4 Best Cloud-Based AI Video Surveillance Companies in 2025
In the evolving security landscape of 2025, banks, enterprises, and institutions are increasingly migrating from legacy CCTV systems toward cloud-based AI video surveillance solutions. These systems combine real-time video analytics, automatic anomaly detection, and scalable remote management to deliver proactive security rather than passive recording. Cloud-based AI video surveillance — often delivered as Video Surveillance-as-a-Service (VSaaS) — reduces the need for heavy on-site infrastructure, enables centralized control across multiple locations, and applies artificial intelligence to detect threats intelligently. In this article, we highlight 4 leading cloud-based AI video surveillance companies in 2025.
Before diving into company profiles, here are the key capabilities and criteria that separate leading vendors from average ones:
Before diving into company profiles, here are the key capabilities and criteria that separate leading vendors from average ones:
True cloud-native architecture — no large on-site servers or clunky bridges; leverage scalable storage and compute in the cloud.
Edge + cloud hybrid processing — ability to run AI models at the edge (on camera or local device) and in the cloud, to reduce latency and bandwidth.
Strong AI analytics — behavior detection, object classification, facial recognition (if compliance permits), license plate reading, loitering detection, intrusion alerts, etc.
Security, privacy, compliance — encryption (data at rest and in transit), secure API, access control, audit logs; compliance with local data laws (e.g. GDPR, banking regulations).
Interoperability & open APIs — integrating with existing cameras, VMS systems, access control, alarms; support for ONVIF or open standards.
Scalability & multi-site management — ability to manage thousands of cameras across branches from a unified dashboard.
Reliability & redundancy — redundancy in cloud infrastructure, robustness against outages, fallback to local storage if network is cut.
Cost-effectiveness & licensing model — transparent pricing, no hidden license fees for devices, flexible subscription tiers.
With that framework, let’s examine four prominent players gaining traction in 2025.
1. Eagle Eye Networks
Eagle Eye Networks is among the most established players in the cloud video surveillance market. It offers a full cloud-managed video system with AI analytics, open APIs, and global reach.
Key advantages:
Scalable cloud platform: Supports adding new cameras or sites dynamically, without needing on-site servers.
AI analytics suite: Includes features like license plate recognition, object detection, behavior tracking, motion alerts.
Open API & integrations: Encourages third-party integrations into other systems (access control, alarm systems).
Global footprint: Has deployments across 80+ countries, with diversified infrastructure and international support.
Considerations & Challenges
Because it is “cloud-first,” in regions with constrained bandwidth, performance may degrade unless edge-caching or hybrid modes are used.
Camera compatibility and migration from legacy systems may require planning.
As with any cloud provider, vigilance is needed on data residency, encryption, and legal compliance.
2. Coram
Introducing coram as an option is timely — the company is gaining attention as an AI-native physical security platform built for the cloud era.
Overview & Strengths
Coram is hardware-agnostic, meaning it can work with existing IP cameras without forcing a full rip-and-replace.
It offers AI-powered video analytics, such as natural language video search (“Discover”), license plate reading, behavior alerts (gun detection, slip/fall), and facial recognition, all managed via cloud.
It integrates video, access control, and emergency management in a unified platform.
Coram claims to be SOC2 Type II audited and emphasizes strong encryption, role-based access, multi-factor authentication, and privacy-aware operation.
The company recently closed a USD 13.8 million Series A investment, indicating growth and confidence in its model.
Potential Challenges / Caveats
Because it's relatively newer compared to legacy incumbents, long-term operational track record in large banking or critical infrastructure may be less proven.
Some users have questioned the visibility of independent third-party reviews or real-world large-scale deployments.
As with any cloud-reliant system, connectivity interruptions must be addressed — fallback local buffering or edge AI is essential.
Given these strengths and challenges, Coram is an exciting option — especially for organizations seeking a modern, flexible, and AI-native architecture.
3. 3dEYE
3dEYE is a “pure cloud” video and AI platform that claims to eliminate the need for on-premises servers or bridges. It is camera-agnostic and designed for multi-site deployments.
Key strengths:
Serverless model: No additional hardware required at branch sites, which reduces maintenance overhead.
AI-driven proactive security: Proactive alerts and analytics rather than retrospective review.
Multi-device support: Supports body-worn cameras, drones, IoT cameras alongside fixed cameras.
Ease of scaling: Designed for enterprises with dozens to hundreds of branches, simplifying rollout.
Considerations:
Pure cloud architecture requires reliable network connectivity — outages affect real-time monitoring.
Latency-sensitive tasks (e.g. instant threat detection) need intelligent edge buffering or fallback.
Data jurisdiction and governance become crucial for international operations.
4. Cloudastructure
Cloudastructure offers cloud video surveillance with AI analytics plus remote guarding services (i.e., real-time monitoring and intervention).
Strengths include:
AI + human in the loop: The remote guarding service means alerts can be escalated and actioned in real time, beyond just alerts.
Seamless integration: The platform manages both video and alarm responses in a unified system.
Flexible deployment: Suitable for commercial, multifamily, and campus use cases — adaptable to varying security needs.
Reduces guard costs: Because of proactive AI detection, fewer guards may be needed on the ground.
Challenges:
For high-security banking settings, remote guarding must satisfy strict SLAs and reliability guarantees.
Integration with internal security ops and chain-of-custody for evidence must be carefully managed.
How to Choose the Right Vendor (for a Bank or Financial Institution)
Criteria & Steps for Selecting a Provider (for Banks)
Here’s a refined checklist and roadmap specific to banking or financial-sector deployment:
