Wowza has launched its Video Intelligence Framework, giving channel partners a way to build AI analytics and managed services around customers’ existing camera infrastructure without requiring a hardware replacement or public cloud deployment.
The on-premises and edge platform is designed for government, transportation, critical infrastructure, healthcare, and other environments where data sovereignty, security, latency, and predictable costs are key requirements.
Wowza adds AI analytics to existing video infrastructure
Rather than requiring organizations to replace existing camera deployments or stream footage to public cloud AI services, the framework enables partners to deploy AI models on customer-owned infrastructure, including air-gapped environments.
The launch reflects growing enterprise demand for AI deployments that prioritize data sovereignty, predictable infrastructure costs, and existing technology investments—particularly across government, defense, transportation, healthcare, and critical infrastructure.
“Our vision has always been that video will become the primary source of data for understanding the world,” Wowza CEO Krish Kumar said.
“Today Wowza sits at the center of more than 200,000 running instances. Most of that video is still just being captured and stored. VIF turns it into how organizations see, decide, and act—and it does it on hardware they already own, in networks they control.”
Local inference supports secure and air-gapped deployments
According to Wowza, more than one billion surveillance cameras are deployed globally, yet the overwhelming majority of enterprise video is stored without being analyzed.
Rather than replacing those installations, VIF is designed to add an intelligence layer that can run computer vision and visual language models against existing camera feeds.
“We’re basically giving all of these cameras brains,” Kumar told Channel Insider. “You don’t need to modernize anything. You can take an existing camera and get it as smart as ChatGPT.”
The framework supports deployments on-premises, at the edge, in private cloud environments, or fully air-gapped networks, with inference performed locally rather than through cloud-based AI services.
The company says this approach delivers sub-200 millisecond latency while eliminating ongoing per-minute inference costs and helping organizations meet data residency and compliance requirements.
Partners can build recurring video intelligence services
For solution providers, MSPs, MSSPs, and systems integrators, Wowza positions VIF as a way to monetize existing customer environments instead of leading another infrastructure replacement cycle.
The platform is camera-agnostic and model-agnostic, allowing partners to deploy Wowza’s bundled AI models, customer-developed models, or third-party models tailored to specific industries. Partners can also build recurring managed services around detection policies, workflow automation, and vertical-specific AI capabilities.
“We don’t have a services team,” Kumar said. “We want the integrators to harness the vertical expertise.”
The company highlighted use cases spanning defense, public safety, transportation, financial services, industrial operations, and healthcare, where organizations can integrate AI-generated alerts into SIEM platforms, dispatch systems, incident management tools, and existing operational workflows while keeping footage entirely within customer-controlled environments.
“I can think of a hundred use cases,” Kumar continued. “The opportunities are pretty crazy for us.”
Addressing AI token costs at scale
Wowza also sees growing demand among organizations seeking alternatives to cloud-based AI pricing models.
Referencing recent industry discussions around AI infrastructure costs, Kumar argued that continuously analyzing thousands of video feeds becomes prohibitively expensive under token- or usage-based pricing.
“Our technology enables you to run it offline,” he said. “You own your own data, you keep your own data, and there’s no token costs.”
The Video Intelligence Framework is generally available beginning July 22 and launches with RF-DETR object detection and CLIP-based scene analysis models. NVIDIA’s Synthetic Video Detector is also available through the framework for supported NVIDIA AI infrastructure.