What’s New In Wowza Video Intelligence Framework (VIF)?

What’s New In Wowza Video Intelligence Framework (VIF) Version 1.1?

The newest version of Wowza Video Intelligence Framework (VIF)® can analyze recorded and on-demand video using the same models and configurations as it does to analyze live video feeds, through the REST API and Wowza Streaming Engine Manager. Version 1.1 also improves the REST API with named webhook secrets and job retention controls. Additionally, the newest version of VIF reorganizes the user interface into three tabs. Install VIF onto an existing non-containerized Wowza Streaming Engine instance, or via the standard Docker Compose deployment. VIF 1.1 runs on Wowza Streaming Engine 4.11.1 or newer versions.

Wowza’s Video Intelligence Framework (VIF) is the intelligence layer for Wowza Streaming Engine that turns live video into structured, actionable detection data on infrastructure the organization already controls. VIF version 1.1 extends the framework to support video on-demand (VOD) analysis. It also updates the REST API, introduces a redesigned user interface, and adds a non-containerized Wowza Streaming Engine deployment path.

Since launch, VIF was named one of three finalists for Streaming Media’s Streaming Innovation of the Year for the 2026 Readers’ Choice Awards. VIF, integrated with the NVIDIA Synthetic Video Detector (SVD), is also a finalist for the IAMT Impact Award in the Products, Services, and Monetization category.

Does VIF Analyze VOD Files?

VIF can analyze recorded files, as well as live streams. On-demand video files can be analyzed through both the REST API and the user interface. Until this release, VIF ran object detection, scene analysis, VLM analysis, and synthetic video detection against live streams only. Now, users can run the same models and saved configurations against video on-demand (VOD) files.

The same configurations that a team has for live streams can also apply to recorded VOD files. A tuned threshold and detector combination for a live camera feed can be reused against a VOD archive. Each job processes the entire file as fast as the analysis backend allows. Then, VIF produces a complete record containing timestamped detections that can be queried, along with a job manifest.

How Does VIF Handle VOD Analysis?

Every VOD job that stores results generates a corresponding JSONL file carrying the results, metadata, and timestamps for tools like Splunk and Elasticsearch. When a job fails, VIF resumes analysis from the last answered window, after confirming that neither the source file nor the requested configuration has drifted. Administrators can set both the maximum number of jobs VIF retains, 25 by default, and how long it keeps them.

Reviewing an archive previously meant restreaming it or standing up separate batch tooling. But with the updates to VIF, the same framework, with the same configurations and data channels, handles both live and VOD workflows.

How Can I Use VIF For VOD Analysis?

VOD analysis in VIF fits many workflows, especially where there are videos or archives that already exist. This includes use cases like post-incident review, archive analysis, content verification, and model evaluation.

A public safety or transportation team could pull a recording and run object detection or scene analysis across it to build a timestamped record of what took place. An organization that deploys VIF for live monitoring can apply the same detector configuration to archives containing historical footage. That makes an archive searchable and actionable, just like the live feed. A media team could run synthetic video detection against submitted or acquired footage before it enters the publishing pipeline. For teams comparing configurations, they could run multiple models or detectors against an identical VOD file to assess performance.

What Changed In The VIF User Interface?

There is a new dashboard for on-demand jobs that lists all running and completed jobs, with detection data, content descriptions, and synthetic video analysis in one place. The redesigned user interface organizes VIF into three tabs, each showing live counts.

1. Live Streams consolidates system health, thumbnail previews, and inline stream controls in one place, with detection overlays drawn directly on the frame.
2. On-Demand handles VOD job submission, management, and results specific to each type of analysis.
3. Configs provides a view of Stream Configs, Stream Config Defaults, and On-Demand Configs, with a Clone Config option for spinning up new Stream Configs without rebuilding from scratch.

With the new updates, an analyst or operator who is reviewing completed VOD analysis jobs now only needs to scrub the timeline to see the detections and confidence scores in context.

How Has The VIF REST API Been Updated?

The updated VIF REST API drives every capability the user interface exposes, including starting or queueing VOD analysis jobs, managing configurations, and retrieving results. Two recent additions are focused on operational security and control:

1. Named webhook secrets let an administrator add webhook endpoint credentials in a secrets document. VIF never exposes the values and never writes them into a job manifest, which keeps credentials out of artifacts that routinely get exported and shared.
2. Retention settings cap how many completed jobs VIF stores, and for how long. A high-volume deployment does not accumulate results indefinitely, and teams can set retention limits that keep them compliant while optimizing storage.

Wowza Streaming Engine and VIF are both highly extensible solutions that can be configured programmatically using the REST and Java API.

Does VIF Support More Advanced AI Models?

VIF added support for select vision-language models in a recent release, including NVIDIA Nemotron Nano, Google Gemma 3, NVIDIA Cosmos3, and Qwen3. Version 1.1 of VIF improves VLM analysis. VIF can confirm an external VLM server is reachable and report which model it serves to catch a misconfigured endpoint at setup instead of after a job fails.

That expansion reinforces and underscores VIF’s underlying approach to model flexibility. Teams can pair VIF with the models that fit their accuracy, latency, and licensing requirements, including custom-trained models for domain-specific detection work.

How Can I Use The Latest Version Of VIF?

Anyone using Wowza Streaming Engine 4.11.1 or later versions can use the latest version of VIF. While Docker Compose remains the standard deployment method, VIF can also be installed onto an existing non-containerized Wowza Streaming Engine instance. Existing VIF users can upgrade to the latest version at no extra cost, provided they have an active maintenance and support plan. This update makes Wowza’s Video Intelligence Framework a single, reliable analysis layer for both live and recorded video, built to be run at scale. Get in touch for a tailored demonstration, or see us at IBC 2026.

Frequently Asked Questions

What is new in Wowza VIF 1.1?

Wowza’s Video Intelligence Framework (VIF) version 1.1 adds four major capabilities: analysis of recorded video on-demand (VOD) files, an updated REST API, a redesigned Manager interface, and a non-containerized Wowza Streaming Engine deployment path. VIF 1.1 also expands support for vision-language models (VLMs) and adds verification for external endpoints.

Does VIF analyze VOD files?

Yes, VIF analyzes VOD files as well as live streams. On-demand analysis works through both the REST API and Wowza Streaming Engine Manager, supporting object detection, scene analysis, VLM analysis, and synthetic video detection workflows.

What Wowza Streaming Engine version does VIF require?

Wowza VIF requires Wowza Streaming Engine 4.11.1 or later. VIF can be deployed via a standard Docker Compose path or installed onto an existing non-containerized Wowza Streaming Engine instance.

What are the tabs in the VIF Manager interface?

The VIF Manager interface contains three tabs: Live Streams, On-Demand, and Configs. Each of these tabs contains live counts and information about analysis jobs. Live Streams shows system health, thumbnail previews with detection overlays, and inline stream controls. On-Demand shows job status and results for VOD projects. Configs provides a view of Stream Configs, Stream Config Defaults, and On-Demand Configs.

What is in the VIF REST API?

The updated VIF REST API lets a user submit an on-demand job, manage configurations, and retrieve results. It also supports named webhook secrets and retention controls.

Which vision-language models does Wowza VIF support?

VIF supports various vision-language models (VLMs), including NVIDIA Nemotron Nano 12B VL, Google Gemma 3 4B, NVIDIA Cosmos3 Edge, NVIDIA Cosmos3 Nano, and Qwen3-VL-4B. These are selectable in VIF, but selecting a model does not deploy it automatically. The matching VLM sidecar must be running as well.

How does VIF handle a failed on-demand analysis job?

For VOD, VIF resumes a failed on-demand job from the last answered window, only after confirming that neither the source file nor the requested configuration has drifted. VIF will retry a failed job up to three times in an effort to complete it satisfactorily. This process resets whenever a retry attempt makes progress past the previous run, so a long file with occasional interruptions is not stalled and can keep advancing.

Can VIF detect synthetic video in recorded files?

Yes, VIF can detect synthetic video content in recorded VOD files. VIF can run the NVIDIA Synthetic Video Detector and return a per-window confidence score against a configurable threshold across both live and on-demand workflows.

Did Wowza win a Streaming Media award in 2026?

Yes, Wowza Streaming Engine won the 2026 Streaming Media Readers’ Choice Award for Real-Time Streaming Solution. VIF was also named one of three finalists for Streaming Innovation of the Year.