Product comparison: Detectify vs. ProjectDiscovery

ProjectDiscovery

Pros

• The entire assessment capability is built on the open-source Nuclei engine. An AppSec engineer can read, modify, and write their own nuclei templates in a simple YAML format.
• The scan logic is transparent in the YAML templates.

Cons

• The AppSec team that is comfortable with CLI tools, scripting, and writing/managing their own scan templates will get the most value.
• Limited stateful crawling – not designed for deep, stateful crawling of a single, complex application.
• Some enterprise buyers see the reliance on Nuclei as a potential limitation compared to a solution with a proprietary engine that is backed by a dedicated, private research team.

Detectify

Pros

• Its proprietary engine is powered by a private, invite-only community of elite ethical hackers. This multi-source model (which also includes an internal research team and AI) uncovers novel, emerging, and non-CVE vulnerabilities that scanners relying only on public intelligence will miss.
• The platform is built on a payload-based testing philosophy. A vulnerability is only reported if the payload successfully resolves, which confirms exploitability.

Cons

• An AppSec engineer cannot simply write and upload their own custom scan logic. They must be supported by Detectify’s internal security research team for support on implementing the tool. 
• Doesn’t capture some of the network assets that ProjectDiscovery can identify.
• Limited out of bounds and BOLA support for some testing.

In-depth comparison: Visibility and Context

Both ProjectDiscovery and Detectify are built on a modern, “outside-in” philosophy for continuous discovery and mapping. ProjectDiscovery’s approach is a transparent extension of its open-source toolchain; its Cloud Platform (PDCP) automates its popular tools like Subfinder, Naabu, and httpx to run seed-based discovery at a continuous, plan-dependent frequency. This automated toolchain finds and catalogs external assets like subdomains, IPs, and open ports. Detectify’s “Surface Monitoring” provides a very similar capability, continuously scanning the internet to discover internet-facing domains and web applications to find “shadow IT”.

A key part of mapping is adding context, which both platforms prioritize. ProjectDiscovery enriches its asset inventory with technical data like screenshots, tech stacks, and, more recently, AI-powered asset labeling. Detectify also has a strong, mature capability in this area, which is called attack surface attribution and asset classification. This process automatically catalogs assets with technical data like open ports and DNS record types and classifies their business purpose. For both tools, this classification is a crucial step in helping security teams understand their external-facing inventory beyond just a list of subdomains.

The most significant difference in approach lies in what happens after an asset is discovered and classified. ProjectDiscovery’s model is built on automatic execution. Its philosophy is to close the gap between discovery and assessment entirely. When a new asset is discovered by its workflow, it is automatically added to the inventory and queued for scanning. The platform is designed to “discover-and-scan” by default, ensuring immediate assessment of new assets.

Detectify’s approach, in contrast, is based on guided action. Detectify recommends web apps to scan. Its Surface Monitoring product discovers and classifies assets, and then provides intelligent scan recommendations on which of those assets should be targeted for a deeper DAST scan using its Application Scanning product. This “discover-and-recommend” model is designed for security teams who need to prioritize their deep-scanning resources on the most critical or high-risk assets first, rather than automatically scanning everything that is discovered which is slow and costly.

In-depth comparison: Assesment

The core assessment methodologies of both ProjectDiscovery and Detectify are engineered for the modern AppSec team, prioritizing high-signal, low-noise results. ProjectDiscovery’s approach is built on its open-source Nuclei engine, which is transparent and template-driven. It uses a vast, community-curated library of YAML-based templates to perform “behavioral checks” that validate a flaw’s existence, rather than just matching software versions. Detectify’s approach is similar in philosophy but different in execution; it uses a proprietary, in-house-built engine that is also payload-based. Detectify’s engine is designed to emulate a real-world exploit, and a finding is only triggered when the payload successfully resolves, a method explicitly designed to confirm exploitability and reduce the AppSec team’s triage workload.

A fundamental difference lies in their vulnerability sourcing. ProjectDiscovery’s strength is its speed and breadth for publicly known vulnerabilities. Because its template library is open-source, the global security community can add new detection templates within hours of a new CVE disclosure. Detectify’s value proposition is its multi-source model for finding novel and non-CVE flaws, where 75% of its vulnerabilities aren’t covered by a known CVE. This means that there are vulnerabilities in the Detectify platform that aren’t available in ProjectDiscovery. It combines its internal security research team with an AI agent (Alfred) and, most notably, the Detectify Crowdsource network—a private, invite-only community of elite ethical hackers. This allows Detectify to source and test for unique, high-impact vulnerabilities that are not in public databases and would be missed by scanners relying only on public intelligence.

For an AppSec engineer, the approach to customization is a critical distinction. ProjectDiscovery is built from the ground up for extensibility. An engineer can use the simple Nuclei YAML DSL to write their own custom templates in minutes, allowing them to codify internal knowledge and test for specific business logic flaws at scale. This is a core, hands-on feature. Detectify’s model for customization is a managed service; the documentation notes that their internal security research teams can build bespoke tests for users. This is a fundamentally different approach, offering customization as a “white-glove” service rather than a direct, self-serve capability for the AppSec engineer.

Both platforms have strong, modern API assessment capabilities. ProjectDiscovery’s Nuclei engine is protocol-aware, can ingest API specifications like OpenAPI, and can test all defined endpoints. Its extensibility also means an engineer can write custom templates to test for complex API business logic flaws. Detectify also has a proprietary API scanner, which it differentiates through dynamic fuzzing. Instead of static checks, it probes the API with randomized and rotated payloads from a massive library (e.g., 330,000+ for command injection), ensuring each scan is unique and designed to find flaws that static analysis would miss.

In-depth comparison: Usability

ProjectDiscovery’s usability is built for the hands-on technical engineer who values a “security-as-code” workflow. Its foundation in open-source, CLI-first tools means that for its target user, it is exceptionally usable. The tools are lightweight, composable, and easily integrated into custom scripts and CI/CD pipelines. Detectify, in contrast, is engineered for a different usability paradigm: that of a modern, “workflow-driven” SaaS platform. Its usability is defined by a “clean, intuitive interface” that prioritizes speed, clarity, and guiding the user to the most important action. This approach is designed to be logical, require minimal training, and reduce the engineer’s cognitive load.

Both platforms are designed for rapid time-to-value. An engineer familiar with ProjectDiscovery’s open-source tools can be running scans very quickly. The ProjectDiscovery Cloud Platform (PDCP) builds on this with a fast SaaS setup that users on G2 have cited as taking “30 minutes” to get running. Detectify’s onboarding is similarly streamlined and designed for speed. Users can get started within minutes by connecting their cloud providers to initiate asset discovery and selecting ‘scan’ on an asset. This allows the platform to provide actionable value almost immediately.

Each model presents a different trade-off. ProjectDiscovery’s power requires engineering overhead; its full value is unlocked by a technical team willing to write and manage their own custom scan templates. Detectify’s superior user experience is for the team that prioritizes speed and guided action over granular, self-serve control. Customization is available, but it is typically a white-glove service where Detectify’s internal research teams build bespoke tests for users. This is a clear contrast to ProjectDiscovery’s self-serve model.

Conclusion: Which product should I choose?

The choice between ProjectDiscovery and Detectify is a strategic one that hinges on an AppSec team’s operational philosophy, technical resources, and primary objectives. Both platforms are modern, highly effective DAST and EASM solutions that decisively move beyond legacy scanners by prioritizing high-signal, low-noise, and payload-driven results. The decision is not about which is “better,” but which is the right fit for your team’s workflow. Detectify is built for the team that values operational efficiency, guided action, and access to unique intelligence; its strength lies in a polished, intuitive workflow that minimizes triage and its ability to source novel vulnerabilities from its private ethical hacker network, allowing teams to focus their resources with precision.