Top 10 Orca Security Alternatives
APIs have evolved from backend connectors into core business engines. Today, 35–62% of enterprises generate direct revenue from APIs, and for nearly a quarter of organizations, APIs contribute more than 75% of total revenue. API first teams drive faster releases, higher revenue retention, and stronger customer trust by embedding security throughout their development and delivery lifecycle. As microservices, DevOps pipelines, and cloud native architectures expand, APIs increasingly determine how quickly companies innovate and how reliably they operate.
But with growth comes risk. Over 70% of APIs remain undocumented or lightly monitored, and 60% of enterprises report at least one API related incident each year. Unmapped endpoints, missing documentation, limited pre production testing, and reactive vulnerability detection expose organizations to compliance failures, data leakage, and costly release delays. With APIs now carrying sensitive financial, personal, and healthcare data, the regulatory pressure for continuous monitoring, proactive defense, and privacy preserving security has intensified. Traditional, runtime only visibility or cloud centric posture tools are no longer enough.
Orca Security, while strong in cloud posture and workload visibility, provides only surface level API insights. Its capabilities are restricted to production, with no shift left testing, no automated documentation, limited API discovery, and no deep data flow mapping. Because Orca processes full customer data in its SaaS environment and cannot observe east west traffic or internal API behavior, critical blind spots persist across microservices, partner APIs, and low traffic endpoints. Without remediation automation or behavioral testing, teams face slow detection, manual triage, and increased risk of runtime exposure.
Enterprises that view API security as essential to revenue, uptime, trust, and engineering velocity require a platform that delivers continuous protection, full lifecycle coverage, automated remediation, rapid deployment, and a privacy first data model.
This blog highlights the Top 10 Orca Security Alternatives, evaluated across API visibility, shift left enablement, runtime depth, deployment complexity, cost efficiency, and alignment with modern API first engineering teams.
When assessing Orca Security for API focused programs, several recurring limitations emerge around context depth, coverage completeness, deployment flexibility, and alignment with modern API first engineering. While Orca excels in cloud posture management, its API security capabilities remain surface level, leaving gaps in discovery, runtime insight, and actionable remediation. These constraints often signal the need for alternatives built specifically for API heavy, microservices driven environments that demand continuous visibility, low overhead deployment, and precise, context-aware detection.
The table below outlines the key triggers and why they matter, from fragmented API visibility to limited runtime correlation, helping teams identify where Orca may fall short in real world API driven environments.
Orca Security is positioned as a cloud security posture and vulnerability management platform, but its capabilities stop short when it comes to API security. Its visibility is limited to production environments, without shift left testing, automated API documentation, internal API discovery, or deep data flow mapping. Because Orca processes full customer data in its SaaS and lacks kernel level instrumentation, critical gaps emerge across east west traffic, low traffic endpoints, and dynamic microservices. For engineering teams, this results in blind spots, slow detection, and a reactive approach to API risk.
Modern API security platforms offer continuous, runtime grounded discovery, automated remediation, high fidelity testing, and privacy preserving architectures that keep sensitive data inside customer environments. They reduce manual workload, accelerate developer velocity, and provide full lifecycle coverage from design to production. For enterprises that prioritize API security depth, cost efficiency, and real time protection, these solutions deliver significantly stronger outcomes than Orca’s production only, cloud centric model.
Below are the leading Orca Security alternatives that deliver these capabilities:
These platforms support automated discovery, real time vulnerability detection, and integrated remediation, ensuring APIs are continuously protected without slowing CI/CD pipelines.
Here’s a side by side comparison of Orca Security versus Levo.ai, highlighting core business value, deployment agility, privacy risk, TCO, API visibility, attack simulation, and SDLC coverage, helping teams quickly identify which solution aligns best with modern, DevSecOps driven API security models.
For modern API driven ecosystems, teams are evaluating platforms that offer deeper API visibility, faster security validation, and broader protection coverage than Orca Security.
Here are the top Inviciti alternatives that unify API discovery, automated testing, and runtime defense for cloud native and rapidly scaling environments.
Levo.ai is built for modern, API first engineering teams that need full lifecycle API security rather than production only snapshots. Unlike Orca Security, which focuses on cloud posture and provides limited API visibility without influencing development velocity, Levo secures APIs continuously from design to deployment, enabling teams to ship faster, safer, and with complete compliance confidence.
Powered by an eBPF based sensor, Levo delivers deep, kernel level visibility into internal, external, partner, shadow, zombie, and low traffic APIs. It automatically generates complete API documentation enriched with more than 12 metadata parameters, maps sensitive data flows, and continuously discovers new endpoints across all environments. Orca’s API visibility is restricted to external discovery and basic data mapping, leaving internal and east west traffic entirely unobserved.
Levo transforms API security from passive monitoring to proactive, exploit aware defense. Every test plan is custom built per API based on real runtime behavior, using live data to eliminate false positives. Vulnerabilities are verified before alerts are raised, and remediation is automated through reproducible payloads, developer mapping, and auto generated patch code, reducing cycles from months to days. Orca offers no API testing, no simulation of complex exploits, and no remediation support, forcing teams to rely on manual pentesting and fragmented workflows.
Levo’s privacy first architecture ensures that all sensitive data stays within the customer environment and less than 1% metadata is processed in its SaaS control plane. This eliminates vendor induced privacy risk and avoids compliance bottlenecks. In contrast, Orca processes full customer data sets in its SaaS, including sensitive information, resulting in heavy bureaucracy, DPIA requirements, and increased exposure risk.
Levo also offers dramatically lower total cost of ownership, reducing egress and cloud processing expenses by up to 10 times and saving $100,000 to $500,000 annually. By contrast, Orca’s lack of smart data capture increases cloud costs and operational overhead without improving API security depth.
Integrated directly into CI CD pipelines, Levo enables shift left testing, real time validation, and automated policy enforcement using YAML and Python rules. Deployment completes in under an hour with minimal DevSecOps resources and supports SaaS, hybrid, and full on premises installations. Orca offers no on premises support, no CI CD level enforcement, no early stage security coverage, and no ability to customize controls or test logic.
Where Orca stops at production only visibility and cloud posture alerts, Levo delivers end to end API security that accelerates releases, eliminates manual work, and protects every API with unmatched coverage, automation, and privacy.
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.
Provides full API visibility and discovery: internal, external, shadow, zombie, and third party APIs, enriched with auth, sensitivity, reachability, and runtime context.
Akamai provides edge centric API protection by extending its WAF and CDN capabilities into API security. Its approach is production first and visibility is limited to what the edge can observe, which means only external, internet facing APIs with enough traffic are detected. Internal, partner, third party, low traffic, and shadow APIs remain outside its discovery boundary. Runtime detection hinges on gateway or WAF level metrics rather than true application context, leading to blind spots across sensitive data flows, east west traffic, and dynamic API behavior. Testing is minimal, shift left coverage is absent, and remediation guidance is generic, so vulnerabilities frequently move from staging to customers without deep validation.
Orca Security focuses entirely on cloud asset posture with agentless scanning of cloud resources, containers, and storage. API visibility is narrow because discovery is limited to external endpoints and misconfiguration analysis based on provided documentation. It cannot observe runtime behavior, does not generate or reconcile API documentation, and offers no security testing. Sensitive data mapping is limited and dependent on metadata rather than payload inspection or traffic context. Since there is no runtime telemetry, breaches and anomalies surface only after logs or external alerts reveal them. With no on prem option and all data processed in its SaaS environment, privacy and compliance reviews slow adoption, especially for regulated sectors.
Both Akamai and Orca Security contribute to production hardening, but in different and incomplete ways. Akamai offers edge level API defense tied to its CDN footprint, but its inventory and monitoring remain shallow and traffic dependent. Orca strengthens cloud security posture but offers minimal API security value beyond documentation based checks. Neither solves end to end API visibility, shift left validation, or automated remediation, leaving large gaps across internal services, multi step logic flows, and sensitive business critical APIs.
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Read More: Top 10 Akamai Security Alternatives
Salt Security focuses on runtime driven API protection, emphasizing detection of misconfigurations, broken access controls, and sensitive data exposures using full traffic ingestion in production environments. It offers deep visibility into active API behavior but visibility depends entirely on high traffic volumes, leaving internal, partner, low traffic, and third party APIs undiscovered. Because discovery is edge based, east west traffic, shadow endpoints, and zombie APIs remain absent from inventory, limiting the breadth and reliability of its coverage.
Salt relies on post deployment detection rather than pre production prevention, meaning issues are uncovered only after APIs are already exposed. There is no native shift left engine, no automated test generation, and no ability to simulate stateful or role based attack flows. Testing depth remains narrow, based on single request payloads that miss complex OWASP API Top 10 issues such as BOLA, IDOR, or chained logic flaws. Remediation guidance is generic, with no developer mapping, payload reproduction, or automated ticketing, resulting in slow and manual fix cycles.
Compared to Salt, Orca Security offers only surface level API visibility derived from cloud misconfiguration detection. It discovers limited external endpoints and lacks runtime telemetry, internal API discovery, automated documentation, or sensitive data flow mapping. Since it cannot detect broken access controls, encryption gaps, or behavioral anomalies in API traffic, it cannot meaningfully support API security programs beyond high level risk snapshots. No testing engine is available, no remediation workflows exist, and most APIs remain fully unassessed.
Both platforms provide partial visibility, but in fundamentally different ways. Salt delivers runtime centric API detection for high traffic external surfaces, while Orca contributes misconfiguration insights around cloud posture without touching real API behavior. Neither solution offers end to end API security, nor pre production coverage, nor business logic testing depth, leaving significant blind spots across internal services, low traffic routes, and critical workflows.
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Read More: Top 10 Salt Security Alternatives
Traceable.ai provides runtime centric API protection focused on detecting attacks, anomalies, and sensitive data exposures in production. It ingests full payloads for SOC analysis and forensic investigation, enabling teams to understand attacker behavior post incident. However, visibility is dependent on live traffic, so low traffic, internal, partner, and third party APIs often remain undiscovered and unprotected. Testing is reactive and generated only for active endpoints, limiting shift left coverage, while deployment requires agents or mirroring, increasing friction and rollout time.
Orca Security delivers cloud wide posture management and misconfiguration detection, but its API visibility is limited to basic external mapping with no east west coverage or runtime context. It cannot detect shadow, zombie, or internal APIs, as discovery is derived from metadata, not live traffic or code plus behavior correlation. Sensitive data flows, undocumented endpoints, and role based authorization paths remain invisible. There is no native API security engine or API testing capability, and the platform relies on static analysis and posture checks rather than API specific logic, making it unsuitable for detecting broken access controls, business logic abuse, or multi step exploits. Coverage is confined to cloud misconfigurations, leaving API security largely unaddressed.
Both platforms strengthen infrastructure and application security but in fundamentally different ways: Traceable.ai focuses on runtime API protection and SOC visibility in production, while Orca Security emphasizes cloud posture and configuration hygiene without deep API awareness. Neither provides comprehensive shift left testing, automated remediation, or full multi environment API discovery, leaving critical gaps across internal, low traffic, and complex business logic APIs. For teams needing end to end API security across the SDLC, both fall short in different but significant ways.
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Read More: Top 10 Traceable Alternatives
Inviciti focuses on spec driven scanning and periodic policy based testing, aiming to validate API schemas, run basic injection checks, and enforce documentation aligned governance. Coverage is dependent on the quality of imported OpenAPI specs, gateway connectors, and network sampling thresholds, which means dynamic, low traffic, internal, partner, and third party APIs frequently remain untested or undiscovered. Since payloads are static and authentication flows are not automated, complex access control flaws, chained multi step vulnerabilities, and sensitive data exposures often slip through. Runtime visibility is limited, monitoring is absent, and remediation guidance is generic, creating long feedback cycles and high operational overhead.
Orca Security approaches API risk from a cloud posture perspective rather than an API first lens. It focuses on cloud misconfigurations, identity drift, and data exposure patterns across cloud assets but offers only production focused API visibility with limited detection depth. API discovery is shallow because visibility depends on cloud metadata, public surface mapping, and traffic accessible through cloud integrations, leaving internal, east west, shadow, zombie, and partner APIs outside its scope. No API testing engine, no pre production validation, and no business logic or access control simulation means teams gain only surface level misconfiguration awareness without actionable API level security coverage.
Both platforms help organizations understand parts of their API landscape, but neither delivers the comprehensive, behaviorally aware, end to end API security needed for modern distributed architectures. Inviciti depends heavily on static specs and periodic policy scans, leading to blind spots across dynamic and undocumented APIs, while Orca Security provides cloud context without API depth or testing. Together they still leave major gaps: no real runtime telemetry, no shift left API validation, no automated remediation, and limited ability to detect complex authorization, business logic, or multi step exploit paths across internal and mission critical services.
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Escape Security focuses primarily on static API schema inference and code driven analysis to highlight potential API risks before deployment. It generates approximate API definitions by parsing source repositories and identifying structural inconsistencies, missing validations, and potential injection points. However, because it does not observe real traffic, multi environment behavior, or sensitive data flows, its coverage is shallow. It misses dynamic, runtime registered, partner integrated, and third party APIs, especially those not explicitly declared in code, leaving nearly half the API surface undiscovered. Moreover, testing is static and limited to AST derived logic checks, offering no meaningful authentication simulation, chained flow execution, or role based abuse detection. This creates a significant gap between detected issues and real world exploitability, delaying remediation and increasing operational overhead.
Orca Security, in contrast, offers only surface level visibility into API endpoints through cloud posture insights and misconfiguration scans. It does not provide API documentation, runtime discovery, behavioral analysis, or shift left validation. Its API inventory is limited to external routes mirrored through cloud connectors and cannot detect internal, partner, third party, zombie, or shadow APIs. Sensitive data mapping is incomplete and dependent on user supplied context, offering no automated insight into data flows or schema behavior. The platform also lacks any native API security testing engine, no reproduction payloads, and no remediation workflows tied to service owners. API security context is minimal and largely decoupled from developer workflows, resulting in high noise and little actionable value for engineering teams.
Both platforms provide partial visibility into API risks but neither delivers full stack, continuously updated API security. Escape attempts to shift left but offers only static, code bound insights without the behavioral depth needed to catch complex authorization or logic vulnerabilities. Orca offers cloud centric misconfiguration detection with limited API awareness and no testing capabilities. Neither platform provides complete API discovery, runtime telemetry, or automated remediation, leaving significant blind spots across dynamic APIs, sensitive data flows, and modern microservices architectures.
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Akto focuses on pre production and early stage API security through scheduled scans, spec validation, and policy driven testing. It relies on user provided OpenAPI files, API hub imports, and network traffic logs, which means discovery is incomplete and critical internal, low traffic, and feature flagged APIs often never enter the inventory. Testing is surface level and constrained to generic payload libraries, so advanced authentication flows, multi step access control flaws, and business logic vulnerabilities frequently remain undetected. Without automated monitoring or runtime telemetry, Akto cannot identify misconfigurations or data exposure risks before they reach production, limiting its ability to prevent breaches proactively. Operational overhead increases as teams must upload specs, configure roles, triage findings, and manually interpret policy failures, slowing release cycles and expanding security debt.
Orca Security provides cloud wide visibility and posture management, but its API capabilities are limited to production only detection of misconfigurations and sensitive data exposures within cloud assets. API discovery is restricted to what the platform can infer from cloud workloads and documentation provided by the user, leaving internal, partner, third party, and low traffic APIs undiscovered. There is no ability to generate API documentation, no runtime sensitive data flow mapping, and no support for pre production validation or automated API security testing. Because all data is processed in Orca’s SaaS platform and no on premise agent exists, highly regulated teams face bureaucratic delays, privacy concerns, and compliance gaps. Costs also fluctuate with cloud footprint and AI processing volumes, increasing unpredictably for large engineering teams.
Both Akto and Orca strengthen certain parts of the API security stack, but in very different and limited ways. Akto focuses primarily on periodic pre production scans driven by static specifications, while Orca focuses on cloud posture and production side misconfiguration detection. Neither provides end to end API discovery, runtime aware documentation, sensitive data lineage, or dynamic, behavior driven testing that adapts to live traffic across environments. As a result, critical gaps remain across internal services, dynamic or low traffic endpoints, advanced logic paths, and pre production pipelines, creating a fragmented and reactive API security posture.
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Qualys delivers API security as an extension of its broader vulnerability management suite, relying on periodic scans, EASM crawls, and user provided specs to identify and test API surfaces. It detects standard OWASP level issues and supports unified reporting across assets, but lacks real time traffic visibility, multi environment discovery, and deep behavioral understanding of APIs. Coverage remains incomplete because over half of internal, partner, low traffic, and third party APIs never enter the inventory, and testing is limited to static payloads without awareness of business logic or role based access patterns. Remediation remains slow because findings are generic, disconnected from service owners, and lack payload reproduction or automated fix guidance, forcing teams into manual triage cycles.
Orca Security approaches API security through cloud and workload visibility, focusing on misconfigurations, posture risks, and production side detections. It provides surface level discovery for external APIs and flags data exposure paths, but does not capture east west traffic, cannot generate API documentation, and misses shadow, zombie, and partner APIs entirely. The platform outputs configuration insights rather than true API centric testing, leaving logic flaws, authentication weaknesses, and multi step exploits undetected. Because runtime telemetry is absent and monitoring depends on cloud configuration signals, teams receive delayed alerts and must rely on manual pentesting to validate issues, extending time to fix and widening exposure windows.
Both platforms contribute partial visibility into API risk, but neither delivers the depth needed for modern, distributed API ecosystems. Qualys offers broader asset context but remains dependent on static scans and incomplete inventories, while Orca adds cloud posture intelligence but lacks any meaningful API testing or documentation. Together they reveal fragments of the API attack surface, yet neither provides continuous runtime monitoring, automated documentation, environment wide discovery, or context aware remediation, leaving significant gaps across internal services, sensitive data flows, and complex business logic paths.
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Rapid7 focuses on traditional DAST style API security, offering point in time scans primarily used for compliance validation rather than continuous risk reduction. It relies on crawler based discovery and static scan profiles, which miss internal, low traffic, partner, and authenticated APIs, creating blind spots across critical services. Without runtime visibility, monitoring, or automated detection of misconfigurations and data exposure, Rapid7 leaves APIs unprotected between scans and forces teams into reactive remediation cycles. Deployment requires on-prem scan engines, manual authentication setups, and repeated configuration updates, slowing DevSecOps pipelines and increasing operational costs. Testing remains shallow and single request driven, with no simulation of chained exploits, business logic abuse, or role based access control flaws. Remediation is manual and report driven, increasing time to fix and delaying production quality improvements.
Orca Security provides limited API visibility through surface level misconfiguration checks tied to cloud assets, but does not offer a complete API security engine. Discovery is constrained to external endpoints and cloud metadata, leaving internal, third party, partner, low traffic, and shadow APIs undetected. With no automated monitoring, runtime telemetry, API documentation, or shift left testing, Orca operates as a cloud security overlay rather than an API centric platform. Deployment requires routing data to Orca SaaS, increasing privacy and approval overhead. The platform delivers alerts on cloud posture and misconfigurations but does not validate real API behavior, cannot detect business logic flaws, and lacks remediation workflows tailored to API vulnerabilities. As a result, breach detection delays increase and API risk remains unaddressed within DevOps and application teams.
Both platforms approach API security from outside the application layer and provide incomplete protection. Rapid7 emphasizes periodic scans for compliance but lacks continuous monitoring, runtime context, or deep attack simulation, leaving APIs exposed between releases. Orca Security brings cloud posture awareness but has no real API testing or runtime visibility, resulting in significant gaps across internal, partner, and low traffic APIs. Neither platform supports automated remediation, behavior aware testing, or lifecycle wide API governance, creating persistent blind spots from pre production to production in modern microservice and multi cloud environments.
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StackHawk focuses on pre production API scanning by analyzing code declared endpoints and running surface level security tests, but it lacks runtime visibility, multi environment discovery, and behavioral context. It relies heavily on static schemas and manually maintained catalogs, so dynamic, internal, partner, and low traffic APIs remain untested. The platform cannot simulate chained, stateful, or role based attacks, and its payloads are generic, leading to frequent false positives and limited business logic coverage. With no real monitoring or sensitive data visibility, critical vulnerabilities often slip into production undetected. While setup is lightweight, it introduces blind spots and forces teams to manually manage authentication, endpoint lists, and triage cycles.
Orca Security focuses on cloud security posture and misconfiguration detection rather than deep API security. It provides mapping of sensitive data to endpoints and limited production only detection, but has no shift left capabilities, no automated API discovery, and no ability to generate API documentation or detect business logic flaws. Since it lacks an on premise deployment option and relies entirely on SaaS based ingestion, cloud and AI processing costs grow unpredictably. With no testing engine, chained attack simulation, or remediation automation, the platform produces raw findings without workflow level context or developer mapping, causing remediation delays and increased breach exposure across internal and external API estates.
Both platforms provide narrow slices of API security but in fundamentally different ways. StackHawk emphasizes code level scanning for known endpoints while Orca provides production only visibility tied to cloud assets. Neither delivers end to end API security, multi environment discovery, or context aware testing. Both miss dynamic, shadow, and business critical APIs, lack deep attack simulation, and offer no automated remediation. The result is significant security gaps that persist across SDLC stages, leaving enterprises exposed to logic flaws, access control issues, and sensitive data risks in both staging and production environments.
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APIs now power the backbone of digital systems, but cloud posture tools like Orca Security leave wide gaps when it comes to API security. Coverage is limited to external misconfigurations, internal and low traffic APIs remain undiscovered, and there is no deep testing or runtime validation for modern microservices and distributed architectures.
Levo.ai solves these challenges by combining API discovery, shift left testing, runtime protection, sensitive data detection, and automated remediation in one unified platform. Teams remove manual overhead, eliminate false positives, and secure APIs continuously from development through production.
For organizations that need complete, enterprise grade API security rather than basic test automation, Levo delivers full spectrum coverage aligned with modern engineering velocity.
Choosing the right API security platform requires automation, context, and lifecycle level visibility. Unlike Akto, which relies on predefined templates and lacks advanced logic testing, Levo provides real time insights, exploit aware detection, and seamless CI CD integration for proactive defense.
Adopting Levo enables teams to accelerate releases, reduce operational burden, and secure every API endpoint without the constraints of template based or manually tuned tools. Achieve true end to end API protection with Levo and future proof your API ecosystem.
Achieve complete API security with Levo and future proof your APIs.
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