Cloud annotation platforms charge per image, per credit, or per team seat. SentinelLabeler runs 100% on your hardware with zero artificial caps.
See why local AI-assisted annotation saves thousands of dollars over cloud subscription tiers.
| Capability | SentinelLabeler ON-DEVICE SUITE | Roboflow Annotate | CVAT.ai Cloud |
|---|---|---|---|
| Starting Cost | $15 / mo (flat) | $249 / mo (Starter) | $33 / mo (Solo) |
| Dataset Frame Limit | ✓ Unlimited | 10,000 images capped | 5,000 tasks capped |
| Footage & Weight Privacy | ✓ 100% Local GPU | ✕ Uploaded to Cloud | ✕ Uploaded to Cloud |
| Auto-Annotation Engine | ✓ SAM 2.1 & YOLO-World | Per-call credit fee | Serverless function queue |
| Video Temporal Propagation | ✓ 300-Frame Batch Flow | ✕ None (static only) | Basic linear tracking |
| Direct Capture Card Ingest | ✓ DirectShow / MF (240Hz) | ✕ None | ✕ None |
| Dual-Model Live A/B Compare | ✓ Built-in Dual HUD | ✕ None | ✕ None |
| Local CUDA Model Training | ✓ Integrated PyTorch Hub | Paid cloud credits ($) | ✕ Manual CLI required |
| Runtime Integration | ✓ 1-Click Sentinel Core | ✕ Generic export | ✕ Generic export |
You keep all of your datasets, labels, and trained models forever. Because SentinelLabeler stores everything in standard formats (YOLO text files, Pascal VOC XML, COCO JSON, PyTorch .pt, and ONNX) locally on your hard drive, you will never be locked out of your research or data. If you cancel, your license remains active until the end of your billing cycle.
Yes. Each license key includes device activation rights. If you build a new PC or switch between your capture workstation and your training rig, you can easily release and reactivate your license from your account dashboard without waiting for manual support.
Never. SentinelLabeler is fundamentally zero-cloud. All AI inference (SAM 2.1, YOLO-World, SAHI) and all model training runs directly on your local GPU via your isolated PyTorch environment. The only network calls made by SentinelLabeler are periodic license status checks to our verification endpoint.
For manual bounding-box annotation and video navigation, an ordinary modern multi-core CPU is sufficient. However, for AI auto-annotation (SAM 2.1) and Local Training Studio, an NVIDIA RTX GPU with at least 6GB of VRAM (RTX 2060 or higher recommended) is required for real-time acceleration via CUDA 12.x.
We stand behind SentinelLabeler with a clear, honest refund policy. If the software fails to operate on your hardware due to an unresolved technical incompatibility within 7 days of your initial purchase, our support squad will either assist you in configuring it or process a full refund. See our Refund Policy for complete details.
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