Alessio Dalla Piazza, Zoran Gorgiev
Table of contents #
- Equixly vs. XBOW at a glance
- Local vs. cloud AI inference: The core difference
- In-house AI vs. frontier models: Which finds more vulnerabilities?
- What continuous security testing costs: Tokens vs. subscription
- Security testing speed: Third-party AI dependencies vs. in-house AI
- Data privacy: local inference vs. cloud LLM exposure
- Equixly vs. XBOW: Attack surface coverage
- Compliance, data residency, and customer support
- How Equixly and XBOW integrate into your workflow
- Conclusion
Software ships faster than any security team can test it by hand, period. AI coding assistants and no-code tools let almost anyone stand up a web app or an API in an afternoon, and each new service widens the attack surface more. Periodic manual pen tests, conducted only a few times a year, were never designed to keep up with that pace. You just have to test the code at the speed at which the code is written.
Equixly and XBOW are two answers to that problem. Both send autonomous AI agents to attack running web applications, chain vulnerabilities into working exploits, prove those exploits are real, and hand back findings a developer can fix. Both aim to replace the slow, calendar-bound pen test with something continuous. On the core promise, they agree.
Where they part ways, among others, is architecture. The two are built on opposite bets about where the AI should run. And those bets ripple outward into cost, speed, data handling, and how much of your attack surface each one covers.
This piece walks through the Equixly vs. XBOW differences. Here is what you will take away at the end of the read:
How each platform runs its AI, and why that changes your cost and data path** What each one coverson your attack surface Where compliance, data residency, and support**influence your decision
Equixly vs. XBOW at a glance #
Equixly | XBOW | | |---|---|---| AI model | In-house, trained only on offensive security; runs on own infrastructure | Cloud-routed third-party frontier models | Pricing | Flat license; effectively unlimited testing | Per-token; cost rises with frequency | Speed | ~1 hour per app; no external dependency | Depends on external model availability (latency, rate limits, provider downtime) | Data & residency | Stays inside one boundary; EU data residency guaranteed | Context sent to external providers; no EU residency guarantee | Attack surface | APIs (REST/SOAP/GraphQL), web apps, LLM endpoints, MCP servers | Web apps (primary); partial API coverage | Compliance | ISO 27001 certified as a company | Not certified; audit-ready reports aligned to frameworks |
Local vs. cloud AI inference: The core difference #
XBOW is a cloud platform. Its agents reason by calling frontier models from third-party providers, and the company routes each task to the external model that fits it best, adopting new ones as they ship.
This design gives XBOW access to the strongest general-purpose reasoning available at any moment. But it also means the reasoning lives outside your walls and outside XBOW’s own walls.
Equixly takes the other road: Its AI is trained and run in-house on the company’s own infrastructure. A scan makes no calls to an outside model provider. The engine was written from scratch for one job: attacking APIs, web apps, LLMs, and MCP servers.
XBOW’s model routing keeps it close to the frontier without owning the training burden. Equixly’s in-house engine gives up that plug-in-the-newest-model flexibility in exchange for control over cost, speed, and data. For security teams that test continuously and answer to auditors, that trade tends to matter more than raw access to the latest public model.
In-house AI vs. frontier models: Which finds more vulnerabilities? #
It’s the natural objection, and it deserves a straight answer: for this kind of work, the in-house model does.
Why specialization can outperform frontier-grade reasoning
Frontier models are built to be good at almost everything at once: writing, coding, math, conversation, and yes, security reasoning when prompted into it. That breadth is the point of a frontier model, and it’s genuinely impressive.
However, vulnerability hunting isn’t an open-ended task. It’s a narrow, well-mapped one, where a finite set of recurring patterns shows up again and again across different applications, such as Broken Object Level Authorization (BOLA), injection points, and logic flaws in multi-step API flows.
It’s also why frontier-grade reasoning isn’t the requirement here. These are familiar API and web flaws, not a browser-engine zero day like a V8 exploit that needs a model working at the very edge of its ability.
A generalist model must reason its way to that pattern each time, guided by a prompt. But a model trained on nothing but attack chains and exploit sequences already has that pattern baked into its reasoning, without needing to be talked into the role.
That’s the same logic behind why a narrow classifier often beats a general-purpose model at a task it was never built for. Depth on a specific problem can outperform breadth across many, especially when the problem has a fixed shape.
The difference between elicited and trained exploit-finding behavior
XBOW’s benchmark results are real, and they show what strong orchestration around a capable frontier model can achieve. What they don’t show is a model whose training was ever about finding these specific flaws; in this case, the exploit-finding behavior is elicited, not intrinsic.
It can’t be otherwise. The strongest frontier models can’t be fine-tuned by the platforms renting them, so those platforms can only prompt the behavior into place, not train it in.
Equixly built its model around that gap. It starts from an open-weight base and trains it on nothing but offensive security work: attack patterns, authorization failure modes, business logic abuse, and API call sequences that chain one weakness into the next. The platform also uses proprietary algorithms to map API endpoints and prioritize which ones matter most and which findings to fix first.
Pairing the model with deterministic algorithms is deliberate. An LLM isn’t always the right tool, and LLM output is nondeterministic by design. That means the same test can return different findings from one run to the next, which is a problem that a hosted frontier model only compounds as its provider swaps in newer models underneath you.
Equixly leans on that algorithmic logic to stabilize results and keep findings reproducible.
Since finding and proving exploitable flaws is the only work the model has ever learned, it reasons in exploit chains by default, instead of having that behavior coaxed out of it one prompt at a time.
Why the model and orchestration must work as one system
But the model is only half of what finds the vulnerability. The other half is the harness around it: the orchestration that chooses the attack, sequences the steps, and confirms the result.
As Equixly built that orchestration to work with a model trained to think like an attacker, the two operate as a single system that surfaces flaws a wrapped general-purpose model would miss.
A platform renting a frontier model can only wrap orchestration around it after the fact, and can swap in a newer model whenever one ships. But it cannot reproduce a model and an orchestration layer that were built for each other from the start.
Tooling is part of what’s built together: Equixly builds the tools its model uses instead of generating fresh exploit code on each run. Generating code afresh each time is brittle, since it can skip the established techniques that a purpose-built, battle-tested tool applies every time.
The operational risks of depending on a frontier-model provider
There’s a structural edge as well. A platform that reasons through a frontier model depends on that provider’s willingness to allow offensive use at all. Access to that class of capability is granted under the provider’s policy, and it can be narrowed, gated, or withdrawn as that policy shifts. That’s a decision made outside the security vendor and outside the customer.
It plays out at test time, too. The model’s built-in safety filters can refuse an offensive request mid-scan, leaving part of your attack surface untested for reasons that have nothing to do with the target.
Equixly’s model sits under no such policy. Attacking is what it was built to do, and nothing upstream can decide otherwise.
What continuous security testing costs: Tokens vs. subscription #
Every reasoning cycle an XBOW agent runs consumes tokens from an external model, and those tokens carry a price. More scans mean more calls, which means more variable costs.
This model is financially feasible for periodic assessments. But it gets harder to plan when you want a scan on every pull request, a nightly run across your portfolio, and a full pass before each release. The bill moves with your testing habits, which is the opposite of what a budget wants.
Equixly folds the AI cost into a flat license. Since inference runs on the company’s own hardware, each scan carries no per-token charge. The practical result is that:
- Daily scans, CI/CD runs, and pre-release checks add nothing to the invoice. - Testing more often does not push the cost up. - Your testing budget is one fixed number, set by the applications you cover, not by how often you test.
If you run a handful of pen tests a year, the token model is fine. If you want security testing to run constantly or be wired into the pipeline, as Gartner recommends, a flat cost is far easier to defend.
Security testing speed: Third-party AI dependencies vs. in-house AI #
XBOW’s testing time depends on the availability of the external models it calls. Consequently, you must factor in contingencies such as network latency, rate limits, and provider downtime because the reasoning happens somewhere else. That is the accepted cost of borrowing frontier models instead of owning them.
Equixly’s engine has no third-party AI in the loop. Its proprietary AI, housed and managed internally, runs each security test as an isolated job with automatic horizontal scaling. As a result, a security test of an average application finishes in roughly an hour.
Nothing waits for an outside API to answer. Removing that dependency also removes a class of failures you would otherwise have to design around.
Data privacy: local inference vs. cloud LLM exposure #
To reason about your application, a cloud-model platform must send it context: API schemas, specifications, and sometimes payloads. That context becomes prompt input to the third-party model. The problem is that prompts routinely carry secrets, such as API keys, tokens, or credentials embedded in real requests. That implies a concrete confidentiality risk every time a scan runs.
With XBOW, the context reaches external model infrastructure that the customer does not control. The tool lets you define scope, logs every action, and aligns deployment to data separation and residency needs.
Still, the reasoning path crosses provider boundaries because that is how a cloud-routed model works.
Equixly’s data privacy guarantees come from keeping the whole process inside one boundary:
Dedicated tenant: One customer’s data is never reachable by another.Tenant isolation: There is no shared network or compute between customers.No training on customer data: The models learn from synthetic data the company generates internally instead of from what your security tests touch.No external telemetry during security tests: Nothing about the run goes to an outside provider.EU data boundary: Your data does not leave the European Union.
For a healthcare provider handling patient records or a finance firm under strict residency rules, “the reasoning never leaves our boundary” is a far better option than “the reasoning crosses a provider we vetted.”
Equixly vs. XBOW: Attack surface coverage #
XBOW built its reputation on autonomous testing of web applications, and it is strong there. Its agents chain vulnerabilities into working exploits and validate each one, which cuts the false positives that plague less disciplined AI tools. That track record is public: a top finish on HackerOne’s US leaderboard.
So, XBOW concentrates on web application pen testing, with some API coverage inside those assessments.
Equixly, on the other hand, was built around APIs from the outset and has reached much further into the contemporary attack surface:
- REST, SOAP, and GraphQL APIs
- Web applications
- LLM endpoints (
[AI red teaming](https://equixly.com/glossary/ai-red-teaming/)) - MCP server architectures
This coverage maps to the frameworks today’s regulated teams live under: OWASP API Top 10, ASVS, PCI DSS, PSD2, ISO 27001, DORA, and NIS2.
Both platforms handle business logic testing. There’s a structural wrinkle worth noting, though: Business logic increasingly lives in APIs, and since XBOW only partially supports API security testing, its reach into that logic is narrower than Equixly’s.
On measured performance, Equixly reports a detection rate above 90% across more than 200 endpoints carrying real-world vulnerabilities, with a false positive rate under 1%. Behind that sit over 100 specialized security agents, backed by thousands of tests.
XBOW reports an 85% success rate across its own suite of 104 public benchmark challenges, a figure the company frames as comparable to what an experienced pen tester achieves in a week. Those benchmarks are capture-the-flag style, so on that suite, false positives fall away by design, meaning the agent either retrieves the correct flag or it doesn’t.
The two sets of numbers aren’t directly comparable. Equixly’s is a detection rate on a private endpoint set, and XBOW’s is a solve rate on public CTF-style challenges. Each simply shows what the vendor measures and reports.
Compliance, data residency, and customer support #
A few differences weigh heavily for European and regulated buyers.
Equixly is ISO 27001 certified as a company and guarantees that data stays within the EU, which speaks directly to digital sovereignty concerns as geopolitical uncertainty grows.
XBOW, a US-based company headquartered in Seattle, approaches compliance from the other direction: Rather than holding the certification itself, it produces audit-ready pen test reports and aligns its deployment to help you meet your own framework requirements, including SOC 2, ISO 27001, PCI DSS, and NIS 2. What it does not offer is EU data residency as a built-in guarantee.
Support is the other quiet differentiator. Equixly is a European vendor with a support team that works directly with customers and a Solutions Engineer to help with integration and adoption. In practice, that means:
Direct access to the people who built the product, not a tiered support queue, and faster responses than a larger, more distant vendor tends to offer** A team that understands the broader European regulatory landscapefirsthand Hands-on help wiring the platform into your environment**from day one
Equixly also holds an international patent on its technology, which is a signal that its core methods are hard for competitors to copy. And it has been recognized through the UniCredit Startup Award, the BCG Innovation Challenge, Google for Startups AI & Cybersecurity, the OVHcloud Top Scaleup Award 2026, and inclusion in the Gartner Hype Cycle 2025 and 2026.
How Equixly and XBOW integrate into your workflow #
Both platforms are built to plug into your workflow.
Equixly integrates natively with CI/CD, GitHub, GitLab, Azure DevOps, Jira, ServiceNow, Wiz, Checkmarx, Qualys, and SIEM tools through webhooks. Its Space Link feature lets you test applications on private networks safely, without opening extra ports.
The platform can run fully autonomously on its own schedule, and it can also hook into the CI/CD pipeline when a team wants scans to fire as code ships. Either way, the point is the same: catch vulnerabilities as they appear instead of months later.
XBOW exposes a REST API to launch pen tests programmatically and supports webhooks that push findings to your workflow. Teams typically wire it in as a pre-release gate: A scan fires on merge, and only proven findings reach SIEM and ticketing tools.
XBOW also offers regional endpoints, including one in the EU. However, that is a service region for calling the API, not the EU data-residency guarantee discussed earlier.
The practical difference is not whether Equixly or XBOW can fit into how you work; they both can. It is more about what continuous use costs and achieves. To restate the earlier cost point in this context:
- XBOW’s per-test pricing makes constant runs a budgeting question. - Equixly’s flat subscription, by contrast, is built for high-frequency testing, and its Space Link extends that to applications on private networks, which is coverage that XBOW doesn’t advertise.
Conclusion #
Equixly and XBOW agree on the goal: continuous, autonomous testing that keeps pace with the changes in your environment. They differ on how to reach it, and that can make all the difference in the world:
XBOW bets on cloud-routed frontier models and the reasoning power that comes with them.Equixly bets on an in-house engine, and everything downstream follows from that one choice, including flat pricing, no external dependencies, data that never leaves your boundary, and coverage that spans APIs, web apps, LLMs, and MCP servers.
Therefore, if you want testing wired into the pipeline and firing constantly, under EU residency and a budget you can name in advance, that is the case Equixly is built for.
Put Equixly to the test on your own attack surface. Book a demo.
FAQs #
How much does Equixly cost?
Equixly has a single plan: a continuous offensive security testing license that caters to enterprises. The pricing is based on the number of applications you want covered, and within that scope, testing is effectively unlimited. More precisely, you can run a pen test on a covered application whenever you want, as often as you want, at no extra cost.
How do I get started with Equixly or run a proof of value (PoV)?
Book a demo to see the platform run against your own targets. That’s the natural starting point for a proof of value, since you see exactly what Equixly finds in your environment. When you are ready to move forward, the team helps you scope the license around the applications you want covered.
What happens to findings after a scan, and how do they reach my developers?
Every exploit-validated finding comes with context-aware risk insights and actionable remediation guidance. Findings flow directly into your existing vulnerability management and ticketing tools, meaning they land in the workflows your engineers already use. Equixly then retests to confirm each fix closes the exploit path.
Which compliance frameworks does Equixly help me meet?
Equixly provides continuous, evidence-backed validation aligned to the OWASP API Top 10 and ASVS, PCI DSS, PSD2, and ISO 27001. It can also provide validated proof that helps you meet DORA and NIS2. It gives you ongoing evidence of control effectiveness for audits rather than a once-a-year snapshot.
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Alessio Dalla Piazza
CTO & FOUNDER
Former Founder & CTO of CYS4, he embarked on active digital surveillance work in 2014, collaborating with global and local law enforcement to combat terrorism and organized crime. He designed and utilized advanced eavesdropping technologies, identifying Zero-days in products like Skype, VMware, Safari, Docker, and IBM WebSphere. In June 2016, he transitioned to a research role at an international firm, where he crafted tools for automated offensive security and vulnerability detection. He discovered multiple vulnerabilities that, if exploited, would grant complete control. His expertise served the banking, insurance, and industrial sectors through Red Team operations, Incident Management, and Advanced Training, enhancing client security.
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Zoran Gorgiev
Technical Content Specialist
Zoran is a technical content specialist with SEO mastery and practical cybersecurity and web technologies knowledge. He has rich international experience in content and product marketing, helping both small companies and large corporations implement effective content strategies and attain their marketing objectives. He applies his philosophical background to his writing to create intellectually stimulating content. Zoran is an avid learner who believes in continuous learning and never-ending skill polishing.