# ShareID vs Ultralytics ShareID records 3,618 monthly visits with +200.5% month-over-month growth and Contact for Pricing pricing. Ultralytics records 793,423 monthly visits with +8.7% growth and Freemium pricing. | Metric | [ShareID](https://openaitools.app/tool/shareid) | [Ultralytics](https://openaitools.app/tool/ultralytics) | | :--- | :--- | :--- | | Website URL | https://shareid.ai | https://www.ultralytics.com | | Categories | AI Face Recognition, AI Detector, AI Image Recognition | AI Developer Tools, AI Image Recognition, AI Models, No-Code&Low-Code | | Pricing Model | Contact for Pricing | Freemium | | Monthly Visits | 3,618 | 793,423 | | MoM Growth Rate | +200.5% | +8.7% | | Primary Market | United States | China | | Direct Visits | 1,440 | 239,747 | | Review Score | 4.1 | 4.6 | | Saved Count | 2 | 0 | | Supported Platforms | Website | Website | ## ShareID ShareID positions itself as an identity hub rather than a single verification widget. Its modules cover document authenticity checks performed by photo, video or NFC, active and passive liveness detection aligned with the low, substantial and high eIDAS 2.0 assurance levels, and the issuance and verification of EUDI Wallet attestations such as PID, EAA and QEAA. A fourth module, described as MFA 3.0, lets users authenticate to services with their EUDI Wallet instead of a password, reusing an identity verification already performed. What makes the platform distinctive is the assembly model. You pick the journeys you need, stack optional behaviours such as reusable versus single-use identity, assisted or unattended flows and intelligent fallback, then deploy through customisable SDKs for web and native apps. Everything runs behind a management console where teams review each request, override automated rejections when the model got it wrong, and track volumes, success rates and fraud typologies. Results are retained only for the authorised retention window. Compliance is treated as a first-class feature: the platform states that all eIDAS 2.0 assurance levels are supported by design, and that it helps with GDPR and related requirements. It is available as SaaS, on-premise or on SecNumCloud. ShareID reports 92% first-attempt enrollment success and 99.9% document authentication accuracy, and states that 0% of personal or biometric data is stored. The company also works with the French Gendarmerie Nationale on document fraud detection. **Pricing & Plans:** Not published; ShareID uses contact-for-pricing sales with a free trial available. Deployment can be SaaS, on-premise or on SecNumCloud, which typically implies volume- and scope-based quoting. - **Document identity verification:** Verify ID document authenticity by photo, video or NFC at low, substantial or high eIDAS 2.0 assurance levels. - **Active and passive liveness detection:** Detect spoofing attempts with liveness checks that adapt to your required assurance level and follow ETSI TS 119 461 alignment. - **EUDI Wallet attestations:** Enroll users with their PID attestations and issue or verify EAA and QEAA attestations in line with EUDI Wallet specifications. - **Passwordless strong authentication:** Let users sign in with their real, already-verified identity through EUDI Wallet authentication rather than a password. - **Management console and analytics:** Review every verification request, override automated decisions, and monitor volumes, success rates and fraud typologies in one place. **Strengths:** covers all eIDAS 2.0 assurance levels by design; States that no personal or biometric data is stored; Modular: pick only the verification blocks you need; Flexible deployment, including on-premise and SecNumCloud **Limitations:** No public pricing; you must contact sales to get a quote; Feature depth aimed at regulated enterprises, which may be heavy for small teams; EU-centric design assumes users hold an EUDI Wallet or EU-recognised documents [Profile (Markdown)](https://openaitools.app/tool/shareid.md) ## Ultralytics Ultralytics is best known as the maintainer of the YOLO model family, one of the most widely deployed families of real-time computer vision models. The same codebase spans seven vision tasks — detection, instance segmentation, pose estimation, oriented bounding boxes, classification, tracking, and more — so teams can move from a quick prototype to production inference without switching frameworks. Models export to a long list of runtimes including ONNX, TensorRT, CoreML, and other formats for edge and embedded targets. Beyond the models, Ultralytics offers a hosted Platform that handles the operational side of vision projects. You can label datasets with SAM-powered assisted annotation, fine-tune or train models on cloud GPUs (including AutoTrain), and deploy to managed regions that scale to zero when idle. An agent-style workflow builder lets teams chain models, logic, and alerting into pipelines, which suits industrial inspection, safety monitoring, and analytics products. Licensing is dual-track. The open-source path is released under AGPL-3.0, which is fine for open projects but imposes copyleft obligations on closed-source products. Companies shipping YOLO inside proprietary software typically purchase an enterprise license instead, which removes those obligations and adds on-premise or private-cloud deployment, broad export format support, and direct engineering support. Ultralytics also publishes extensive documentation and tutorials, which is a large part of why the ecosystem is so widely adopted by students, researchers, and commercial integrators alike. **Pricing & Plans:** Freemium. The YOLO models and tooling are available open source under AGPL-3.0 at no cost; the Ultralytics Platform offers free and paid cloud tiers, and commercial use inside closed-source products requires a paid enterprise license with custom support terms. - **YOLO real-time vision models:** One model family covering detection, instance segmentation, pose estimation, oriented boxes, classification, and tracking, tuned for real-time inference on GPU, CPU, and edge devices. - **Ultralytics Platform for the full lifecycle:** Label data with SAM-assisted annotation, train on cloud GPUs with AutoTrain, and deploy to managed regions that scale to zero when idle. - **Broad export and runtime support:** Ship trained models to ONNX, TensorRT, CoreML, TFLite, and many other formats for cloud servers, mobile devices, and embedded hardware. - **Dual licensing for open and commercial use:** Adopt the open-source AGPL-3.0 release, or purchase an enterprise license to embed YOLO in closed-source products without copyleft obligations. - **Workflow and agent builder:** Chain vision models, business logic, and Slack-style alerts into automated pipelines for monitoring, inspection, and analytics. **Strengths:** Mature, heavily documented model family with millions of daily inference usages and a large open-source community; Covers the whole lifecycle — annotation, training, deployment — so teams avoid stitching together separate vendors; Wide export format support makes it practical to target cloud, mobile, and embedded hardware from one codebase; Enterprise licensing removes AGPL obligations for closed-source commercial products **Limitations:** AGPL-3.0 on the open-source release is a blocker for many commercial closed-source deployments without a paid license; Achieving top accuracy still requires careful dataset curation and hyperparameter tuning rather than one-click results; Heavy dependence on the hosted Platform for managed features can create lock-in for teams that later want to self-host [Profile (Markdown)](https://openaitools.app/tool/ultralytics.md) ## Frequently Asked Questions ### How do ShareID and Ultralytics compare in monthly traffic? ShareID has 3,618 monthly visits, while Ultralytics has 793,423. Traffic estimates describe audience size, not product quality. ### How do their growth rates compare? ShareID has a month-over-month growth rate of +200.5%; Ultralytics has +8.7%. ### How does pricing compare? ShareID: Contact for Pricing. Not published; ShareID uses contact-for-pricing sales with a free trial available. Deployment can be SaaS, on-premise or on SecNumCloud, which typically implies volume- and scope-based quoting. Ultralytics: Freemium. Freemium. The YOLO models and tooling are available open source under AGPL-3.0 at no cost; the Ultralytics Platform offers free and paid cloud tiers, and commercial use inside closed-source products requires a paid enterprise license with custom support terms. ## Alternatives | # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | 1 | [Undetectable AI](https://openaitools.app/tool/undetectable-ai) | 3,113,403 | +17.5% | Freemium | Detect, humanize and verify AI content in one place | [.md](https://openaitools.app/tool/undetectable-ai.md) | | 2 | [Roboflow](https://openaitools.app/tool/roboflow) | 1,431,204 | +6.6% | Freemium, Contact for Pricing | Provides an all-in-one ecosystem for dataset annotation, model training, and edge deployment. | [.md](https://openaitools.app/tool/roboflow.md) | | 3 | [Labelbox](https://openaitools.app/tool/labelbox) | 762,803 | -22.8% | Contact for Pricing | Enterprise-grade labeling and data curation platform for complex computer vision training pipelines. | [.md](https://openaitools.app/tool/labelbox.md) | | 4 | [Encord](https://openaitools.app/tool/encord-com) | 244,204 | +9.4% | Contact for Pricing | Provides advanced multimodal dataset curation and annotation tools specifically for computer vision teams. | [.md](https://openaitools.app/tool/encord-com.md) | | 5 | [Frigate NVR](https://openaitools.app/tool/frigate-nvr) | 216,701 | -21.2% | Freemium | Open-source self-hosted NVR optimized for running real-time local object detection on security feeds. | [.md](https://openaitools.app/tool/frigate-nvr.md) | | 6 | [Face Search AI](https://openaitools.app/tool/face-search-ai) | 211,136 | +10% | Freemium, Paid | Reverse face search across the public web, with strong privacy guardrails | [.md](https://openaitools.app/tool/face-search-ai.md) | | 7 | [Label Studio](https://openaitools.app/tool/label-studio) | 137,199 | -16.1% | Contact for Pricing, Free | Open-source labeling tool with extensible support for custom computer vision annotation tasks. | [.md](https://openaitools.app/tool/label-studio.md) | | 8 | [Voxel51](https://openaitools.app/tool/voxel51-com) | 117,715 | -8.2% | Contact for Pricing | Specializes in dataset curation, visualization, and quality evaluation for computer vision workflows. | [.md](https://openaitools.app/tool/voxel51-com.md) | | 9 | [Shufti](https://openaitools.app/tool/shufti-pro) | 114,794 | -28.1% | Contact for Pricing | Global identity verification, KYC/AML compliance and fraud prevention | [.md](https://openaitools.app/tool/shufti-pro.md) | | 10 | [Viso Now](https://openaitools.app/tool/viso-now) | 111,150 | -3% | Freemium, Contact for Pricing | Focuses on prompt-based rapid assembly of end-to-end enterprise computer vision applications. | [.md](https://openaitools.app/tool/viso-now.md) | --- Source: https://openaitools.app/compare/shareid-vs-ultralytics More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)