AI Streaming Anywhere: A Video API Approach to Deploying Intelligent Video Workflows (Update)
On our recent webinar, we demonstrated Wowza Streaming Engine’s ability to deliver AI-powered media solutions via the cloud, on-prem, and at the edge. Learn how to build intelligent media workflows on any device.
Table of contents
What Is “AI Anywhere” And Why Should You Care?
Video teams need AI-powered streaming features like live captions, object detection, redaction, moderation, and analytics. But, they don’t want to rebuild their pipelines to get this functionality. Flexible media infrastructure that supports cloud, on-prem, hybrid, and edge deployments brings this intelligent functionality within reach.
Treat AI as core functionality within your existing ingest-to-delivery workflow and place each step where it fits your constraints. Use cloud when you need elasticity and reach. Keep sensitive or deterministic work on-prem or in a private cloud. Push latency-critical steps closer to cameras at the edge.
This article summarizes the approach and the patterns that practitioners can adopt today. It draws on the concepts and live examples discussed in our recent session while keeping the focus on how to design and build. For more in-depth information, we highly encourage you to watch the webinar on-demand.
A Practical Framework for AI in Streaming
When it comes to these AI-powered capabilities, you want a simple, modular flow that you can plug into any environment. The most important decision is where each step runs. That decision should be driven by latency targets, data policies, cost per unit of work, and the skill sets you already have. Your intelligent media solutions should cover:
- Capture & Ingest
- Seamlessly incorporate feeds with ultra low-latency across all types of capture hardware (e.g. cameras, drones, CCTV feeds)
- Processing & Automation
- Modularized AI functions (e.g. captioning, transcoding, clipping
- Intelligent, centralized observability (e.g. detecting, logging, alerting)
- Packaging & Delivery
- Reliable Live & VOD streaming to any device
Example Deployment Blueprints, Strengths, & Tradeoffs
Events and venues will want a cloud-first approach.
Elastic GPUs handle spikes, CDN covers delivery, and analytics remain centralized. Typical AI steps in cloud include captioning, highlights and moderation.
For monitoring and compliance, keep it all on-prem.
Video and inference should stay inside private infrastructure for air-gapped or sensitive environments. Export metadata only when policies allow it.
OTT and VOD delivery requires a hybrid strategy.
Preprocess near the source to reduce egress and GPU minutes, then perform scalable captioning, search, and clip generation in the cloud.
When To Use A Cloud Reference Pattern
- Use The Cloud When
- Traffic is bursty
- Global audience
- Cloud Strengths
- Elastic GPUs and managed services
- Shortest path to scale
- Centralized analytics
- Cloud Costs and Trade-Offs
- GPU and egress costs can climb at peak
- Privacy and sovereignty require guardrails
- Typical Cloud Blueprint
- Ingest into cloud → run captioning or enrichment services → deliver over CDN → monitor centrally
When To Use A Virtual Private Cloud (VPC) Or On-Premises Reference Pattern
- Use A VPC or On-Prem When
- Data must not leave private networks
- Workflows are regulated
- Latency must be deterministic
- VPC and On-Prem Strengths
- Control
- Predictable performance
- Minimal egress fees
- VPC and On-Prem Costs and Trade-Offs
- Capacity planning
- Hardware lifecycle
- Feature velocity depends on what you bring in
- Typical VPC or On-Prem Blueprint
- Local ingest → local detection and redaction → transcription on private infrastructure → metadata-only export to external systems
When To Use An Edge Reference Pattern
- Use Edge When
- Round-trip latency and backhaul costs dominate
- Connectivity is intermittent
- Edge Strengths
- Near-camera inference
- Fewer bytes crossing the wire
- Resilience during network issues
- Edge Costs and Trade-Offs
- Device management at scale
- Model right-sizing for power and thermals
- Typical Edge Blueprint
- Camera to small-form device (e.g. NVIDIA Jetson Nano) running on ARM → enrich with captions or events → emit metadata → optionally restream to a central service
When To Use A Hybrid Reference Pattern
- Use A Hybrid Approach When
- You operate large catalogs or multi-region workloads
- Mixed constraints
- Hybrid Best Practices
- Keep privacy-sensitive and latency-critical steps close to the source
- Put elastic post-processing where scale is cheapest
- Typical Hybrid Blueprint
- Pre-tagging, QC, and initial redaction near the source to reduce egress and GPU minutes → captioning, semantic search, and clip generation scaled out to the cloud
Balancing “Bring Your Own Model” Flexibility with Out-Of-The-Box Functionality
Teams building these workflows have two options they need to evaluate:
| Out-of-the-Box Services and Partners | Bring Your Own Model (BYOM) Flexibility |
| When time-to-value and operational simplicity are priorities. Benefits include: – Managed uptime – Strong language coverage – Compliance features – Steady quality improvements Good for captioning, translation and standard detection tasks. | When you need domain-specific classes, custom thresholds, or proprietary logic. Benefits include: – Complete flexibility & control – Custom-defined workflows – Enhanced integrations Good for aligning datasets, packaging, and so models can fit your pipeline without code churn. |
Start with managed services to unblock value quickly. Introduce custom models where accuracy or privacy requires it. Keep the interfaces stable so swapping a service for a model is a configuration change rather than a refactor.
On the webinar, we shared demonstrations of Wowza Streaming Engine using AI for live captioning, object detection, and custom workflows via Model Context Protocol (MCP).
Live Captions and Translations
Ian showed a workflow for generating subtitles using Automatic Speech Recognition via Azure Speech or Whisper. This delivered WebVTT caption tracks to the player in real time. It also supported on-the-fly translation for multilingual audiences. This fits existing delivery flows and is easy to validate against latency and accuracy targets.
- Wowza Streaming Engine Caption Handlers Plugin on GitHub: wse-plugin-caption-handlers (translate branch)
- Generating & Delivering Captions with Wowza Streaming Engine article: https://www.wowza.com/blog/captions-with-wowza-streaming-engine
Object Detection Edge on Small-Form Devices
Running inference at the edge reduces round-trip time and network load. Ian showed an NVIDIA Jetson device running a YOLO-based intruder detection demo, with annotated frames and ID3 metadata events like “person_detected.” The player reacted to those events, rendered overlays, and updated charts accordingly. This works well for field and venue streaming, branch sites, and mobile command posts where bandwidth is at a premium.
Orchestrating Media Operations with MCP
Model Context Protocol (MCP) makes Wowza Streaming Engine actions available as discoverable tools that agents and IDE extensions can call. This transforms your video platform into a programmable surface. List apps, inspect incoming streams, start or stop recordings, and scaffold publishers using natural language prompts. This standardizes orchestration while keeping humans in the loop.
- Wowza Streaming Engine MCP on GitHub: wowza-streaming-engine-mcp
Live subtitles and MCP orchestration are available now through the repositories linked in this post. Object detection and BYOM are tracking for near-term release. To prepare, finalize data schemas, define model I/O contracts, and plan capacity so adoption is a configuration change rather than a rebuild.
See more demonstrations, workflows, and best practices in the on-demand session.
Frequently Asked Questions
Can AI run inside a virtual private cloud (VPC) without using public internet?
Yes. Keep ingestion and inference inside VPC or on-prem. Export only events and metadata if policy allows.
What is the easiest place to start?
Live subtitles. The repository shows how to route audio through ASR and emit WebVTT to the player. Measure end-to-end latency and tune buffering for sentence boundaries.
Do we need new hardware to try edge inference?
Not always. Reuse existing GPUs where possible. For remote and branch sites, compact devices such as Jetson or ARM-based devices work well. Make sure to size models for power and thermals.
How do I decide between cloud, VPC, or edge?
Figure out what your priorities, constraints, and limitations are. Latency-critical and privacy-sensitive steps stay close to the source. Elastic and compute-heavy post-processing belongs where scale is cheapest and easiest to operate.
How should teams plan for BYOM?
Define your inputs, outputs, and error handling to set AI up for success at the inference step. Package models consistently for easy integration. Then plug them into the same orchestration you already use for captions or other AI services.