Discover why hybrid cloud-edge deployments are the 2026 standard for security integrators. Balance real-time edge AI with cloud scalability to lower TCO and e-waste.
In 2026, the question is no longer whether the best place for AI analytics is at the edge
or in the cloud. Real-life deployments have delivered the answer—a resounding “both.”
Hybrid infrastructures rely on AI cameras, smart access control terminals and other IoT “edge” devices that share the AI processing workload with a VMS that runs in the cloud of a VSaaS provider. Hybrid infrastructures of this kind seek to distribute the workload of analytics and storage in the best-possible way, enabling systems to benefit from the advantages of both edge AI and cloud AI. Both “ends” of the system take on the tasks that their best equipped for.
Additional on-prem devices, such as NVRs and DVRs, are no longer needed, or at least their deployment is optional, making hybrid infrastructures lean, responsive and easy to maintain.
Table of contents
Pros and cons of edge and cloud make hybrid the option of choice
Hybrid infrastructures in video security
Hybrid infrastructures in access control
Regulatory regimens and regional perspectives
Different brands, different philosophies
There are several factors to consider in the balance of power in hybrid architectures. Integrators and (prospective) users should first get clear about the benefits of edge AI and cloud AI.
Benefits at the edge
- Latency: There is virtually zero lag between the image sensor generating raw data and initial AI analytics and storage.
- Bandwidth & cost: When recording in 4K at 10Mbps, a single security camera may generate 4GB of raw footage per hour. Edge AI can decide whether something important is happening in the frame and reduce the amount of high-res data drastically that is being transmitted.
- System reliability: Unstable Internet does not affect the quality of AI analytics and storage.
- Compliance and privacy: Local jurisdictions typically require two weeks to one month of local data storage. Moreover, users are often suspicious of their data being streamed to the cloud indiscriminately.
- Energy efficiency: Modern SoCs are highly energy efficient. Edge AI hardly affects the energy bill of the user, while it helps mitigate the spiraling energy needs of cloud data centers in the time of global warming.
Benefits in the cloud
- Raw compute power: Cloud data centers can perform the most advanced analytics and process terabytes of data in no time, at all times.
- Maintenance and cybersecurity: System maintenance is no longer an issue for users and administrators as VSaaS providers continuously update their systems, including in the ever-more important field of cybersecurity.
- Disaster recovery: While a “robust edge” gives systems resilience in day-to-day operations, cloud backups of important data mitigate the risk of hardware failure at the edge.
- Scalability: Additional devices can simply be plugged in. DVR and NVR updates to accommodate more video streams are no longer needed.
- TCO: Cloud services are usually based on subscription models. Organizations seek lower total cost of ownership when switching to hybrid deployments, thanks to lower on-prem maintenance costs and a smaller hardware footprint without servers, NVRs and DVRs that need to be replaced every few years.
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Video security infrastructures traditionally relied on rooms full of churning NVRs, DVRs and servers, next to a video control room staffed with personnel keeping an eye on grainy live streams and combing recorded footage for “the scene when the crime happened.” On-prem maintenance required additional personnel or a service provider visiting the site regularly.
Today, recorders and servers may have fully disappeared. Control room staff, relieved from the burden of painstaking manual forensic investigations, oversee systems that generate intelligence in real time, with crowd maps, automated incident responses and IoT integration. Software updates “happen” automatically, and even the health of edge devices and components such as microSD cards for storage are monitored in the cloud.
In reality, however, many hybrid deployments include at least some legacy hardware, kept in place as older cameras, for example, in many cases analogue, are still doing their job reliably enough. This increases the challenge for integrators, as users seek to benefit from advanced AI without a full edge device overhaul. Solutions that add cloud gateways or AI boxes, or pair old analogue and new edge AI cameras might be needed.
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While the “natural habitat” of hybrid infrastructures is video surveillance, the other major field of physical security—access control—is also increasingly adopting hybrid models.
The reasons are similar: Data demand is increasing, as access control systems often handle thousands of user profiles, including biomentrics and integration with IT systems such as credential and time management. In the cloud, updates to databases can be made immediately, across whole infrastructures, as can cybersecurity updates.
To address privacy concerns, sensitive data may be handled at the edge only, which also helps minimize the volume of data streams between access points and the cloud. Integrated hybrid infrastructures encompass video security and access control, unlocking deeper intelligence for the user, as the ACaaS component of a unified system logs the comings and goings of users, while the VSaaS supervises each entry point.
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Sensitive data generated by video security and access control systems are increasingly subject to national and supranational regulations—especially in the US and Europe, but also in smaller markets such as India or Vietnam.
Privacy concerns play a role, as does cybersecurity and the fact that states seek to ensure that data generated locally remains within their jurisdiction. Concretely that means:
- Certain data needs to be stored for a period of time at the edge
- Transmission between the edge and cloud needs to be cybersecure
- Cloud data needs to remain within the same jurisdiction
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Virtually all major security brands offer solutions tailored to hybrid needs, while harnessing their traditional strengths. This may explain some differences in their approaches
That could mean:
- Developing edge AI-ready SoCs optimized for edge/cloud solutions (Axis Communications and Hanwha Vision, for example)
- Unifying video security and access control in one cloud solution (Genetec, but also other providers focusing on VMS instead of hardware)
- Offering “cloud-first” solutions that fully harness the compute power of the cloud. Such offerings, by Verkada or Eagle Eye Networks (soon to be integrated in Brivo) for example, seek to leave the logic of a local server-based VMS fully behind, while other companies, such as VIVOTEK, seek to offer longtime customers a cloud suite based on the workflow of the on-prem VMS they have been working with for years.
One key differentiator between brands is whether their systems are open, allowing users to integrate third-party hard and software, or closed (or proprietary), allowing significantly less, closely guarded third-party integration.
The logic behind the former is clear: Users should have the choice and be able to keep legacy infrastructure. The latter, meanwhile, promise a perfectly orchestrated interplay of all components in the system, without friction between components from different manufacturers.
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Conclusion
Hybrid edge/cloud architectures offer the best of both worlds—unlocking new potentials, increasing operational efficiency and reducing hardware footprints. Hybrid is a trend that’s here to stay.
As more organizations enter the next hardware refresh cycle, demand for hybrid solutions is only expected to grow, as are their capabilities as AI technology advances. For security integrators, mastering hybrid cloud-edge deployments is the key to delivering future-proof security systems that are resilient and smart.