vms and video management system: Market overview and definitions
Video management has become the backbone of modern security and operations. In simple terms, a video management system ties cameras, storage, recorders, and software into a single workflow that captures, stores, and makes video searchable. A vms performs those functions by offering live view, recording, indexing, and playback. As a result, facilities use these tools to reduce response times and improve situational awareness.
Market figures show sharp growth. The video management market grew from US $12.44 billion in 2023 to US $15.14 billion in 2024, at about a 21.7% CAGR; that jump reflects rapid adoption across sectors US $12.44 bn → US $15.14 bn (Research and Markets). Forecasts expect the sector to hit roughly US $18.41 billion by 2025, maintaining about a 21.6% CAGR (Business Research Company). Longer-range projections put the market near US $28.28 billion by 2034 (Business Research Insights). These numbers demonstrate strong demand for smarter, more automated video systems.
Definitions matter. Video management software is the application layer that orchestrates recording, search, and device control. A video management system refers to the complete solution that includes cameras, recorders such as NVRs, storage, networks, and the software. The distinction clarifies procurement: you may buy vms software alone, or purchase a full surveillance system with cameras and recorders bundled.
Deployment choices split into on-premise and cloud-based options. An on-premise deployment keeps video data local, which helps with GDPR and government regulations for sensitive sites. In contrast, cloud-based deployments shift processing and storage offsite, easing remote access and rapid software updates. Many organizations pick hybrid models so they can keep sensitive video locally while using cloud services for aggregation and remote dashboard views.
Finally, open architecture and open-platform approaches let operators integrate third-party cameras and management systems such as Milestone Systems. This makes upgrades and customization easier. For more details about specific detection capabilities that work with your VMS, consider reading our use case on people detection in airports, which shows how video management ties into operational sensors.
video management software key features: Streamline surveillance and analytics
Modern video management software focuses on core functions that streamline operations. First, live view lets operators monitor dozens of feeds from a unified dashboard. Second, recording and retention policies run on local storage or NVRs for compliance. Third, fast search and playback reduce investigation times by letting users jump to motion events, tagged clips, or specific camera license periods. These key features help teams find critical video footage quickly and reduce added costs tied to manual review.
Analytics modules extend capability. For example, video analytics such as motion detection and license plate recognition enable automated tagging and triggers. Facial recognition and AI-powered search can locate persons of interest, while behavior models flag loitering or crowd density. These tools reduce false alarms and streamline incident response. Also, intelligent video combined with human review reduces escalation time and improves accuracy.
Integration is crucial. A VMS must integrate with access control and intrusion detection to provide a full security system view. Linking camera events to access control systems allows event correlation: an unlock event can prompt a camera to record, then trigger an immediate alert to guards. Many deployments use active directory for user permissions and single sign-on, which keeps security management user-friendly and auditable.
Vendors deliver different approaches. Some vms software packages emphasize an open architecture for third-party cameras and encoders, while others ship as a top video bundle with proprietary hardware. For sites that need bespoke object detection, Visionplatform.ai integrates with existing VMS deployments to turn CCTV into operational sensors. Our platform can detect vehicles, ANPR/LPR, PPE, and custom objects in real-time and publish events so operations can act on visual data. For concrete examples of ANPR and PPE use, see our pages on ANPR/LPR in airports and PPE detection in airports.
Vendors also manage licensing in different ways. Camera license counts, recorder limits, and concurrent user caps affect total cost of ownership. A feature-rich platform that permits open-platform integrations tends to lower long-term costs by supporting third-party cameras and avoiding vendor lock-in. In addition, software updates and regular maintenance keep a system cybersecure and ensure predictable response times during incidents.
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end-to-end video surveillance with video analytics: Integration and workflow
An end-to-end deployment connects the security camera at the edge to a management platform and the operator dashboard. First, cameras capture streams. Then, encoders or edge devices perform initial processing. Next, NVRs or local storage keep master copies. Finally, VMS indexes the streams and pushes alarms to security operations teams. This workflow creates a chain of custody for video footage and supports forensic search when post-incident analysis is needed.
Real-time video analytics add value across each step. Edge analytics can detect people, vehicles, or suspicious behavior before the stream reaches central servers. When an event meets threshold criteria, the vms issues an alert and a short clip is flagged on the dashboard. This lets guards verify events quickly, then dispatch responders or lock specific doors with integrated access control systems. The whole flow improves response times and reduces reliance on constant human monitoring.
Edge computing offers clear benefits. Processing at the camera or on a local GPU server cuts latency, preserves bandwidth, and lowers cloud costs. Research into IoT edge streaming video analytics shows that processing video closer to the source reduces network load and supports near real-time decision-making (MDPI). For many sites, this means critical detection happens on-site and only structured events or compressed clips are sent to central systems.
Security and compliance matter in the workflow. Encryption in transit and at rest protects video data from tampering. Many enterprises require local storage options so they can meet government regulations on retention and sovereignty. Visionplatform.ai supports on-premise deployments by default so customers retain data control and comply with EU AI Act expectations. That approach keeps models and training datasets private while still enabling operational use of events across OT and BI systems.
centralize security with management systems: Cloud-based solutions
Centralize multiple sites with a cloud-enabled management platform to get a single-pane view of dispersed assets. Cloud-based video management helps organizations roll out consistent policies, push software updates, and enable remote access without heavy on-site IT. For small and medium enterprises, cloud models reduce initial hardware investment and simplify deployment. At the same time, large enterprises gain scalability and redundancy across thousands of cameras.
Cloud-based video management supports scalability in several ways. First, it lets administrators add camera licenses and storage on demand. Second, it centralizes user permissions and dashboards across sites, so security management teams can monitor many locations from one console. Third, it integrates with other cloud services and analytics software for advanced reporting. These features make cloud-based solutions a cost-effective path to enterprise-grade monitoring.
There are trade-offs. Data sovereignty, encrypt, and compliance must be addressed when moving sensitive video offsite. Some organizations keep primary streams on-premise and use cloud-based services for aggregated monitoring, disaster recovery, or long-term archive. Hybrid models provide redundancy and help meet government regulations while still offering the benefits of remote access and vsaas-style management.
For teams that want plug-and-play deployment, cloud-native vendors emphasize simplicity and user-friendly interfaces. Other vendors emphasize open architecture and allow integration with legacy recorders and third-party cameras. If you need event-driven operational data, platforms like Visionplatform.ai can publish detections via MQTT so dashboards and BI systems can use camera output beyond physical security. That integration helps shift cameras from passive recorders to active sensors that power operations.
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Case study: avigilon and verkada in scalable video management
Avigilon and Verkada represent two distinct approaches to scalable video management. Avigilon focuses on AI-driven analytics and on-premise performance. Their platform adds self-learning video analytics that adapts to a site’s environment and reduces false detections. As one market report notes, “The exponential growth in the video management software market is a direct response to the increasing demand for high-quality video processing and the integration of VMS with other security and IoT systems” (Research and Markets).
By contrast, Verkada champions a cloud-native approach with tight hardware-software integration and rapid deployment. Its ease of deployment and remote access appeal to chains and distributed enterprises that need consistent policies and fast onboarding. A second expert observation notes that “Innovations in voice-controlled devices and smart home audio systems are driving the need for more sophisticated video management systems that can seamlessly communicate between hardware and software” (Grand View Research). This highlights how vendor strategies differ on openness versus simplicity.
Compare total cost of ownership and user experience. Avigilon’s AI-driven, on-premise model can reduce bandwidth and protect privacy, but it often requires more upfront hardware investment. Verkada’s cloud-native model reduces onsite infrastructure and streamlines the installation process, but it can incur camera license and subscription fees over time. When choosing a vendor, evaluate feature-rich analytics, local storage options, and whether the system supports third-party cameras. For multi-site airport deployments, for example, ANPR/LPR and people counting are common requirements; see our example on ANPR/LPR in airports for how detection integrates into operations.
Finally, consider interoperability. Milestone Systems remains a popular management platform for open integrations, especially where third-party cameras and recorders must work together. If you need customizable models or site-specific classes, platforms that let you train on your own video footage will cut false alarms and speed forensic search. Visionplatform.ai supports integration with Milestone and other management systems to add ANPR, PPE, and specialized detections while keeping data local and cybersecure.
Future trends in video management and analytics: Scalability and AI
Expect continued growth and innovation in the coming decade. Market forecasts project the video management sector to keep expanding toward roughly US $28.28 billion by 2034 from a smaller base, implying sustained investment in scalable systems (Business Research Insights). That projection reflects demand from smart cities, transport authorities, and enterprise security operations that need centralized monitoring and predictive tools.
AI-driven advances will power predictive security and reduce false positives. New models for people counting, license plate recognition, and behavior prediction will let operators act before incidents escalate. Edge-streaming video analytics and encoder-level processing will lower latency and bandwidth, enabling near real-time responses for traffic management and perimeter monitoring. Research supports edge processing as a critical trend for low-latency video analytics and bandwidth savings (MDPI).
Standards and interoperability will improve. Open architecture, open-platform APIs, and standardized event schemas make it easier to integrate access control systems, intercoms, and BMS feeds. That shift enables cameras to work as sensors in OT and BI systems for operations beyond physical security. Also, expect more focus on government regulations, encryption, and local storage choices to meet compliance requirements.
For organizations planning deployments, prioritize scalability and customization. Choose systems that integrate with third-party cameras, support NVRs, and allow model retraining on your own video data. Visionplatform.ai’s approach—on-prem and edge-first, with flexible model strategies—illustrates how to keep models local while making video searchable and actionable. As a result, teams can convert CCTV into operational sensors that improve both security operations and core business KPIs.
FAQ
What is the difference between video management and vms?
Video management often refers to the broader practice of operating cameras, storage, and workflows across a site or enterprise. A vms is the software component that performs live view, recording, indexing, and playback within that broader setup.
How fast is the video management market growing?
Growth has been robust; the market rose from about US $12.44 billion in 2023 to US $15.14 billion in 2024 at roughly a 21.7% CAGR (Research and Markets). Forecasts expect further increases to US $18.41 billion by 2025 (Business Research Company).
Should I choose on-premise or cloud-based video management?
It depends on compliance and operational needs. On-premise keeps sensitive video data local for GDPR and government regulations, while cloud-based systems offer easy remote access, centralize updates, and simplify deployment for multi-site organizations.
Can I integrate video analytics into my existing vms?
Yes. Many platforms and third-party tools integrate with popular management systems and Milestone Systems to add advanced video analytics. Visionplatform.ai, for example, connects to existing CCTV and streams structured events for operations and security.
How does edge computing help video monitoring?
Edge computing processes video near the camera, which lowers latency and reduces bandwidth use. As a result, detection and alerts can occur in near real-time and only event metadata or clips need to be transmitted to central systems.
What are the typical key features of video management software?
Typical key features include live view, recording, fast search and playback, user permissions, and integrations with access control. Additional features may include AI-powered search, LPR, and customizable dashboards.
How do Avigilon and Verkada differ?
Avigilon focuses on AI-driven, on-premise analytics and self-learning features, while Verkada emphasizes a cloud-native, integrated hardware-software experience with simplified deployment. Each approach has trade-offs in cost, control, and scalability.
Are there solutions for converting CCTV into operational sensors?
Yes. Platforms that publish detections as structured events allow cameras to feed dashboards, BI, and OT systems. Visionplatform.ai offers integrations that turn CCTV into a sensor network for use cases beyond physical security.
What should I consider about licensing and total cost?
Check camera license models, recorder and NVR limits, and subscription fees. Also account for installation process, feature-rich modules, and any added costs for cloud storage or advanced analytics software.
How can I ensure my video security deployment stays compliant?
Use encryption for transit and storage, retain control over sensitive video data, and follow local government regulations for retention and access. Consider on-premise or hybrid architectures to meet legal and regulatory requirements while keeping systems scalable.
