Top 10 Traceable.ai Alternatives
APIs have evolved from mere technical enablers into critical business drivers. Studies indicate that 35–62% of enterprises generate direct revenue from APIs, and for nearly a quarter, APIs account for over 75% of total revenue. This transformation is powered by DevOps, microservices, and cloud-native architectures, turning software delivery into a strategic growth engine.
Yet this growth introduces a new set of risks. Unsecured, undocumented, or poorly monitored APIs can compromise innovation, trigger data breaches, incur regulatory fines, and disrupt production. As APIs increasingly handle sensitive data including personal identifiers, payment details, and healthcare records, regulators are demanding continuous security, privacy controls, and proactive monitoring. Simply relying on edge-based protections or post-incident alerting is no longer enough.
API security is now a board-level concern, focusing on both revenue protection and customer trust. Organizations must move fast while building responsibly, with deep visibility across all environments from development to production.
This is where Traceable.ai often falls short. Built primarily as a reactive runtime defense, Traceable.ai focuses on detecting and blocking attacks in production while storing full API traffic for forensic analysis. Its shift left capabilities are limited and addon in nature. With high operational overhead for SOC teams, incomplete discovery of low traffic, internal, or third party APIs, and heavy payload ingestion, critical vulnerabilities can still slip through, leading to delayed remediation, high costs, and privacy risks.
For enterprises that want to treat APIs as strategic assets rather than compliance checkboxes, a modern, automated, and privacy-conscious approach is essential.
This blog highlights the Top 10 Traceable.ai Alternatives, evaluated across coverage, scalability, deployment ease, cost efficiency, and alignment with API-first, DevSecOps-driven delivery models.
When evaluating Traceable.ai’s approach to API security, several recurring challenges emerge that directly impact security coverage, operational efficiency, compliance readiness, and developer productivity. These limitations explain why many organizations are exploring Traceable.ai alternatives.
The table below highlights the key triggers and explains why they matter, from limited pre-production coverage and reactive monitoring to high operational overhead and incomplete discovery, helping teams understand where Traceable.ai may fall short in real world deployments.
Traceable.ai delivers runtime API protection, detecting and blocking attacks in production while providing forensic analytics for SOC teams. However, its approach remains largely reactive, with limited pre-production visibility, high data ingestion costs, and significant operational overhead. For enterprises aiming to reduce privacy risk, accelerate remediation, and achieve full lifecycle API protection, several alternatives now offer stronger automation, scalability, and cost efficiency.
Below are the Top 10 Traceable.ai Alternatives that combine deeper pre-production coverage, privacy-preserving architectures, and CI/CD alignment, helping organizations secure APIs as strategic business assets without slowing down innovation.
Here’s a side-by-side comparison of Salt Security vs leading alternatives across core dimensions like business value, privacy, total cost of ownership, visibility, and deployment agility, helping you quickly identify the best fit for your API security strategy.
Levo.ai is built for modern, API-first enterprises that need full-lifecycle API security, not just post-incident defense. Unlike Traceable.ai, which focuses on detecting and blocking attacks in production, Levo secures the entire software development lifecycle, from pre-production testing to runtime protection, without slowing down teams or compromising privacy.
Powered by an eBPF-based sensor, Levo delivers deep, kernel-level visibility into every API, including internal, external, partner, and low traffic endpoints. It automatically generates API documentation, maps sensitive data flows, and detects vulnerabilities early in development. Traceable’s visibility depends on runtime traffic ingestion, leaving gaps across internal, inactive, and third-party APIs.
Levo transforms API security from reactive to proactive. Instead of relying on forensic dashboards and alerts like Traceable, Levo continuously validates APIs through exploit-aware, real data testing. Each alert is verified before being raised, cutting false positives and shrinking remediation cycles from months to days. Its inline protection blocks only confirmed threats, ensuring zero disruption to legitimate traffic or application performance.
Levo’s privacy-first design keeps all sensitive data within the customer environment, processing less than 1% of metadata in its SaaS control plane. This removes the vendor-induced data exposure that Traceable introduces by capturing full API payloads. The result is up to 10x lower infrastructure and egress costs, saving enterprises $100K–$500K annually while simplifying compliance.
Integrated directly into CI/CD pipelines, Levo automates shift left security with YAML and Python-based customization, rapid deployment, and hybrid or on-prem options. No inline agents, DPIAs, or long rollout cycles are required. Deployments complete in under an hour with minimal DevSecOps effort. In contrast, Traceable’s in-app instrumentation, network mirroring, and heavy data lake architecture slow down adoption and increase operational complexity.
Where Traceable stops at detection, Levo secures the entire journey, helping enterprises build, test, and operate APIs faster, safer, and with greater cost and privacy efficiency.
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.
Salt Security delivers runtime-first API protection, focusing on detecting attacks, access control misconfigurations, and sensitive data exposure in production. It provides full traffic ingestion, SOC analytics, and detailed API visibility, including sensitive data flows, but deployment is complex, ingestion incurs high compute costs, and vendor privacy risk is elevated. It is reactive, offering limited preproduction testing or shift left coverage, and remediation guidance is generic, requiring manual followup.
Traceable.ai provides runtime API protection with SOC-friendly dashboards and analytics, detecting attacks and anomalies in production. It captures full API payloads to support monitoring and runtime discovery, but low traffic, internal, and partner APIs are often missed. Testing is reactive, pattern-based, and lacks deep behavioral simulation, business-logic coverage, or automated remediation. Deployment requires inline agents or network mirroring, adding operational complexity and lengthy rollout times.
Both platforms strengthen API security in production, but in complementary ways: Salt excels in traffic driven detection and full spectrum runtime visibility, while Traceable.ai focuses on SOC-centric alerting and runtime attack prevention. Neither alone delivers comprehensive shift left security or automated, context-aware remediation, leaving gaps across pre-production, internal, and complex business-critical endpoints.
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Akamai Security delivers production focused API defense, emphasizing attack blocking, traffic inspection, and anomaly detection at the edge. It integrates with WAFs and CDNs for runtime protection and SOC-ready analytics, but coverage is mostly limited to external APIs. Internal, partner, or low-traffic endpoints often remain invisible, and preproduction testing is minimal. Deployment can be complex due to reliance on cloud SaaS and multi layer approvals. Costs scale with traffic, and full cloud processing raises privacy and compliance concerns. Remediation guidance is generic, lacking developer-specific mapping, automated patching, or deep behavioral context.
Traceable.ai provides runtime and active API monitoring with partial pre-production scanning. It uses traffic instrumentation to detect OWASP Top 10 flaws, misconfigurations, and anomalous access patterns, supporting SOC workflows. Runtime discovery depends on active traffic, leaving inactive, shadow, or internal APIs under-monitored. Security testing is reactive, attack simulations are shallow, and multistep business logic exploits remain largely untested. Deployment requires inline agents, adding operational friction. Remediation is report-based, without automation or developer guidance, increasing manual triage effort.
Both enhance API security in complementary ways: Akamai excels at edge-based runtime protection for external traffic, while Traceable.ai strengthens runtime visibility and SOC monitoring. Neither provides full shift left coverage, internal API discovery, or behaviorally aware testing, leaving gaps across preproduction, internal, and complex business-critical endpoints.
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Orca Security delivers cloud workload API visibility and security, emphasizing vulnerability detection, misconfigurations, and sensitive data exposure across cloud repositories. It leverages agentless scanning and automated schema inference to map APIs across environments, offering compliance insights and risk scoring. However, Orca lacks runtime protection, deep shift-left capabilities, or automated attack simulation. Deployment is SaaS-first, with some on-prem scanning limitations, and full-scale scanning introduces compute overhead and potential exposure of sensitive repository metadata.
Traceable.ai provides runtime API protection, detecting attacks, access-control misconfigurations, and anomalies in production. It captures full API traffic to feed SOC workflows and supports shadow API discovery, but testing is reactive, dependent on traffic, and offers limited pre-production validation. Multi-step attack simulation, business-logic flaws, and internal APIs often remain under-tested. Deployment is complex, with high overhead for in-line agents or network mirroring, and sensitive payload ingestion raises privacy and compliance concerns.
Both improve API security, but in complementary ways: Orca excels at pre-production API discovery, compliance, and static vulnerability mapping, while Traceable.ai focuses on runtime detection and reactive protection. Neither alone delivers end-to-end shift-left, behavior-aware API security, leaving gaps across staging, internal, and business-critical endpoints.
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Rapid7 delivers point-in-time DAST scans and compliance-focused API testing. It provides structured, periodic assessments of known endpoints, catching common OWASP Top 10 issues, but cannot emulate stateful, multistep attacks or role based logic flows. APIs remain unmonitored between scans, creating false confidence and leaving business-critical vulnerabilities exposed. Deployment is scan engine heavy, on-prem orchestration is complex, and remediation relies on manual triage of static reports. Coverage of internal, partner, and low-traffic APIs is limited, and runtime visibility is nonexistent, making the approach largely static and reactive.
Traceable.ai focuses on runtime API protection, emphasizing attack detection, anomaly monitoring, and SOC-driven forensic analytics. It captures full traffic to provide visibility into active endpoints and runtime behaviors, enabling teams to detect misconfigurations, fraud, and sensitive data exposures. However, runtime only monitoring means low traffic, shadow, or internal APIs may remain unseen. Its reactive posture, coupled with high compute and storage overhead, limited customization, and complex deployment, introduces operational friction and vendor privacy concerns. Preproduction testing, automated remediation, and shift left integration are minimal, leaving gaps in early stage API security.
Both improve API security, but in complementary ways: Traceable.ai excels at runtime detection and post-incident visibility, while Rapid7 offers structured, preproduction scans for compliance. Neither alone provides end to end shift left, behavior-aware API security, leaving blind spots across runtime, internal, and business-critical endpoints.
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Akto provides automated API security with broad endpoint discovery, pre-built test libraries, and CI/CD integration for pre-production scanning. It generates OpenAPI specs from live traffic, enabling teams to detect schema violations, injection flaws, and basic access control issues. However, tests are largely single request and static; complex multistep flows, business logic vulnerabilities, and ephemeral/internal APIs remain under-tested. Deployment requires traffic connectors or sidecars, and sensitive data handling lacks built-in privacy scrubbing, creating compliance overhead. Remediation is manual, relying on security engineers to triage findings and map them to developers, which slows patch cycles.
Traceable.ai focuses on reactive runtime protection, monitoring live API traffic to detect attacks, misconfigurations, and anomalous behavior. It offers SOC-ready dashboards and forensic analytics, providing runtime visibility into active APIs. Yet, runtime-only detection leaves pre production gaps, and low traffic or internal APIs may remain invisible. Deployment involves in-app instrumentation or traffic mirroring, introducing high friction and sensitive data exposure risks. Remediation guidance is limited to reports and dashboards, without automated ticketing or developer mapping.
Both enhance API security but in complementary ways: Akto emphasizes preproduction scanning and automated discovery, while Traceable.ai excels at runtime monitoring and attack detection. Neither alone delivers end to end, behavior-aware API security, leaving gaps in internal, ephemeral, and business-critical flows.
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Inviciti provides pre-production API scanning, generating security tests from OpenAPI imports and static policy engines. It enables compliance oriented coverage and basic injection checks but misses business logic flaws, low traffic/internal APIs, and runtime anomalies. Testing is manual heavy, static, and single request, leading to high false negatives. Deployment is on-prem capable but orchestration-heavy, and remediation requires manual triage.
Traceable.ai delivers runtime-first API protection, focusing on detecting attacks, access control misconfigurations, and sensitive data exposure in production. It offers good traffic based API visibility and SOC analytics, but discovery is limited to active or external endpoints, leaving internal, shadow, and low traffic APIs largely untested. Testing is reactive, lacks preproduction coverage, automated remediation, and shift left integration. Deployment is complex, and capturing full traffic introduces high compute costs and vendor privacy risk.
Both improve API security, but in complementary ways: Inviciti excels at preproduction scanning for compliance and static issues, while Traceable.ai focuses on runtime detection and SOC-driven response. Neither alone achieves end to end, behavior-aware API security, leaving gaps across business-critical, internal, and ephemeral endpoints.
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Qualys delivers API security primarily through pre production and compliance-focused vulnerability scanning, leveraging VMDR, EASM, and TotalAppSec modules. It covers standard OWASP Top 10 flaws and ensures regulatory compliance but lacks runtime awareness, behavioral analysis, or true shift-left capabilities. Deployment is heavy, requiring multiple modules and connectors, resulting in high operational overhead and unpredictable TCO. API discovery is limited to user provided specs, leaving dynamic, low traffic, or internal endpoints unseen. Remediation is manual, with generic recommendations that slow developer workflows, and real-time monitoring or anomaly detection are absent.
Traceable.ai provides runtime-first API protection, ingesting full API traffic to detect attacks, access control misconfigurations, and sensitive data exposure. It offers SOC-ready analytics and live API visibility, but coverage depends on active traffic, leaving low traffic, internal, or partner APIs partially blind. Testing is reactive, pattern driven, and single request in depth, missing complex business logic flaws and multistep exploits. Deployment requires inline agents or network mirroring, and full payload ingestion introduces privacy and compliance risks. Remediation is limited to dashboards and reports, with no automated code level fixes or pre production testing.
Both enhance API security in complementary ways: Qualys strengthens preproduction, compliance-focused scanning, while Traceable.ai excels at reactive runtime protection. Neither provides end to end shift left, behavior-aware security, leaving gaps across internal, shadow, and business-critical endpoints.
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StackHawk focuses on pre production API security, enabling automated DAST scans in CI/CD pipelines and containerized deployments. It generates tests from code and OpenAPI specs, identifying OWASP Top 10 vulnerabilities and basic business logic flaws. Custom payloads and authentication flows can be configured, but multistep exploits and complex runtime behaviors remain under tested. Deployment is lightweight, developer friendly, and integrates directly into pipelines, minimizing manual overhead. Remediation is provided via developer-focused reports, but runtime protection and post deploy monitoring are absent, leaving production APIs exposed until the next build.
Traceable.ai provides runtime-first API protection, emphasizing attack detection, fraud prevention, and sensitive data monitoring. It captures full traffic and enables SOC analytics, offering visibility into live APIs, including some shadow endpoints. Testing is reactive, based on observed runtime traffic, and lacks pre production scans, CI/CD integration, or shift left capabilities. Deployment is heavy, requiring inline agents or network mirroring, and full traffic ingestion introduces high compute costs and privacy risks. Remediation is manual, and complex multistep attack simulations are limited.
Both enhance API security in complementary ways: StackHawk strengthens pre-production testing and CI/CD shift-left initiatives, while Traceable.ai excels at runtime visibility and attack mitigation. Neither alone delivers complete coverage across runtime, internal, and business-critical endpoints, leaving gaps in end to end API security posture.
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Traceable.ai provides reactive runtime API defense, detecting attacks, access control misconfigurations, and sensitive data exposure. It captures full traffic for SOC analytics and forensic visibility, but only for active, external facing APIs. Shadow, internal, or low traffic APIs remain undiscovered. Deployment is complex, requiring inline agents or network mirroring, and full payload ingestion introduces high compute costs and privacy risk. Testing is reactive, with no preproduction validation or automated remediation, leaving shift left coverage incomplete. Traceable excels at runtime protection but cannot prevent vulnerabilities from reaching production.
Escape Security delivers preproduction API protection by generating schemas and security tests from connected code repositories. It provides compliance oriented coverage with automated test plans but lacks runtime observability and active monitoring of production APIs. Testing is largely static and code inferred, leaving feature flagged, ephemeral, or internal APIs untested. Deployment requires connecting repos and configuring AST parsers, creating moderate DevSecOps overhead. Remediation guidance is limited to raw findings, without auto-ticketing or live payload repro, slowing developer response. Escape excels at shift-left API security but cannot detect runtime anomalies, misconfigurations, or behavioral attacks, leaving critical endpoints exposed in production.
Both enhance API security in complementary ways: Escape Security strengthens pre production shift left testing, while Traceable.ai provides reactive runtime protection. Neither alone achieves full lifecycle or behavior-aware API security, leaving gaps across internal, ephemeral, and business-critical endpoints.
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APIs are critical to modern applications, but runtime-only platforms like Traceable.ai leave key gaps: limited pre-production testing, manual SOC overhead, and insufficient privacy controls.
Levo.ai fixes this by providing end to end API security, shift left testing, runtime protection, sensitive data safeguards, and automated remediation, all in a single platform. Teams ship faster, reduce operational burden, and stay compliant without sacrificing coverage.
For organizations that can’t afford partial solutions, Levo.ai delivers complete, proactive API security, turning what used to be a bottleneck into a driver of innovation.
Choosing the right API security platform means balancing speed, visibility, and compliance. Unlike Traceable.ai, which relies heavily on reactive monitoring and manual SOC management, Levo equips organizations with proactive, automated protection across the entire software lifecycle.
Adopting Levo enables teams to close security gaps, reduce complexity, and transform API security from a reactive bottleneck into an enabler of innovation. Achieve true end-to-end API protection with Levo and future-proof your APIs.
Achieve complete API security with Levo and future-proof your APIs.
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