# Labelbox vs Qode

Labelbox records 762,803 monthly visits with -22.8% month-over-month growth and Contact for Pricing pricing. Qode records 8,522 monthly visits with -30.3% growth and Contact for Pricing pricing.

| Metric | [Labelbox](https://openaitools.app/tool/labelbox) | [Qode](https://openaitools.app/tool/qode) |
| :--- | :--- | :--- |
| Website URL | https://labelbox.com | https://qode.world |
| Categories | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools | AI Recruiting, AI Interview Assistant, AI Agent |
| Pricing Model | Contact for Pricing | Contact for Pricing |
| Monthly Visits | 762,803 | 8,522 |
| MoM Growth Rate | -22.8% | -30.3% |
| Primary Market | United States | United States |
| Direct Visits | 370,024 | 3,205 |
| Review Score | 4.4 | 4.1 |
| Saved Count | 0 | 1 |
| 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)

## Qode

Qode positions itself as a unified hiring platform for enterprise-scale recruiting. Rather than stitching together a sourcing tool, an ATS and a separate interview product, it brings the whole funnel into one environment: find candidates, reach out, post jobs, track applicants, and run interviews. The stated philosophy is centralisation by default with AI agents handling the high-volume, repetitive parts of that work.

The standout capability is AI-led interviewing. Candidates take CV-tailored technical and behavioural interviews conducted by AI agents in more than 30 languages, and recruiters receive structured reports covering skills, communication and cognition, together with video and transcripts. Integrity signals such as face, tab and behaviour checks are included so results can be reviewed with supporting evidence rather than a single score.

On the sourcing side, Qode provides an AI search builder with advanced filters, smart candidate ranking, email and phone discovery, and automated outreach sequences. Jobs can be distributed to 200+ hiring platforms, and applicants are matched and ranked automatically. Automated engagement over email and WhatsApp supports multi-step follow-up. The platform is sold on a self-serve subscription with a free trial and annual discounts, which makes it accessible to solo recruiters as well as larger teams.

**Pricing & Plans:** Qode offers a free trial with no credit card required that includes limited searches, LinkedIn, email and phone reveals, one active job post and two AI interviews. Paid self-serve plans start at $59 per month for Starter, aimed at solo recruiters with monthly quotas for reveals and AI interviews, and scale upward by usage. Annual billing saves 20%, and enterprise or custom arrangements are available by contacting sales.

- **Sourcing and outreach:** Search and rank candidates at scale with advanced filters, smart ranking aligned to your criteria, and built-in outreach sequences with email and phone discovery.
- **Jobs and applicant tracking:** Publish roles to 200+ hiring platforms, track applicants, and automatically match and rank candidates across every pipeline from one ATS.
- **AI-led interviews:** Run CV-tailored technical and behavioural interviews in 30+ languages, with structured reports covering skills, communication and cognition.
- **Integrity and assessment signals:** Face, tab and behaviour monitoring plus video and transcripts give hiring teams verifiable evidence behind each assessment.
- **Automated candidate engagement:** Automated multi-step engagement over email and WhatsApp keeps candidates moving through the funnel without manual chasing.

**Strengths:** Covers sourcing, ATS and interviews in one platform instead of separate tools; AI interviews in 30+ languages with structured, evidence-backed reports; Job distribution to 200+ hiring platforms with automatic candidate matching; Free trial with no credit card required and transparent self-serve plans

**Limitations:** Enterprise pricing and demo-based sales mean costs are not fully transparent; AI interviews may feel impersonal to some candidates; Broad feature set implies onboarding effort; Legal and compliance requirements around AI screening vary by jurisdiction

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

## Frequently Asked Questions

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

Labelbox has 762,803 monthly visits, while Qode has 8,522. 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%; Qode has -30.3%.

### 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. Qode: Contact for Pricing. Qode offers a free trial with no credit card required that includes limited searches, LinkedIn, email and phone reveals, one active job post and two AI interviews. Paid self-serve plans start at $59 per month for Starter, aimed at solo recruiters with monthly quotas for reveals and AI interviews, and scale upward by usage. Annual billing saves 20%, and enterprise or custom arrangements are available by contacting sales.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Workable](https://openaitools.app/tool/workable) | 10,780,627 | +5.5% | Free Trial, Paid | The agent recruits, you manage talent | [.md](https://openaitools.app/tool/workable.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 | [Manatal](https://openaitools.app/tool/manatal) | 459,004 | -1% | Free Trial, Paid | AI recruitment software that unites ATS, CRM and automated candidate screening | [.md](https://openaitools.app/tool/manatal.md) |
| 6 | [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) |
| 7 | [PyjamaHR](https://openaitools.app/tool/pyjamahr) | 325,592 | -15.3% | Free Trial, Paid, Contact for Pricing | AI-powered applicant tracking that takes hiring from source to hire in one workspace. | [.md](https://openaitools.app/tool/pyjamahr.md) |
| 8 | [TalentRecruit](https://openaitools.app/tool/talentrecruit) | 319,337 | -12.1% | Contact for Pricing | Autonomous hiring powered by agentic AI | [.md](https://openaitools.app/tool/talentrecruit.md) |
| 9 | [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) |
| 10 | [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) |

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

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