Top 10 Rapid7 Alternatives
APIs have become fundamental to digital business, with over 90% of application traffic now flowing through APIs. More than 54% of enterprises say APIs directly influence revenue, and in high growth organizations, API driven services account for over 70% of new product and feature delivery. As cloud native architectures, microservices, and continuous deployment pipelines accelerate software release cycles, security must now keep up with both speed and scale. It is no longer enough to secure applications periodically. Security must be continuous, automated, and integrated into every stage of the engineering lifecycle.
However, this shift brings a significant increase in operational and cybersecurity risk. Research shows that over 63% of breaches now originate from vulnerabilities at the application or API layer. Undocumented interfaces, unmonitored endpoints, and delayed visibility can lead to compliance failures, production outages, and costly emergency remediation. With APIs increasingly carrying financial data, authentication tokens, personal information, and regulated industry data, boards and regulators are holding organizations accountable for provable security assurance and continuous monitoring.
This is where Rapid7 begins to fall short for many modern teams. Built around point in time DAST scanning, Rapid7 focuses on generating reports rather than improving security posture continuously. Vulnerabilities remain unprotected between scans, which often run weekly or monthly. More than 40% of remediation time is spent chasing issues that could have been prevented pre production. On premises scan engines create operational overhead, deployment cycles can take weeks, and full payload ingestion introduces additional privacy and audit challenges, especially in BFSI and healthcare environments. The result is high cost, limited API visibility, and slower response to real world risks.
Organizations that treat application and API security as a driver of uptime, revenue, and customer trust need an approach that delivers real time protection, full lifecycle coverage, automated remediation, minimal manual effort, rapid deployment, and privacy first data handling.
This guide presents the Top 10 Rapid7 Alternatives, evaluated by SDLC coverage, visibility depth, scalability, cost efficiency, deployment speed, and alignment with API first and DevSecOps focused delivery models.
When evaluating Rapid7’s approach to application and API security, several recurring limitations begin to impact security maturity, deployment speed, cost efficiency, and overall alignment with modern DevSecOps practices. These challenges often signal the need to consider alternatives that offer continuous protection, deeper coverage, lower manual effort, and fewer privacy or deployment constraints.
The table below highlights key triggers and explains why they matter, from unprotected gaps between scans to rising infrastructure costs and limited discovery visibility, helping teams identify where Rapid7 may fall short in real world API driven environments.
Rapid7 has long been known for its DAST capabilities and compliance driven reporting, but its approach still centers on point in time scanning rather than continuous application and API security. For teams operating in fast moving CI/CD environments, this model can leave vulnerabilities undiscovered until after deployment, driving higher remediation effort, production risk, and operational overhead. As security becomes inseparable from delivery velocity, enterprises are now leaning toward platforms that offer continuous protection, automated testing, deeper API visibility, and lower total cost of ownership.
Below are the Top Rapid7 Alternatives that provide stronger lifecycle coverage, privacy preserving architectures, and DevSecOps alignment without requiring heavy manual configuration, on premises scan engines, or constant rescan cycles.
These platforms support automated discovery, real time vulnerability detection, and integrated remediation, allowing organizations to secure APIs and applications without slowing releases.
Here’s a side by side comparison of Rapid7 vs leading alternatives across key dimensions including business value, deployment agility, privacy risk, TCO, API visibility, and SDLC coverage, helping teams quickly identify which solutions offer the best fit for modern API first and security driven engineering models.
For organizations modernizing their API security programs, teams are seeking platforms that provide deeper automation, stronger visibility, and broader lifecycle protection than Rapid7.
Here are the top Rapid7 alternatives that deliver continuous discovery, shift left testing, and runtime defense for high velocity, cloud native environments.
Levo.ai delivers continuous, full lifecycle API security built for modern, API first teams, while Rapid7 remains rooted in periodic DAST scans that leave long exposure windows between assessments. Levo identifies issues the moment they appear across dev, CI, staging, and production, eliminating the blind spots Rapid7 creates with infrequent, crawler based scans.
Using an eBPF based sensor, Levo provides deep, kernel level visibility into every API including internal, authenticated, shadow, and low traffic endpoints. It auto generates complete API documentation, maps sensitive data flows, and highlights real reachability and auth context. Rapid7 only captures externally visible traffic during scans, missing business critical APIs and generating incomplete inventories.
Levo replaces static, payload based scanning with continuous, runtime informed testing that validates every finding before alerting, reducing false positives and shrinking remediation cycles from months to days. Rapid7 outputs generic reports without reproducible payloads or dev ready guidance, slowing fixes and increasing engineering effort.
With a privacy first design, Levo ensures no sensitive data leaves the customer environment and processes less than one percent metadata in SaaS. Rapid7’s full payload capture introduces privacy, compliance, and procurement risk, especially for BFSI and healthcare.
Levo reduces infra and egress spend by nearly ten times, saving enterprises hundreds of thousands of dollars annually, while Rapid7’s repeated scans, on prem engines, and manual scripting inflate costs and delay releases. Levo deploys in under an hour with no inline agents or complex configuration; Rapid7 often takes weeks or months.
Where Rapid7 offers post build scanning only, Levo secures the entire SDLC with real time monitoring, drift detection, misconfiguration alerts, and continuous validation. Instead of treating APIs like web forms, Levo secures them as core business infrastructure, delivering stronger protection, faster delivery, and dramatically lower operational overhead.
Selecting the right API security platform depends on whether your priorities center on proactive prevention, runtime visibility, compliance assurance, or operational efficiency.
Each platform serves a distinct maturity level and team focus, and understanding where they excel or fall short helps align tools with enterprise security and growth goals.
Akamai delivers production first API security built on its edge network, focusing on WAF driven protection, basic misconfiguration detection, and traffic inspection at the CDN layer. Its visibility is limited to external north south traffic, offering no insight into internal low traffic or partner APIs. Because Akamai cannot see east west flows, sensitive data paths, or authenticated interactions, most internal shadow and zombie APIs remain undiscovered. Deployment is often heavy, requiring multi team coordination, mirroring, and gateway dependencies, and visibility is constrained by post encryption traffic capture. As a result, discovery, monitoring, and documentation remain shallow and incomplete.
Rapid7 provides point in time DAST scans aimed at compliance, not continuous security posture improvement. Its crawler based discovery misses APIs behind auth, internal endpoints, and those without linked web routes. There is no runtime monitoring, anomaly detection, or API specific testing depth, leaving production APIs unprotected between scans. Testing lacks behavioral context, auth automation, and business logic simulation, so complex issues like access control flaws, multi step patterns, and role based attacks frequently go undetected. TCO rises quickly due to scan heavy architecture, dedicated appliances, and repeated rescans for new endpoints.
Both platforms strengthen API security in limited but different ways. Akamai brings edge based production protection but lacks depth, context, and internal API visibility. Rapid7 offers surface level scans that support compliance workflows but leaves large runtime and discovery gaps. Neither solution delivers full API inventory, continuous monitoring, shift left testing, or automated remediation, which results in major blind spots across internal services, sensitive data flows, and complex business critical APIs.
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Read More: Top 10 Akamai Security Alternatives
Salt Security delivers production first API protection, built around full traffic ingestion, anomaly detection, and access control monitoring. It focuses heavily on runtime analytics, correlating user behavior, sensitive data access, and API sequence flows to detect attacks that traditional scanners miss. Salt’s value is strongest in SOC grade visibility and edge based protection, but it lacks meaningful shift left depth. There is no pre production testing, no context aware payload generation, and no automation for remediation. Coverage gaps remain across internal, partner, low traffic, and third party APIs because discovery depends heavily on gateway and WAF level visibility.
Compared to Rapid7, which relies on crawler based scans and static templates, Salt provides stronger runtime intelligence and richer behavioral analytics. Rapid7’s API coverage is shallow and limited to OWASP style DAST checks with no continuous monitoring, leaving APIs exposed between scans. However, Salt’s architecture introduces significant operational overhead. Traffic mirroring, inline agents, and full payload ingestion increase deployment friction, compliance scrutiny, and privacy risk. Its total cost of ownership rises quickly due to the heavy analytics workload.
Both platforms reinforce API security, but in different ways. Salt excels in production side anomaly detection and sensitive data visibility. Rapid7 operates primarily as a compliance oriented scanner that provides point in time assessments. Yet neither platform delivers complete API security across discovery, shift left testing, runtime correlation, and automated remediation. Critical gaps persist in internal API discovery, business logic testing, and development side prevention. This allows vulnerabilities to reach production while remediation stays slow, manual, and resource intensive.
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Rapid7 is suitable for organizations that prioritize compliance oriented point in time testing and want traditional DAST style reporting, but its lack of continuous protection, limited coverage, and heavy manual workflows make it less effective for modern API first environments.
Salt Security is the better fit for teams needing runtime attack visibility and SOC focused threat monitoring, but its production only posture, deep deployment friction, and discovery blind spots leave major security gaps before APIs reach customers.
In contrast, Levo.ai delivers end to end coverage across the SDLC with automated discovery, zero egress privacy, deep attack simulation, and minimal operational burden, ensuring enterprises ship securely without slowing development velocity.
Read More: Top 10 Salt Security Alternatives
Traceable.ai provides runtime API protection with SOC focused dashboards and analytics, detecting attacks and anomalies in production. It captures full API traffic to support monitoring and runtime discovery, but low traffic, internal, and partner APIs are often missed. Security testing is reactive, pattern based, and lacks deep behavioral simulation, multi step attack coverage, and automated remediation. Deployment requires inline agents or network mirroring, adding operational complexity and lengthy rollout times. Pre production coverage is minimal, leaving shift left initiatives unaddressed and sensitive endpoints partially exposed.
Rapid7 delivers point in time DAST scans designed primarily for compliance. It lacks runtime monitoring, anomaly detection, and continuous discovery, leaving APIs unprotected between scans. Discovery relies on crawler based approaches, missing endpoints without linked routes or inactive APIs. Testing is limited to generic OWASP Top 10 checks with no auth automation, stateful or chained attack simulation, and minimal business logic coverage. Remediation guidance is static and requires significant manual effort, slowing fixes and increasing production risk. Deployment depends on on prem scan engines and manual configuration, further delaying time to value.
Both platforms enhance API security in production but in complementary ways: Traceable.ai focuses on runtime attack prevention, SOC alerting, and forensic analytics, while Rapid7 supports compliance driven testing with periodic scans. Neither solution alone provides comprehensive shift left coverage, automated remediation, or full visibility into internal, partner, or low traffic APIs, leaving gaps across pre production and business critical endpoints.
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Rapid7 suits enterprises that prioritize traditional web vulnerability scanning and audit driven reporting but falls short for modern API first environments. Its crawl based discovery, static remediation reports, and point in time testing leave gaps that attackers can exploit, especially in fast release DevOps pipelines.
Traceable.ai is a stronger choice when the priority is real time threat detection, incident triage, and SOC led runtime defense, but its limited shift left capabilities and high data storage costs restrict full SDLC adoption.
For organizations looking to secure APIs end to end across development, staging, and production with minimal operational overhead and zero data privacy friction, platforms like Levo.ai deliver the most complete coverage and the highest business ROI.
Read More: Top 10 Traceable Alternatives
Orca Security provides cloud native, agentless visibility across APIs and cloud workloads, focusing on runtime monitoring, vulnerability detection, and sensitive data exposure without requiring inline agents or heavy instrumentation. It captures metadata and traffic patterns to map risk across the environment, generating alerts and context aware dashboards, but discovery is limited to external endpoints, and internal, low traffic, partner, or shadow APIs are often missed. Pre production testing is minimal, and remediation guidance is generic, requiring manual follow up.
Rapid7 delivers periodic, post build API scanning for compliance and vulnerability detection, but lacks real time monitoring, runtime visibility, or anomaly detection. Discovery relies on crawler based scans, missing endpoints behind authentication, inactive routes, or internal APIs. Testing is retrofit from web application scanners, limited to single request OWASP Top 10 checks, leaving business logic flaws, multi step exploits, and access control misconfigurations untested. Remediation is report based, with no automated developer mapping or payload reproduction, creating delays in patching.
Both platforms strengthen API security, but in complementary ways. Orca excels in SaaS friendly runtime visibility and metadata driven risk assessment, while Rapid7 provides compliance oriented post build scanning. Neither fully delivers comprehensive shift left coverage or automated, context aware remediation, leaving pre production and complex internal endpoints partially exposed.
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Rapid7 is suited for teams that only need periodic compliance driven assessments and traditional DAST scanning, but its lack of continuous monitoring and limited API depth mean real vulnerabilities can remain undetected until production.
Orca Security is better for cloud centric organizations seeking high level API visibility without deployment complexity, but offers minimal testing accuracy, no shift left capability, and runtime only context.
In comparison, Levo.ai remains the more complete choice for organizations that require continuous API discovery, deep attack simulation, privacy first deployment, and fully automated coverage across development, staging, and production.
Escape Security delivers API-first protection focused on runtime visibility, pre production testing, and continuous SDLC coverage. It provides kernel level traffic capture to generate enriched API catalogs and OpenAPI documentation with sensitive data flow mapping while enabling automated, context aware remediation tied to individual services and developers. Deployment is SaaS-based but streamlined, minimizing privacy risk since only metadata is processed outside the environment. Security policies and tests can be customized via YAML or Python, covering internal, partner, and low traffic APIs, and reducing manual effort to near zero.
Rapid7 relies on point in time DAST scans and crawler based discovery. This leaves APIs unprotected between scans and misses internal, shadow, and low-traffic endpoints. Testing is retrofit to web app scanners, providing basic OWASP coverage without deep business logic simulation or pre production gating. Remediation guidance is generic, requiring manual interpretation, and deployment involves heavy scan engines with complex authentication configuration. This results in high operational overhead, compute cost, and delayed time to value.
Both platforms strengthen API security but in complementary ways. Escape Security emphasizes full SDLC coverage, automated testing, and actionable runtime insights for every endpoint, while Rapid7 offers limited post build scanning for compliance reporting. Neither alone is ideal for real time SOC style alerting or production traffic prevention, leaving gaps in active runtime monitoring.
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Rapid7 is best suited for organizations treating API security as a compliance checkbox, where periodic scanning and audit visibility matter more than continuous risk reduction. It provides broad reporting but lacks the depth, automation, and runtime insight needed for modern API first architectures.
Escape Security works for teams seeking lightweight code analysis without production integration, but its lack of runtime discovery, traffic aware testing, and continuous protection makes it insufficient for organizations with meaningful API risk exposure.
Teams that require complete API security coverage across the SDLC, i.e. discovery, testing, monitoring, privacy, and automation, are better served by platforms that provide behavioral, environment aware intelligence instead of static or scan only approaches.
Inviciti delivers end to end API security across the SDLC, combining pre production and runtime coverage with automated API discovery, custom test generation, and continuous monitoring. It generates fully enriched API catalogs and OpenAPI documentation, tracks sensitive data flows, and supports developer friendly remediation with reproducible payloads and service level mapping. Deployment requires multiple components such as scan agents, network traffic analyzers, and Auth Verifier flows, making rollout complex and time consuming, but once deployed, it significantly reduces manual effort and accelerates secure delivery. Security tests are custom-built per API, automatically handling authentication and minimizing false positives, while business logic attacks, access control misconfigurations, and injection vulnerabilities are fully simulated across internal, partner, and external endpoints.
Rapid7 relies on point in time DAST scans for API security, leaving endpoints unmonitored between scans. Discovery is crawler based and misses internal, low traffic, shadow, and zombie APIs, resulting in blind spots that attackers can exploit. Testing is reactive and limited to OWASP Top 10 patterns without business logic coverage, automated payload generation, or context-aware remediation. Deployment is simpler than Inviciti but requires dedicated scan engines, frequent rescans, and manual auth setup, creating operational overhead, delayed adoption, and elevated breach risk. Remediation guidance is static, leaving developers to interpret findings manually, and production protection is limited to scheduled scans without continuous runtime monitoring.
Both platforms improve API security but in complementary ways. Inviciti emphasizes comprehensive SDLC coverage, proactive testing, and automated remediation for all endpoints, while Rapid7 focuses on compliance driven, post build scanning. Neither alone offers fully integrated runtime SOC grade alerting or real time breach prevention, leaving gaps in internal, partner, and complex business critical APIs.
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Rapid7 is suitable for organizations prioritizing regulatory compliance and broad vulnerability reporting, but its scan only approach offers limited value for fast moving or API heavy applications.
Inviciti works for enterprises needing full on-prem control and unified scanning, but coverage remains shallow, deployment complexity is high, and real API attack paths often go undetected.
Levo.ai remains the most complete option for organizations seeking continuous, privacy safe, end to end API security that improves developer velocity, reduces manual effort, and delivers true shift left coverage without sacrificing runtime protection.
Akto delivers automated API security across the SDLC, focusing on endpoint discovery, pre built security test execution, and risk based remediation. It generates dynamic OpenAPI specs with enriched metadata and provides runtime visibility for low traffic and internal APIs. Akto’s approach includes pre production testing, CI/CD integration, and end to end vulnerability coverage with minimal false positives. Deployment is straightforward with SaaS or on-prem sensors, and privacy risk is low due to scrubbing and selective traffic capture. Operational overhead is moderate, as test plans are largely pre built, but some manual configuration for auth flows and edge case scenarios is required.
Rapid7, in contrast, relies on point in time DAST scans and scheduled API testing, leaving APIs unprotected between scans. Discovery depends on crawlers and user supplied specs, often missing low traffic, internal, or third party endpoints. Testing is retrofitted, generic, and reactive, with limited business logic coverage and no automated remediation. Deployment is resource intensive, requiring scan engines and manual auth setup, while full traffic capture introduces privacy and compliance concerns. Security remains largely reactive, and shift left coverage is minimal.
Both platforms improve API security, but in complementary ways: Akto emphasizes proactive, automated pre production testing and end to end SDLC coverage with behavioral context, while Rapid7 focuses on post build, scan based vulnerability detection. Neither fully eliminates manual intervention for complex or dynamic APIs, but Akto provides stronger shift left capabilities and lower operational friction.
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Rapid7 suits teams that need traditional compliance scanning and point in time reporting, but it offers limited API depth, no continuous protection, and high manual operational effort, making it better for audits than real security outcomes.
Akto is appealing for quick out of the box scanning and basic automated discovery, but generic testing, limited attack simulation, and manual configuration mean complex business logic vulnerabilities often remain undetected.
Both leave significant blind spots: Rapid7 due to periodic DAST and Akto due to shallow, generic endpoint testing. Organizations needing continuous, automated, privacy safe API security across the SDLC should consider a platform purpose built for APIs that delivers coverage pre production through runtime with minimal overhead.
Qualys provides scheduled, scan based API and web application security, unifying vulnerability management, asset discovery, and compliance checks across cloud and on prem environments. It relies on user provided OpenAPI specifications, crawler driven scans, and network connectors to identify exposed endpoints and misconfigurations. While comprehensive in coverage of known vulnerabilities and OWASP Top 10 issues, detection is periodic and static, leaving APIs unmonitored between scans. Remediation guidance is generic and largely manual, requiring security and development teams to triage, reproduce, and patch findings.
Deployment is complex, with multiple modules, connectors, and integrations needed, and initial setup can take months for large environments. Data consolidation into the Qualys cloud introduces additional vendor induced privacy risk, especially for sensitive API payloads. Shift left security is limited, as pre production environments and low traffic internal or partner APIs are rarely included, leaving gaps in proactive security.
Compared with Rapid7, which delivers point in time DAST scans primarily for compliance reporting, Qualys offers broader platform integration across asset types and modules. However, both platforms share reactive approaches: APIs remain exposed between scans, testing is largely generic, and remediation is manual. Neither provides continuous runtime monitoring, behavioral attack simulation, or automated pre production protection, making them complementary in coverage but leaving modern API first, business critical endpoints under secured.
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Rapid7 is suitable for teams needing basic post build compliance scanning and reporting but falls short on continuous protection, attack depth, and modern API complexity.
Qualys works best for large enterprises already standardized on its ecosystem, but its API capabilities are retrofitted, operationally heavy, and limited in discovery and attack simulation depth.
Levo.ai stands out by delivering continuous, zero egress, behavior aware API security across the full SDLC with 100% API discovery, deep context driven testing, near zero manual effort, and dramatically lower cost of ownership compared to scan driven legacy tools.
StackHawk delivers API security focused on shift left testing and automated pre production vulnerability detection. It integrates with CI/CD pipelines to run context aware, authenticated scans against every API endpoint, including internal and low traffic services, providing detailed vulnerability insights and OpenAPI spec generation. Remediation guidance is actionable, with reproducible payloads and developer mapping, reducing the time to fix from weeks to days. Deployment is lightweight, requiring minimal configuration and no traffic ingestion, lowering operational overhead and privacy risk.
Rapid7 offers point in time DAST scans targeting APIs as part of a broader web application security suite. It provides compliance focused vulnerability reports but lacks continuous runtime monitoring, automated pre production testing, or context aware attack simulation. API discovery is largely crawler based, missing internal, shadow, and low traffic endpoints, while remediation guidance is generic and manual. Deployment is heavy, requiring on-prem scan engines, auth setup, and repeated rescans, creating high operational overhead and delayed time to value.
Both platforms strengthen API security but in complementary ways: StackHawk excels at shift left testing, pre production coverage, and actionable remediation, reducing developer friction and production risk, while Rapid7 provides periodic compliance focused scanning but leaves critical runtime, internal, and complex business logic vulnerabilities untested. Neither platform alone fully covers runtime monitoring or automated, context aware protection in production, leaving gaps for APIs exposed to live traffic and evolving threats.
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Rapid7 fits organizations that need compliance oriented, scheduled DAST scanning but can tolerate manual effort, blind spots between scans, and slow deployment cycles.
StackHawk suits teams seeking basic developer side scans to catch low hanging issues early, but lacks real runtime context, deep discovery, and business logic coverage needed for modern API security.
Levo.ai remains the leading option for complete SDLC wide API security, providing continuous discovery, privacy first operation, automated remediation, and deep behavioral testing powered by live runtime intelligence.
APIs underpin every modern digital workflow, but platforms like Rapid7 struggle to meet the demands of API first architectures. Reliance on generic web DAST engines, limited API coverage, and scan based workflows create visibility gaps across microservices, cloud native deployments, and internal service to service APIs.
Levo.ai addresses these gaps with unified API discovery, shift left testing, runtime protection, sensitive data mapping, and automated remediation within a single platform. Teams eliminate manual tuning, reduce false positives, and enforce continuous security across every stage of the SDLC.
For organizations that need API native security rather than retrofitted web scanning, Levo delivers complete, contextual, and production grounded coverage aligned with modern engineering velocity.
Choosing the right API security platform requires automation, depth, and real time understanding of live API behavior. Unlike Rapid7, which is constrained by scheduled scanning and limited multi step or logic aware testing, Levo offers live context, exploit aware detection, and seamless CI CD integration.
Adopting Levo allows teams to accelerate development, reduce operational load, and secure all APIs without the constraints of legacy DAST tooling.
Achieve full lifecycle API protection with Levo and build a future proof API security foundation.
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