# FaceSearch vs Labelbox

FaceSearch records 167,598 monthly visits with +66.6% month-over-month growth and Freemium pricing. Labelbox records 762,803 monthly visits with -22.8% growth and Contact for Pricing pricing.

| Metric | [FaceSearch](https://openaitools.app/tool/facesearch-ai) | [Labelbox](https://openaitools.app/tool/labelbox) |
| :--- | :--- | :--- |
| Website URL | https://facesearch.net | https://labelbox.com |
| Categories | AI Face Recognition, AI Search Engine, AI API | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools |
| Pricing Model | Freemium | Contact for Pricing |
| Monthly Visits | 167,598 | 762,803 |
| MoM Growth Rate | +66.6% | -22.8% |
| Primary Market | United States | United States |
| Direct Visits | 43,240 | 370,024 |
| Review Score | 3.7 | 4.4 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## FaceSearch

FaceSearch is a specialized reverse search product built around faces rather than scenery or objects. Instead of typing a query, you upload a single photo; the system validates that a usable face is present, then scans indexed public imagery from social networks, dating platforms and websites and returns candidate matches with source links and confidence scores. The service markets itself for practical everyday problems: checking whether a dating profile is real, spotting a catfish or romance scam, verifying a candidate or contractor, and finding out whether your own pictures are being reused without permission.

Privacy and legality get explicit treatment throughout the product. Uploads are encrypted in transit and, per the vendor, deleted within 24 hours, with no permanent storage of biometric data. Users must confirm they have the right to search a given image, and stalking or harassment is prohibited. Each search costs three credits, and credits are only consumed when a search completes, so a technical failure gets refunded, though a search that genuinely returns nothing still uses credits.

Pricing follows a credit model with a free entry point. A PRO subscription is listed at $29 per month including 60 credits and features such as search history and PDF export, while pay-as-you-go credit packs range from a small starter pack up to enterprise volumes at progressively lower per-credit rates. Payment options cover major cards, wallets such as Apple Pay and Google Pay, PayPal, regional methods including Alipay and WeChat Pay, and cryptocurrency. The product also offers dedicated search modes per social network and companion apps for iOS and Android.

**Pricing & Plans:** Freemium with a free starting tier, a PRO subscription listed at $29/month for 60 credits plus history and PDF export, and pay-as-you-go credit packs from about $6 for 3 credits up to $599 for 1,000 credits.

- **Reverse image face search:** Search across social profiles, dating sites and general web pages to locate where a given face has appeared publicly.
- **Smart face pre-validation:** Uploaded images are screened before a search runs, so low-quality or faceless photos don't burn your search credits.
- **Exportable PDF reports:** Turn a completed search into a professional report with matches, confidence scores and source links for documentation or legal use.
- **Search history for subscribers:** PRO users can revisit and re-download past search results, keeping a record of everything they have checked.
- **Mobile apps and social-specific searches:** Apps for iOS and Android plus dedicated modes for Instagram, TikTok, Facebook, X, Snapchat, YouTube and LinkedIn.

**Strengths:** Purpose-built for faces rather than generic image matching, with confidence scores and source links for each match; Clear privacy stance: encrypted uploads deleted within 24 hours and no permanent biometric storage; Flexible payment coverage including cards, wallets, PayPal, regional methods and cryptocurrency

**Limitations:** Searches that return no results still consume credits, so unsuccessful checks cost money; Credits are generally non-refundable once purchased, aside from technical failures; Face search raises ethical and legal sensitivities that vary by jurisdiction and must be respected

[Profile (Markdown)](https://openaitools.app/tool/facesearch-ai.md)

## 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)

## Frequently Asked Questions

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

FaceSearch has 167,598 monthly visits, while Labelbox has 762,803. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

FaceSearch has a month-over-month growth rate of +66.6%; Labelbox has -22.8%.

### How does pricing compare?

FaceSearch: Freemium. Freemium with a free starting tier, a PRO subscription listed at $29/month for 60 credits plus history and PDF export, and pay-as-you-go credit packs from about $6 for 3 credits up to $599 for 1,000 credits. 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.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Lenso.ai](https://openaitools.app/tool/lenso-ai) | 1,588,648 | -1.3% | Freemium | Offers broader reverse image and face matching capabilities across the web, but focuses more heavily on general visual search. | [.md](https://openaitools.app/tool/lenso-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 | [FaceSeek](https://openaitools.app/tool/faceseek-ai-face-search) | 350,166 | -11.9% | Freemium | Provides a similar direct reverse face lookup utility, though index coverage and result accuracy may vary. | [.md](https://openaitools.app/tool/faceseek-ai-face-search.md) |
| 7 | [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) |
| 8 | [SocialFinder.ai](https://openaitools.app/tool/socialfinder-ai) | 250,665 | +47.7% | Paid | Specializes more strictly in tracking down public social media profiles from photos, with a clean user interface. | [.md](https://openaitools.app/tool/socialfinder-ai.md) |
| 9 | [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) |
| 10 | [Face Search AI](https://openaitools.app/tool/face-search-ai) | 211,136 | +10% | Freemium, Paid | Scans public web pages for matching faces similarly, but includes additional built-in privacy guardrails and photo removal. | [.md](https://openaitools.app/tool/face-search-ai.md) |

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Source: https://openaitools.app/compare/facesearch-ai-vs-labelbox

More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)