1. Why Edge AI for Visual Inspection Is a Different Category
Most articles ranking 'best edge AI platforms' are written for developers and data scientists. They compare hardware accelerators (NVIDIA Jetson, Intel OpenVINO, Google Edge TPU), training frameworks, and inference libraries. These are valuable tools — but they are not what an industrial operations team needs when evaluating real-time visual inspection.
Edge AI for visual inspection is a distinct category with distinct requirements. Think about what it actually means to run AI inspection on a manufacturing plant or energy substation:
Cameras are already installed — the platform must connect to existing RTSP/ONVIF streams, not require new hardware
Data cannot leave the facility network — regulated industries require complete on-premises processing
Detection must happen in under 10 milliseconds — at production line speeds, latency directly affects defect escape rate
Non-technical staff must manage the system — data scientists are not on the factory floor
Incidents must close — detection alone is insufficient; the system must trigger workflows and generate evidence
Generic edge AI infrastructure platforms address none of these requirements. This guide evaluates platforms on the criteria that matter for industrial visual inspection deployments.
2. The 8 Evaluation Criteria for Industrial Edge AI Visual Inspection
| Criterion | Why It Matters | What to Test |
|---|---|---|
| 1. Camera-agnostic connectivity | Replacing cameras is expensive. Must connect to any RTSP/ONVIF camera. | Connect 3 different camera brands from your existing fleet without hardware changes. |
| 2. Sub-10ms inference latency | At 60 products/minute, 100ms latency means defects missed between frames. | Run live inference on a production-speed test. Measure actual latency, not spec sheet. |
| 3. On-premises deployment | Chemical, pharma, energy facilities cannot route data through cloud services. | Confirm 100% local inference with no mandatory cloud dependency or outbound data. |
| 4. Pre-built industrial models | Training from scratch takes months. Pre-built models must work day one. | Run pre-built models on your cameras within 48 hours of connection. |
| 5. Closed-loop workflow | Detection without workflow closure is an alert system, not an assurance platform. | Trace an incident from alert through assignment, escalation, and closure with evidence. |
| 6. Self-learning models | Static models degrade. Edge AI must adapt to changing conditions automatically. | Ask for accuracy data at 3-month and 12-month mark from a live deployment. |
| 7. Multi-site governance | Enterprise buyers manage 5-50 sites. Central governance must be built-in. | Request a demo of central dashboard managing 3+ sites with role-based access. |
| 8. MES/ERP/PLC integration | Visual inspection must close loops with production systems, not operate as a silo. | Test API integration with your existing MES within the evaluation period. |
3. Infrastructure Edge AI Tools vs. Vision AI Platforms
Infrastructure Edge AI Tools (Not Visual Inspection Platforms)
These tools appear in most 'best edge AI platforms' rankings. They are powerful infrastructure components — but they require significant development effort to become a visual inspection solution. Appropriate for organisations with in-house AI engineering teams who want to build custom solutions.
| Platform | Category | Key Gap for Visual Inspection |
|---|---|---|
| NVIDIA Jetson (Orin) | Edge hardware + SDK | Requires full application development — no pre-built inspection models or workflow layer |
| Intel OpenVINO | Inference optimisation toolkit | Framework, not a platform — no camera management, workflow, or governance |
| Google Edge TPU / Coral | ML inference accelerator | Hardware accelerator only — no inspection application, models, or workflow |
| AWS Greengrass | Cloud-to-edge deployment | Cloud-dependent architecture — not suitable for fully air-gapped industrial facilities |
| Azure IoT Edge | Edge compute + cloud sync | Cloud-sync architecture — data sovereignty concerns for regulated environments |
These tools answer: 'How do I run ML models at the edge?' They do not answer: 'How do I implement 24/7 visual inspection with workflow closure, audit trails, and multi-site governance?' That requires a Vision AI platform.
Vision AI Platforms with Edge Deployment Capability
| Platform | Camera-Agnostic | Sub-10ms | On-Premises | Workflow Closure | Self-Learning | Multi-Site |
|---|---|---|---|---|---|---|
| AegisVision | Yes | Yes | Yes (full) | Yes (built-in) | Yes (continuous) | Yes |
| Jidoka | Partial | Yes | Partial | Partial | Partial | Partial |
| Landing AI | Yes | Yes | Yes | Custom build | Yes | Limited |
| Cognex ViDi | No | Yes | Yes | No native | Limited | No |
| Overview.ai | No | Yes | Yes | Limited | Yes | No |
| Spot.ai | Partial | Yes | Partial | Alert only | No | Limited |
4. The Latency Question: Why Sub-10ms Matters in Practice
Sub-10ms inference is often listed as a specification without context. Consider a food packaging line running at 80 products per minute — one product every 750 milliseconds. A camera at 30fps produces one frame every 33 milliseconds. An AI model that takes 50ms to process a frame will miss approximately 1 in every 1.5 frames at this line speed — creating systematic blind spots.
At sub-10ms inference, the model processes every frame with time to spare. Detection coverage is 100% — no frames skipped, no gaps. At 50-100ms inference (typical of cloud-based or poorly optimised edge models), coverage is 70-85% at high line speeds. The 15-30% coverage gap is where defects escape.
5. Deployment Architecture Options
On-Premises (Full Edge)
The most common architecture for regulated industries. All processing within the facility network. No data transits to external services. Inference on edge servers in the control room or server room at sub-10ms latency. Best for: chemical processing, pharmaceutical manufacturing, energy substations, defence facilities.
Hybrid Edge + Cloud
Optimal for multi-site enterprise deployments. Edge nodes handle real-time inference at the facility level; cloud handles analytics, cross-site dashboards, and model training. Best of both worlds: production-floor responsiveness with enterprise-scale analytics. Best for: multi-site manufacturing groups, retail chains, logistics networks.
6. Frequently Asked Questions
Can I use NVIDIA Jetson as the hardware for a Vision AI platform?
Yes — many Vision AI platforms (including AegisVision) can run their inference layer on NVIDIA Jetson hardware. The Jetson provides compute hardware; the Vision AI platform provides models, camera connectivity, workflow management, and governance above it. They are complementary, not competing.
How many cameras can a single edge node handle?
Typical industrial edge deployments handle 8-32 camera streams per edge node depending on inference model complexity and hardware specification. Enterprise deployments with multiple nodes can scale to hundreds of concurrent streams under central governance.
Is edge AI more expensive than cloud AI for visual inspection?
The upfront capital cost of edge infrastructure is higher. However, total cost of ownership over 3-5 years typically favours edge for industrial applications because: cloud inference costs scale with data volume (and camera streams generate enormous data volumes), latency requirements eliminate cloud for real-time detection, and data sovereignty requirements in many industries mandate on-premises processing regardless of cost.