Customers evaluating deep-learning visual inspection often compare 3HLE’s Retina A.I. Vision & Robotics with the Cognex stack — VisionPro for rule-based and Cognex Deep Learning (formerly ViDi) for AI. Both can solve most defect-detection problems on the production floor. The right choice depends on licensing economics, integration footprint, and how the team will maintain the model over time.
What each platform optimises for
Cognex VisionPro + Cognex Deep Learning is the safe choice when an automation house already standardises on Cognex hardware. The In-Sight and DataMan cameras integrate natively; the toolset is mature for measurement, OCR, and pattern matching; and the deep-learning module adds anomaly detection without forcing a stack rewrite.
Retina A.I. is the better fit when (1) the camera hardware doesn’t need to be Cognex-branded, (2) per-camera licensing matters in the total cost of ownership, and (3) the customer wants the option to retrain models in-house without licence entanglements.
Hardware compatibility
Both platforms run on the same class of GPU industrial PC. 3HLE deploys Retina A.I. on the IVC-7 family (RTX Pro Blackwell GPUs, 24/7 industrial rating). Customers can also point Retina at their existing Cognex IPC — we maintain an Industrial PC for Cognex reference build for mixed deployments. Cameras are the same Sony Image Sensing Solutions GigE-Vision or USB3 Vision parts in either path.
Training-data workflow
Cognex Deep Learning uses a workflow tightly coupled to the VisionPro tool palette — fine for teams already trained on VisionPro, less obvious for teams starting fresh. Retina A.I.’s no-code training UI was designed for production engineers without a data-science background, which shortens the time from “we have a defect” to “we have a deployed model” on most projects.
Total cost of ownership
Cognex licensing is per-camera and re-billed when capability expands. Retina A.I. is a flat licence per IPC, with the model count un-metered. On a 10-camera line with two-shift retraining, the second-year TCO gap is typically 40-60%.
When Cognex is the right answer
- The customer already runs Cognex VisionPro and has trained operators on it
- The defect set is well-suited to rule-based tools and needs only marginal deep-learning augmentation
- Procurement requires a US-headquartered vendor for compliance reasons
When Retina A.I. is the right answer
- The customer is starting fresh and wants a no-code training interface
- Per-camera licensing is a TCO concern
- The team wants the option to retrain models in-house without re-engaging the vendor
- Swiss/EU support footprint is a procurement preference
3HLE delivers both paths. If you’re not sure which fits your line, a 30-minute scoping call with our engineering team will narrow it down faster than a feature checklist.