# Labelbox vs Screenify

Labelbox records 762,803 monthly visits with -22.8% month-over-month growth and Contact for Pricing pricing. Screenify records 4,326 monthly visits with -17.1% growth and Free Trial, Paid, Contact for Pricing pricing.

| Metric | [Labelbox](https://openaitools.app/tool/labelbox) | [Screenify](https://openaitools.app/tool/screenify) |
| :--- | :--- | :--- |
| Website URL | https://labelbox.com | https://screenify.ai |
| Categories | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools | AI Interview Assistant, AI Recruiting, AI Chatbot |
| Pricing Model | Contact for Pricing | Free Trial, Paid, Contact for Pricing |
| Monthly Visits | 762,803 | 4,326 |
| MoM Growth Rate | -22.8% | -17.1% |
| Primary Market | United States | United States |
| Direct Visits | 370,024 | 1,694 |
| Review Score | 4.4 | 4 |
| Saved Count | 0 | 0 |
| 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)

## Screenify

Screenify positions itself as a fully automated screener rather than another skills-assessment tool. The company reports that customers see around a 33% reduction in time to hire, 10x more hiring capacity, and up to 90% lower hiring costs, figures it attributes to removing scheduling back-and-forth and manual first-round screening. Customer testimonials cited on the site mention over 80% alignment between Screenify's evaluations and internal team assessments, which is the kind of claim worth validating against your own roles during a trial.

The workflow is deliberately simple. A recruiter builds the interview, writes the questions and scoring criteria, and sends invitations to applicants. Candidates receive an email with a custom intro video and then complete the conversation on Google Meet or Zoom at any hour, which removes the usual scheduling friction and tends to improve completion rates. Screenify then returns scored summaries so the hiring team can shortlist quickly.

Integration is handled through applicant tracking systems as well as non-ATS tools such as Typeform and Airtable, and companies without an ATS can still share candidate details inside the platform. Plans scale by job role and interview volume, with a 7-day free trial on paid tiers and custom pricing for high-volume or private-infrastructure requirements.

**Pricing & Plans:** Screenify uses volume-based pricing with a 7-day free trial on paid plans: Starter at $149/month covers 1 job role and up to 100 interviews per month, Growth at $629/month covers up to 5 job roles, 500 interviews, up to 3 integrations, and custom branding, and larger requirements are quoted custom. Credits are only charged for interviews that candidates start and join.

- **Fully automated screening:** Create an interview, define scoring criteria, and send invitations automatically so screening, scheduling, and feedback collection happen without manual coordination.
- **Adaptive, personalised interviews:** Every interview is tailored to the role and the candidate's experience, and the AI follows up on responses the way a human interviewer would.
- **Consistent AI scoring:** Candidates are scored against your company's criteria using AI evaluation, which reduces interviewer bias and keeps comparisons fair across applicants.
- **Candidate-friendly experience:** Applicants get an email invite, a custom intro video, and the freedom to interview any time through Google Meet or Zoom, with your own branding throughout.
- **ATS and workflow integrations:** Connect Screenify to your ATS or to no-code tools like Typeform and Airtable so candidate data flows into the systems you already use.

**Strengths:** Fully automates first-round screening, scheduling, and feedback collection; Candidates can interview any time via Google Meet or Zoom, improving completion rates; AI scoring applies your criteria consistently and reduces interviewer bias; Works with ATS platforms as well as non-ATS tools like Typeform and Airtable

**Limitations:** Pricing starts at $149/month, which is steep for very small teams or occasional hiring; Interview credits are metered monthly, so high-volume hiring seasons can push you into an upgrade; Advanced features such as custom branding and multiple ATS integrations are gated behind the Growth tier; AI scoring is consistent but still benefits from a human review pass for senior or highly specialized roles

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

## Frequently Asked Questions

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

Labelbox has 762,803 monthly visits, while Screenify has 4,326. 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%; Screenify has -17.1%.

### 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. Screenify: Free Trial, Paid, Contact for Pricing. Screenify uses volume-based pricing with a 7-day free trial on paid plans: Starter at $149/month covers 1 job role and up to 100 interviews per month, Growth at $629/month covers up to 5 job roles, 500 interviews, up to 3 integrations, and custom branding, and larger requirements are quoted custom. Credits are only charged for interviews that candidates start and join.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [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) |
| 2 | [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) |
| 3 | [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) |
| 4 | [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) |
| 5 | [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) |
| 6 | [Metaview](https://openaitools.app/tool/metaview) | 259,340 | +54.3% | Freemium, Contact for Pricing | Agentic recruiting platform for sourcing, screening, and interviews | [.md](https://openaitools.app/tool/metaview.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 | [Alex AI](https://openaitools.app/tool/alex-ai) | 235,876 | +49.3% | Contact for Pricing | An AI recruiter that interviews, verifies, and screens every applicant | [.md](https://openaitools.app/tool/alex-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 | [FloCareer](https://openaitools.app/tool/flocareer) | 136,868 | -9.8% | Contact for Pricing | Interviews on demand, AI-driven or expert-led | [.md](https://openaitools.app/tool/flocareer.md) |

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

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