Rapid7
Pros
- Correlates web app vulnerabilities with underlying infrastructure risk and active threat data.
- Provides expansive enterprise discovery of unknown web applications and open ports.
Cons
- Lacks an automated “discovery-to-scan” workflow for newly found applications.
- The DAST engine is not optimized for modern apps and can create significant false positive noise.
- Requires users to purchase several products to get desired results.
Detectify
Pros
- Payload-based testing provides high-confidence, exploitable findings that minimize triage time.
- Novel Vulnerability Sourcing – finds novel, non-CVE vulnerabilities sourced from its private community of elite ethical hackers .
Cons
- Cannot scan non-public, internal applications as it lacks an on-premise or agent solution.
- Writing new, application-specific scan logic is not a self-serve feature for engineers .
In-depth comparison: Visibility and Context
For an AppSec team, the primary visibility challenge is frankly not a lack of data, but too much irrelevant data. The core use case is discovering the external attack surface—specifically, finding all internet-facing web applications, subdomains, and APIs, including “Shadow IT” that was deployed without much visibility within an org. The challenge is to get a complete and actionable inventory of these web assets. Without this, an AppSec team cannot even begin to assess risk, as their biggest vulnerability is the unknown application they’ve never scanned.
Rapid7’s approach to visibility is platform-centric, driven by a combination of products like its Surface Command (EASM) and InsightVM products. You provide known domains, and its internet-wide scanning engine (Project Sonar) builds a map of associated subdomains, IPs, and open ports. Rapid7 then correlates this external discovery data with internal vulnerability data from InsightVM (e.g., “this external web app is running on an unpatched server”) and active threat data from InsightIDR (e.g., “our SIEM sees probes hitting this asset”). This is comprehensive, but presents a challenge to a lean AppSec team if you don’t have the bandwidth to onboard, manage and take action on this data.
Detectify’s approach is purpose-built for the AppSec use case. Its external attack surface discovery also works from an “outside-in” perspective, but its focus is exclusively on the application layer. The context it provides is not about internal server posture but about the application’s technology stack. Detectify automatically discovers and classifies assets based on their web technologies (e.g., “this is a WordPress site,” “this is a Java web server”). This AppSec-specific context is then used to explicitly provide “Scan Recommendations” for newly discovered web apps that the team may have missed and which are potential attack targets.
In comparison, Rapid7’s approach is designed to give a Security Operations Center (SOC) a unified view of all risk, combining infrastructure, cloud, and application vulnerabilities with active threats. For an AppSec team, this is powerful but can be noisy, mixing application-layer flaws with OS-level patching concerns. Detectify’s approach is more precise, it is designed to filter out noise and provide the AppSec team with a clear inventory of the external web applications and APIs, classified by their technology and what you should scan.
In-depth comparison: Assessment
Once an app is discovered, the core AppSec use case is to test it for vulnerabilities. The primary challenge for an AppSec team is not just finding vulnerabilities, but cutting through the noise. Teams are overwhelmed by high-volume, low-impact findings and outright false positives from traditional scanners. These legacy DAST tools often fail to crawl and test modern apps and complex APIs, creating a critical false sense of security where an AppSec team believes an asset is “scanned” and “secure” when it hasn’t actually been tested effectively.
Rapid7’s approach to assessment is driven by its DAST product, InsightAppSec. This tool is integrated into the broader “Command Platform” and acts as a black-box scanner, crawling a running application and launching attacks to find flaws like XSS and SQL Injection. Its primary prioritization benefit comes from this integration: a vulnerability found by InsightAppSec is enriched with infrastructure data from InsightVM and threat intelligence from AttackerKB. This creates a unified “Active Risk” score, which helps an AppSec engineer prioritize a web vulnerability based on the holistic risk of the underlying asset it’s running on. Its API testing functions similarly, typically requiring a user to upload a specification file (like OpenAPI) to guide the DAST scanner.
Detectify’s assessment philosophy is built around exploitability and practitioner-focused results. Its primary differentiator is that all its tests are payload-based, designed to provide proof of exploitability. This approach aims to drastically reduce the false positive rate and validation overhead that burdens AppSec engineers. Detectify’s vulnerability tests are sourced from its Detectify Crowdsource platform, a private community of elite ethical hackers who submit real-world, cutting-edge exploits. Detectify also leverages its internal security research team and Alfred, an AI agent that finds relevant CVEs and builds tests for them that are reviewed by that same internal research team. This provides deep, specialized coverage in complex areas, like subdomain takeovers. For API testing, the platform offers a dynamic, modern scanner with an “innovative payload rotation capability,” positioning it as a dedicated solution rather than an extension of a legacy web scanner.
In comparison, the two platforms solve the assessment challenge differently. Rapid7 is ideal for a SOC-driven or “top-down” security program that needs to correlate application flaws with the total risk posture of an asset. It answers the question, “Which of my applications is on the riskiest server?” Detectify is purpose-built for the “bottom-up,” practitioner-focused AppSec team. It ignores infrastructure context and instead focuses on providing high-confidence, low-noise, exploitable findings sourced from active research. It answers the question, “Which of my applications has the most exploitable vulnerability?”
In-depth comparison: Usability
For an AppSec team, “usability” is not just a clean UI, it is a measure of efficiency. The primary usability challenge is the high-friction workflow between discovery and remediation. This includes the high rate of false positives that eat away at an engineer’s time, configurating authenticated scans for modern single-page applications (SPAs) and APIs, and the manual effort required to even decide what to scan. A usable tool reduces this “time-to-triage” and surfaces high-confidence, actionable findings with minimal manual intervention.
Rapid7’s usability is centered on its integrated Command Platform. For a manager, the usability is high, as it provides a single, intuitive interface to see all risk (infrastructure, cloud, application) in one place. For the AppSec practitioner, however, this usability breaks down at the workflow level. There is a significant manual gap—Surface Command discovers a new web application, but an engineer must then manually onboard it into InsightAppSec, configure the scan scope, and, most painfully, “teach” the scanner to authenticate, which is a notorious challenge for modern applications. User reports also suggest InsightAppSec struggles to effectively scan these modern JavaScript-heavy apps, leading to configuration friction and a false sense of security.
Detectify’s usability, by contrast, is designed for the AppSec practitioner’s workflow. It’s simple to set up and manage, but at its core is its signal-to-noise ratio. Its payload-based testing model, sourced from the Detectify Crowdsource community, internal security research team, and Alfred (AI agent) is built to provide exploitable, high-confidence findings, drastically reducing the time engineers spend validating findings. The platform’s “Scan Recommendations” feature attempts to automate the AppSec engineer’s triage process by proactively identifying which newly discovered assets should be prioritized for testing, bridging the manual gap seen in other platforms. This focus extends to its API testing, which is described as a modern tool with an innovative payload rotation, suggesting it’s a purpose-built, usable solution, not a legacy feature.
In comparison, Rapid7’s usability is designed for the SOC and the CISO, offering a unified management dashboard that creates significant manual work for the AppSec practitioner. Detectify’s usability is purpose-built for the AppSec team. It sacrifices the all-in-one infrastructure view in favor of a low-friction workflow that prioritizes high-confidence, exploitable findings, aiming to get the engineer from discovery to a validated, high-priority ticket as quickly as possible.
Conclusion: Which product should I choose?
Ultimately, your choice between Rapid7 and Detectify is a strategic decision that hinges on your AppSec team’s primary charter and biggest operational bottleneck. If your team’s role is to feed application data into a larger, SOC-driven vulnerability management program and correlate web flaws with holistic infrastructure risk, Rapid7’s unified platform is built for that purpose. However, if your team is the practitioner-in-the-loop, and your primary challenge is the daily grind of triage, the noise of false positives, and the fear of unknown “shadow IT,” Detectify is the purpose-built solution. It is engineered to solve the AppSec-specific use case: providing a high-confidence, low-noise stream of exploitable findings sourced from elite hackers , allowing your team to focus on remediation, not just reporting.