pricing | Contact for Pricing |
| Monthly Visits | 13,245 | 153,455 |
| MoM Growth Rate | -34.3% | +1.3% |
| Primary Market | United States | United States |
| Direct Visits | 5,289 | 21,556 |
| Review Score | 4.1 | 4.3 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## Equixly

Equixly is an offensive security platform built for enterprise engineering and security teams that need continuous testing rather than a once-a-year penetration test. Its AI engine runs four specialized agents in coordination: a Discovery Agent maps endpoints, parameters, authentication states, and workflows; an Attacker Agent writes and refines payloads and adapts as the target responds; an Evaluator Agent decides whether an attack succeeded and which controls failed; and an Orchestrator Agent carries context through the whole engagement and chains individual findings into higher-impact attack paths.

The platform covers web applications, APIs, GenAI and LLM applications, and MCP servers. A core design principle is that findings are only reported behind a proof-of-exploit gate — if the Evaluator cannot reproduce an issue, it is dropped rather than surfaced as noise. This gives teams a view of exploitable risk with demonstrated impact, and supports continuous validation instead of a snapshot assessment that ages quickly.

A distinguishing claim is sovereignty: Equixly trains and runs its own models and operates its own infrastructure, so testing data such as endpoints, credentials, and tokens stays within a defined security boundary with EU data residency, rather than being routed through general-purpose AI providers. The platform integrates into CI/CD pipelines, supports continuous testing at the pace of software change, and is positioned for regulated industries including financial services, energy, SaaS, and retail. Pricing is quote-based and scales with what you test, without token metering or per-test charges.

**Pricing & Plans:** Pricing is custom and quote-based. Equixly scales cost with what you test rather than a fixed annual engagement, and there is no token meter or separate per-test charge. Book a demo or request a quote for details.

- **Multi-agent offensive security engine:** Discovery, Attacker, Evaluator, and Orchestrator agents map a target, attempt exploits, verify what actually worked, and chain findings into real attack paths.
- **Exploit-validated findings:** Every finding passes a proof-of-exploit gate, so reports focus on vulnerabilities an attacker can genuinely reach instead of unvalidated alerts.
- **Coverage across modern surfaces:** Test web apps, APIs, GenAI and LLM applications, and MCP servers across one platform, with coverage that scales from a single app to a portfolio.
- **CI/CD pipeline testing:** Trigger testing from your pipeline so applications are re-tested as they change, with no separate charge tied to test frequency.
- **Sovereign, self-run models:** Equixly owns its models and runs its own infrastructure, keeping sensitive testing data inside a defined boundary with EU data residency.
- **Predictable cost model:** Pricing scales with what you test rather than a fixed annual engagement, with no token meter or unpredictable inference bill.

**Strengths:** Exploit-validated findings reduce alert noise and focus teams on reachable risk; Multi-agent architecture chains minor issues into meaningful attack paths; Covers web apps, APIs, GenAI/LLM apps, and MCP servers in one platform; Owns its models and infrastructure, supporting EU data residency and predictable costs

[Profile (Markdown)](https://openaitools.app/tool/equixly.md)

## Mindgard

Mindgard is an AI security platform that approaches protecting AI systems the way an attacker would. Instead of starting with a checklist of prompts, it begins with agent-native reconnaissance: profiling the models, agents, tools, instructions, permissions and behaviours that make up an AI system, so it can identify the attack paths most likely to cause real damage. That reconnaissance is fed by vulnerability intelligence drawn from the company's own published disclosures.

The platform spans the full security loop. Recon surfaces what an AI deployment can actually do; red teaming continuously tests agents and systems against evolving attacks; assessment validates which weaknesses are genuinely exploitable and quantifies their impact; runtime protection and remediation guidance help teams close gaps and verify that defences hold as the system changes. This matters because AI behaviour shifts with every model update, prompt change or new tool integration, which makes one-off testing insufficient.

Mindgard originated from more than a decade of AI security research at Lancaster University and says its technology has been behind 175+ publicly disclosed vulnerabilities in prominent AI systems. It is designed to slot into existing engineering workflows through APIs, CI/CD pipelines and Burp Suite integration, so AI security assessment can run before deployment and continuously in production rather than as a periodic external exercise.

**Pricing & Plans:** Mindgard does not publish public pricing. Plans are quoted based on scope, deployment model and enterprise requirements. Contact the team for a demo and custom quote.

- **Attacker-style AI reconnaissance:** Agent-native reconnaissance maps models, agents, tools, instructions, permissions and behaviours the way an attacker would before launching attacks.
- **Continuous AI red teaming:** Continuous, adaptive testing of AI agents and systems against evolving attack techniques, rather than one-off audits.
- **Proprietary vulnerability intelligence:** Findings are backed by a knowledge base built from more than 175 publicly disclosed AI vulnerabilities across major AI systems.
- **Remediation and runtime protection:** Runtime protections and remediation guidance help close defensive gaps and verify that fixes actually hold.
- **Governance and compliance support:** Risk reduction, evidence and reporting aligned to AI policies and governance requirements, keeping AI compliant as it changes.

**Strengths:** Attacker-led methodology focuses on provably exploitable risk rather than generic prompt noise; Backed by a decade of Lancaster University AI security research and 175+ disclosures; Covers recon, red teaming, assessment, remediation and runtime protection in one platform; Integrates with CI/CD, APIs and Burp Suite for continuous assurance

**Limitations:** Requires a sales conversation; no transparent public pricing; Some capabilities overlap with other AI security vendors, so evaluation takes care; Fully realising value assumes integration into CI/CD and security workflows

[Profile (Markdown)](https://openaitools.app/tool/mindgard.md)

## Frequently Asked Questions

### How do Equixly and Mindgard compare in monthly traffic?

Equixly has 13,245 monthly visits, while Mindgard has 153,455. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Equixly has a month-over-month growth rate of -34.3%; Mindgard has +1.3%.

### How does pricing compare?

Equixly: Contact for Pricing. Pricing is custom and quote-based. Equixly scales cost with what you test rather than a fixed annual engagement, and there is no token meter or separate per-test charge. Book a demo or request a quote for details. Mindgard: Contact for Pricing. Mindgard does not publish public pricing. Plans are quoted based on scope, deployment model and enterprise requirements. Contact the team for a demo and custom quote.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Aikido Security](https://openaitools.app/tool/aikido-security) | 757,491 | +9.6% | Freemium | All-in-one AppSec platform that finds and auto-fixes vulnerabilities | [.md](https://openaitools.app/tool/aikido-security.md) |
| 2 | [Arize AI](https://openaitools.app/tool/arize-com) | 249,028 | +3.2% | Paid, Contact for Pricing | Agent observability, evaluation, and continuous improvement for production AI | [.md](https://openaitools.app/tool/arize-com.md) |
| 3 | [ProjectDiscovery Neo](https://openaitools.app/tool/projectdiscovery-neo) | 158,530 | +22.5% | Paid, Contact for Pricing | AI security platform that finds exposures, proves they're exploitable, and ships the fix | [.md](https://openaitools.app/tool/projectdiscovery-neo.md) |
| 4 | [Hacktron](https://openaitools.app/tool/hacktron) | 125,526 | -40.9% | Freemium, Paid, Contact for Pricing | Automates continuous red teaming and security reviews, but lacks Mindgard's dedicated machine learning vulnerability scanning suite. | [.md](https://openaitools.app/tool/hacktron.md) |
| 5 | [Future AGI](https://openaitools.app/tool/future-agi) | 121,741 | -7.8% | Freemium, Usage-based | Provides open-source LLM simulation and guardrails, but requires more manual setup for enterprise security testing than Mindgard. | [.md](https://openaitools.app/tool/future-agi.md) |
| 6 | [Maxim AI](https://openaitools.app/tool/maxim-ai) | 121,351 | +10% | Contact for Pricing, Free Open Source | Evaluate, monitor, and govern your AI applications in production | [.md](https://openaitools.app/tool/maxim-ai.md) |
| 7 | [Lakera Guard](https://openaitools.app/tool/lakera-guard) | 88,435 | -24% | Contact for Pricing | Specializes in real-time runtime security against prompt injections, but lacks comprehensive offline model vulnerability scanning. | [.md](https://openaitools.app/tool/lakera-guard.md) |
| 8 | [Confident AI](https://openaitools.app/tool/confident-ai) | 83,400 | -3.3% | Freemium, Paid, Contact for Pricing | Offers robust LLM testing and red teaming tools, but is tailored more toward developer evaluation than enterprise security auditing. | [.md](https://openaitools.app/tool/confident-ai.md) |
| 9 | [Openlayer](https://openaitools.app/tool/openlayer) | 48,415 | -6.4% | Freemium, Contact for Pricing | Focuses broadly on AI governance and compliance monitoring, but offers less specialized autonomous model red teaming. | [.md](https://openaitools.app/tool/openlayer.md) |
| 10 | [Fiddler AI](https://openaitools.app/tool/fiddler-ai) | 44,204 | -10.2% | Contact for Pricing | Delivers enterprise-grade AI observability and model governance, but provides less automated vulnerability red teaming. | [.md](https://openaitools.app/tool/fiddler-ai.md) |

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Source: https://openaitools.app/compare/equixly-vs-mindgard

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