# Labelbox vs ShareID

Labelbox records 762,803 monthly visits with -22.8% month-over-month growth and Contact for Pricing pricing. ShareID records 3,618 monthly visits with +200.5% growth and Contact for Pricing pricing.

| Metric | [Labelbox](https://openaitools.app/tool/labelbox) | [ShareID](https://openaitools.app/tool/shareid) |
| :--- | :--- | :--- |
| Website URL | https://labelbox.com | https://shareid.ai |
| Categories | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools | AI Face Recognition, AI Detector, AI Image Recognition |
| Pricing Model | Contact for Pricing | Contact for Pricing |
| Monthly Visits | 762,803 | 3,618 |
| MoM Growth Rate | -22.8% | +200.5% |
| Primary Market | United States | United States |
| Direct Visits | 370,024 | 1,440 |
| Review Score | 4.4 | 4.1 |
| Saved Count | 0 | 2 |
| Supported Platforms | Website | Website |

## Labelbox

Labelbox occupies an unusual position in the AI stack: it does not primarily sell a chatbot or a generator, it builds the environments and data that models learn from plus the agents that take over recurring engineering and research work. The homepage claim is direct — frontier AI labs train on Labelbox environments and expert data, while engineering and research teams hand their recurring backlog to agents running on Recursion. A recent Series E of more than $100 million signals a heavily funded bet on that thesis.,For enterprises, the newer half of the product is Recursion. Agents are triggered by real signals — a failing build, a PagerDuty alert, a new dependency release, a fresh model checkpoint, or a Monday morning schedule — and then work in the background until the job is done. The examples the company promotes are concrete deliverables rather than summaries: a tested pull request with a preview link, a postmortem draft in Confluence with every event sourced, an ablation table naming the changes that mattered, or a patch for each exposed service after a critical CVE. A grader agent scores each run, and the graded session leaves behind memories, notes and skills that the next session reads before it acts.,The original foundation remains the data business: labeling, annotation, curation and model evaluation supported by expert human reviewers, used for computer vision, NLP, supervised fine-tuning, RLHF and red teaming. In practice the two halves reinforce each other, since the environments that produce high-quality training data are the same environments where agents learn to do real work. Pricing is enterprise-style, routed through sales conversations and shaped by data volume and agent usage, with no public self-serve tier.

**Pricing & Plans:** Contact for pricing. Labelbox does not publish self-serve plans or public rate cards; its pricing route instead directs interested teams to talk to sales, and cost is shaped by data production scope, agent deployment and enterprise requirements. There is no published free tier. Prospective customers should request a quote based on their volumes and use cases.

- **Data labeling and expert annotation:** Structured annotation, curation, model evaluation and human-in-the-loop review across vision, language and generative tasks, used to build the training and eval data behind production models.
- **Autonomous agent sessions:** Recursion agents start on their own from builds, alerts, commits, checkpoints and schedules, then work in the background until the task is finished and reviewable.
- **Domain specialists by function:** Specialist agents cover software engineering, reliability, security, ML research, evals, R&D, product and finance workflows, from flaky test fixes to postmortem drafts and vulnerability patches.
- **Graded outcomes that compound:** Every agent run is scored by a grader, and graded sessions leave memories, notes and skills that the next session reads before acting, so quality compounds over time.
- **Native tool and data integrations:** Agents operate against the tools teams already use, including GitHub, Jira, Linear, Slack, Datadog, PagerDuty, BigQuery, Databricks, Vercel and cloud providers.

**Strengths:** Covers both sides of the problem: training and eval data production plus agents that act on recurring work; Backed by a recent $100M+ Series E and adoption by frontier AI labs, indicating real funding and demand; Agent runs produce concrete deliverables with proof — tests, reruns, ablations — rather than unverified suggestions; Integrates with the toolchain teams already run: GitHub, Jira, Linear, Slack, Datadog, PagerDuty, BigQuery and Databricks

**Limitations:** No self-serve pricing or open signup: everything routes through a sales conversation, which slows down evaluation; The platform assumes real engineering and research infrastructure (GitHub, cloud providers, data warehouses) to deliver value; Positioning has shifted quickly toward autonomous agent sessions, so teams wanting only labeling workflows may find it heavier than needed

[Profile (Markdown)](https://openaitools.app/tool/labelbox.md)

## 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)

## Frequently Asked Questions

### How do Labelbox and ShareID compare in monthly traffic?

Labelbox has 762,803 monthly visits, while ShareID has 3,618. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Labelbox has a month-over-month growth rate of -22.8%; ShareID has +200.5%.

### How does pricing compare?

Labelbox: Contact for Pricing. Contact for pricing. Labelbox does not publish self-serve plans or public rate cards; its pricing route instead directs interested teams to talk to sales, and cost is shaped by data production scope, agent deployment and enterprise requirements. There is no published free tier. Prospective customers should request a quote based on their volumes and use cases. 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.

## 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 | Streamlines end-to-end computer vision workflows from annotation to model deployment, but lacks Labelbox's general enterprise agent orchestration. | [.md](https://openaitools.app/tool/roboflow.md) |
| 3 | [Appen](https://openaitools.app/tool/appen-com) | 665,840 | +0.2% | Contact for Pricing | Relies heavily on managed human-in-the-loop workforce services for data validation, offering less self-serve technical tooling than Labelbox. | [.md](https://openaitools.app/tool/appen-com.md) |
| 4 | [Scale](https://openaitools.app/tool/scale) | 616,915 | +12.5% | Contact for Pricing | Delivers enterprise-grade labeling and model evaluation at massive scale, but often requires higher enterprise budgets than Labelbox. | [.md](https://openaitools.app/tool/scale.md) |
| 5 | [SuperAnnotate](https://openaitools.app/tool/super-annotate) | 386,034 | +37.4% | Free Trial, Contact for Pricing | Offers robust multimodal annotation and workflow automation, featuring slightly more intuitive data curation pipelines for computer vision teams. | [.md](https://openaitools.app/tool/super-annotate.md) |
| 6 | [Weights & Biases (CoreWeave Forge)](https://openaitools.app/tool/prompts) | 315,624 | -80.2% | Freemium, Contact for Pricing, Free | Track, evaluate, and ship AI models and agents in one connected environment. | [.md](https://openaitools.app/tool/prompts.md) |
| 7 | [Encord](https://openaitools.app/tool/encord-com) | 244,204 | +9.4% | Contact for Pricing | Specializes in advanced computer vision and physical AI data pipelines, though with a narrower focus than general text-and-image labeling. | [.md](https://openaitools.app/tool/encord-com.md) |
| 8 | [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) |
| 9 | [Label Studio](https://openaitools.app/tool/label-studio) | 137,199 | -16.1% | Contact for Pricing, Free | Provides a flexible open-source alternative for custom data labeling, but requires self-hosting and manual infrastructure maintenance. | [.md](https://openaitools.app/tool/label-studio.md) |
| 10 | [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) |

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Source: https://openaitools.app/compare/labelbox-vs-shareid

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