As of September 2026, Latka tracks 3 network detection and response software companies with $1M–$5M in annual revenue. They have combined revenues of $5.5M and employ 48 people.
Every company below sells network detection and response software to other businesses and is ranked by its most recent annual revenue. Revenue, funding, headcount and customer figures come from CEO interviews on the Latka podcast, public company filings, and Latka estimates where a company has not disclosed a number.
Network Detection and Response (NDR) software is a category of cybersecurity tools designed to identify and respond to threats within a network. It leverages advanced machine learning algorithms and behavioral analysis to monitor network traffic, detect anomalies, and identify suspicious activities in real-time. NDR is essential for organizations looking to enhance their security posture against both known and unknown threats and is especially valuable in environments with complex and dynamic architectures. Typical features of NDR software include continuous network monitoring, incident response capabilities, automated threat detection, and forensic analysis tools. Buyers of NDR solutions are often IT security teams, network administrators, and compliance officers who seek to minimize the risk of data breaches and ensure regulatory compliance. Use cases range from monitoring user behavior, detecting lateral movement of threats, to securing Internet of Things (IoT) devices connected to the network.
Sorting: Highest -> Lowest
Showing 3 of 3 companies ranked by annual revenue.
Boston, Massachusetts, United States
Developer of software intended for Smart City, Public Safety and Critical Infrastructure networks. The company offers a scalable, cost-effective network visibility solution, enabling IT and security teams to optimize performance issues and detect threats on smart networks as they emerge and prevent attacks before they occur.
Pornichet, Pays de la Loire, France
At ServicePilot, we're revolutionizing IT monitoring by offering a comprehensive observability and security platform. Our mission is to integrate data across diverse IT components – applications, servers, containers, databases, networks, mainframes, third-party services and other resources – to achieve complete visibility into your technology stack. 🌟 Why Choose ServicePilot? - Unified Monitoring: Seamlessly monitor everything from servers to containers and networks, all in one place. - Enhanced Observability: Get deep insights into your systems, helping you understand not just what's happening, but why. - Security and NDR: Protect your networks and systems with advanced monitoring. - Proactive Issue Resolution: Our platform helps IT teams anticipate and solve performance issues before they affect your users. - Downtime Reduction: Reliability is key. We assist in maintaining the highest uptime, ensuring your services are always available. - Optimal User Experience: Understand and enhance how customers interact with your services, guaranteeing they always have the best experience. 🔍 Our Approach We believe in a holistic view of IT services. ServicePilot brings together critical operational data from every corner of your IT environment. This integrated approach helps in making informed decisions, faster issue resolution, and strategic planning based on comprehensive data analysis. 🤝 Join Our Community Connect with us to stay updated on the latest in observability and learn how ServicePilot is driving innovation in IT monitoring.
Latka tracks 3 network detection and response software companies with $1M–$5M in annual revenue. Together they generate $5.5M in annual revenue and employ 48 people.
Cynamics is the largest, with $3M in annual revenue, founded in 2018.
The average network detection and response software company in this list makes $1.8M a year, across 3 companies with reported revenue.
Ranked by annual revenue, the leaders are Cynamics, Synergy Information Systems and ServicePilot.
- Must provide continuous network monitoring and analysis capabilities. - Must utilize machine learning for threat detection and anomaly identification. - Should include incident response tools for managing identified threats. - Must be able to handle real-time data for immediate threat detection. - Not just limited to detecting known threats; must also identify unknown or evolving threats.
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