Read Time: 7 minutes

What Is Vectra AI? 

Vectra AI is a cybersecurity company specializing in Network Detection and Response (NDR) solutions. Its primary focus is on detecting and responding to cyber threats using artificial intelligence and machine learning algorithms. Vectra AI’s platform analyzes network traffic, cloud environments, and identity processes to uncover malicious activity, even if attackers use stealthy or sophisticated techniques. 

The solution offers continuous monitoring and delivers prioritized alerts, assisting security teams in identifying and responding to threats faster. Vectra AI addresses both on-premises and cloud security, making it suitable for organizations with hybrid digital infrastructures. The platform can integrate with other security products and SIEM systems, providing broader coverage and enabling automated response actions. 

Vectra is used primarily by enterprises that need deep visibility into east-west network traffic and advanced threat analytics. Its focus on AI-driven detection makes it a strong candidate for organizations seeking proactive cyber defense capabilities.

Key Vectra AI Limitations 

While Vectra AI offers advanced capabilities in detecting and responding to cyber threats, there are several limitations that users may encounter. These challenges range from technical complexities to cost-related concerns, which can impact the overall user experience. These limitations are from the G2 platform:

  • Limited reporting capabilities: Currently, Vectra AI offers only one report with different timelines and lacks the ability to export data directly from the console.
  • Costly for multiple offices: The solution can become expensive, particularly for organizations with several small offices, as the cost of appliances adds up quickly.
  • Complex alert management: Like many security tools, Vectra AI can generate a large number of alerts. However, its quadrant-based approach helps highlight serious issues for faster investigation.
  • Steep learning curve: The platform requires a significant amount of knowledge to operate effectively, which can make onboarding difficult. Users need to become familiar with the terminology and data without adequate documentation.
  • Limited remote office sensor capacity: Remote office sensors are limited to a single gigabit upload, which necessitates deploying larger hardware if sites require higher uplink capacities (e.g., 10Gbps).
  • Challenging setup: Configuration and integration with an organization’s intranet are complex and require training. Many organizations need to hire external experts for proper setup and usage training.
  • Technical jargon: The product’s interface is filled with technical terms that may be difficult for non-technical users to understand, making it harder for business executives or less technical staff to navigate the platform.
  • Early release: As the product is still in its initial release, its features are evolving. While it shows promise, some aspects, such as threat reporting, could benefit from being more user-friendly for non-technical stakeholders.

Notable Vectra AI Competitors 

In light of the above limitations, many organizations are seeking alternatives to Vectra AI. Here are a few common options.

1. Faddom

Faddom is an agentless application dependency mapping platform that enhances network and workload visibility for security, operations, and IT teams. Unlike traditional NDR tools that rely on deep packet inspection or complex configurations, Faddom maps real-time communication flows across hybrid environments to uncover blind spots, shadow IT, and risky east-west traffic.

The platform is especially effective for detecting unauthorized internal connections, supporting segmentation strategies, and reducing the attack surface. Its continuously updated topology maps provide crucial context for incident response and help teams prioritize actions based on real infrastructure behavior.

Key features include:

  • Agentless deployment: Installs in under 60 minutes with no agents or credentials required, even in complex or hybrid environments.
  • Real-time dependency mapping: Visualizes how applications and systems interact to reveal lateral movement paths and undocumented connections.
  • Support for segmentation and risk reduction: Enables teams to plan microsegmentation and reduce exposure by mapping interdependencies accurately.
  • Intuitive visual interface: Easy to use across security and IT teams, with continuously updated topology views that support fast investigation and remediation.

Book a demo to discover how Faddom can enhance your visibility and support smarter threat detection across hybrid environments!

2. Darktrace

Darktrace is a cybersecurity platform that uses self-learning AI to detect, respond to, and prevent cyber threats. It offers a proactive approach to security by learning the normal behavior of an organization’s network, devices, and users. Using this contextual understanding, Darktrace can identify and respond to known and unknown threats across digital environments.

Key features include:

  • Self-learning AI: Learns the normal behavior of an organization to detect abnormal activity and emerging threats.
  • Real-time detection: Identifies and responds to cyber threats instantly, ensuring faster mitigation of risks.
  • Autonomous response: Takes action to neutralize threats and mitigate potential damage.
  • Cross-platform coverage: Protects various digital environments including network, cloud, endpoints, email, and identity systems.
  • Exposure management: Continuously analyzes attack surfaces to expose vulnerabilities before they are exploited.

Source: Darktrace 

3. CrowdStrike Falcon

CrowdStrike Falcon is a cybersecurity platform to stop breaches, reduce complexity, and lower overall costs by unifying security and IT functions into a single, cloud-native solution. It uses AI and machine learning to deliver protection across endpoints, cloud environments, and workloads. 

Key features include:

  • Cloud-native architecture: Designed to operate without relying on hardware-defined network perimeters.
  • Single platform, console, and agent: Provides a unified security solution with one lightweight agent, simplifying deployment and management.
  • AI-native platform: Uses AI and machine learning to detect and respond to cyber threats.
  • Extensible ecosystem: Allows for integration with additional security modules, third-party data sources, and partner applications.
  • Workflow automation: Built-in automation simplifies threat detection, investigation, and response processes.

Source: CrowdStrike 

4. Trellix

Trellix is an AI-powered cybersecurity designed to help Security Operations (SecOps) teams with integrated solutions that improve detection, response, and resilience across endpoints, email, networks, data, cloud, and security operations. The platform uses generative and predictive AI to drive threat detection and investigation.

Key features include:

  • AI-powered platform: Uses generative and predictive AI to improve detection accuracy, simplify investigations, and contextualize threats.
  • Integrated security across environments: Provides a unified solution for endpoint, email, network, data, and cloud security.
  • Resilient architecture: Designed to operate across on-premises, hybrid, and cloud infrastructure.
  • Detection and response: Delivers high efficacy in threat detection and response.
  • Automation and analytics: Incorporates AI-driven automation and analytics to optimize security operations, reducing the workload on SecOps teams.

Source: Trellix 

5. ExtraHop Networks

ExtraHop Networks offers a Network Detection and Response (NDR) solution to provide complete visibility into network activity, enabling security teams to detect, investigate, and respond to cyber threats. The platform uncovers hidden risks and offers insights into encrypted traffic and workloads across on-premises and cloud environments. 

Key features include:

  • Network visibility: Provides visibility across the attack surface, identifying risks that endpoint-based tools or SIEM systems might miss.
  • Cloud-scale machine learning: Uses machine learning to analyze behavior, detect threats, and automate investigations.
  • Threat detection: Powered by machine learning and rule-based detection, detects threats in real time.
  • Simplified investigation: AI-enhanced workflows allow for quick investigations.
  • Response: Offers integrated automated response options, enabling security teams to stop threats quickly or take manual action as needed.

Source: ExtraHop Networks

6. Zscaler

Zscaler is a cloud-native security platform built on a zero trust architecture that protects users, workloads, and devices by verifying access based on identity, context, and business policies. The Zscaler Zero Trust Exchange platform removes the need for traditional firewall and VPN-based network security by securing direct connections between users and applications.

Key features include:

  • Zero trust architecture: Eliminates traditional network perimeters by enforcing the principle of least-privileged access, ensuring that users and devices only access the resources they are authorized for.
  • Cloud-native security: Provides scalable security without the overhead of on-premise appliances.
  • TLS/SSL inspection: Enables visibility into encrypted traffic without degrading performance.
  • Identity and context-based access: Uses identity verification and contextual information to determine who can access what.
  • Risk mitigation across the attack chain: Protects organizations by minimizing the attack surface, preventing lateral movement, and stopping data loss.

Source: Zscaler 

7. Gatewatcher

Gatewatcher offers a Network Detection and Response (NDR) platform to provide network visibility, early threat detection, and rapid response. It enables security teams to anticipate and proactively prevent threats, ensuring continuous protection for enterprise infrastructures. 

Key features include:

  • Network visibility: Provides analysis of network traffic across all environments.
  • AI-powered threat detection: Leverages AI and machine learning to detect advanced threats, including zero-day attacks, by analyzing encrypted traffic and identifying early signs of malicious activity.
  • Generative AI assistant (GAIA): Uses generative AI to assist SOC teams in detecting, qualifying, analyzing, and responding to cybersecurity incidents.
  • Multi-engine detection: Combines synthetic analysis, heuristic detection, and machine learning to identify both known and unknown threats.
  • Proactive resilience: Automatically adapts to evolving network environments, reducing SOC fatigue by minimizing false alerts, anticipating attacks, and automating responses.

Source: Gatewatcher 

8. ReliaQuest

ReliaQuest offers a cybersecurity platform called GreyMatter that connects all security tools into a single, unified system. It uses automation to improve visibility, enable faster decision-making, and scale protection as the business grows. The platform helps security teams to prevent, detect, contain, investigate, and respond to threats quickly.

Key features include:

  • Unified security ecosystem: Integrates with all existing security tools, creating a centralized platform that improves visibility across the security stack.
  • AI and automation-driven security: Uses agentic AI to automate repetitive tasks, freeing up security teams to focus on higher-priority issues.
  • Exposure and asset management: Identifies and prioritizes exploitable vulnerabilities, helping organizations validate risks and protect critical assets before threats escalate.
  • Threat detection and response: Enhances threat detection capabilities, scales quickly, and enables faster, more accurate responses to emerging threats.
  • Faster security operations: Automates Tier 1 and Tier 2 tasks, simplifying response times and improving overall operational efficiency.

Source: ReliaQuest 

Conclusion 

Organizations evaluating alternatives to Vectra AI are often looking for solutions that balance advanced threat detection with ease of deployment, cost-effectiveness, and operational simplicity. Modern network detection and response platforms increasingly combine AI-driven analytics, cloud-native architectures, and automation to help security teams manage complex hybrid environments. Selecting the right solution depends on an organization’s requirements for scalability, integration, and the level of visibility needed across on-premises and cloud infrastructure.