# Labelbox vs TeamFeedback

Labelbox records 762,803 monthly visits with -22.8% month-over-month growth and Contact for Pricing pricing. TeamFeedback records 979 monthly visits with -50% growth and Freemium pricing.

| Metric | [Labelbox](https://openaitools.app/tool/labelbox) | [TeamFeedback](https://openaitools.app/tool/teamfeedback) |
| :--- | :--- | :--- |
| Website URL | https://labelbox.com | https://teamfeedback.co |
| Categories | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools | AI Report Generator, AI Summarizer, AI Productivity Tools, AI Assistant |
| Pricing Model | Contact for Pricing | Freemium |
| Monthly Visits | 762,803 | 979 |
| MoM Growth Rate | -22.8% | -50% |
| Primary Market | United States | Türkiye |
| Direct Visits | 370,024 | 403 |
| Review Score | 4.4 | 4.2 |
| 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)

## TeamFeedback

TeamFeedback is an AI-powered employee feedback platform built around a simple observation: gathering feedback is easy, but reading, categorising and acting on it is where teams lose time. The tool brings 360-degree feedback, quick surveys, an employee and team dashboard, a feedback framework and a skills centre into one place, then uses AI to summarise it all into conclusions, insights and a performance report covering employee needs, issues and predictions.

The workflow is designed to reduce manual effort. You post a quick survey that takes about two minutes to complete, or run structured 360-degree feedback, and TeamFeedback's AI produces a fast summarisation you can scan at a glance — digging into individual comments only when something needs attention. From there you can set employee OKRs and goals, track skills in a matrix, and give people a way to grow on their own terms, helped by goals and mentors.

Credibility and compliance matter in HR tooling, and TeamFeedback leans on both: it is ISO 27001 certified and states it is funded by the European Union, having received roughly 1,000,000 PLN to develop AI supporting feedback. It is freemium, with a free tier for teams up to 10 people and paid plans for larger companies, and it advertises a free start with a demo booking.

**Pricing & Plans:** Freemium with three tiers. Free is $0 per user/month for teams up to 10 people. Basic is $6.95 per user/month for companies up to 250 people, adding manager templates. Professional is $8.50 per user/month for companies above 250 people, adding custom data exports and personal integrations and assistance. ISO 27001 certified.

- **360-degree feedback reports:** Collect structured 360-degree feedback and turn it into reports that support a culture of continuous improvement.
- **Quick surveys:** Post engaging surveys that take about two minutes to complete, gathering data on performance, mood and issues.
- **Employee and team dashboard:** An anonymised control panel gives an overview of team mood, work approach and open issues.
- **AI summaries and goals:** AI summarises multiple feedbacks into a quick review, then generates employee goals based on feedback and team skill gaps.
- **Skills centre and goals:** Track skills and plan development in a structured way, letting people set goals and share and grow skills over time.

**Strengths:** AI summarises multiple feedbacks in seconds instead of hours of manual reading; Ready-to-use 360-degree feedback templates and manager templates; Anonymised dashboard gives an honest read on team mood and issues; Skills matrix, OKRs and goals support structured development

**Limitations:** Full performance analytics and custom exports sit on the higher paid tier; Best value depends on broad team adoption, typical of 360 feedback tools; Interface is functional rather than heavily styled; Advanced integrations are limited to the upper plans

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

## Frequently Asked Questions

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

Labelbox has 762,803 monthly visits, while TeamFeedback has 979. 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%; TeamFeedback has -50%.

### 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. TeamFeedback: Freemium. Freemium with three tiers. Free is $0 per user/month for teams up to 10 people. Basic is $6.95 per user/month for companies up to 250 people, adding manager templates. Professional is $8.50 per user/month for companies above 250 people, adding custom data exports and personal integrations and assistance. ISO 27001 certified.

## 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 | [Voxpopme](https://openaitools.app/tool/voxpopme-com) | 301,522 | +38.5% | Contact for Pricing | Turn customer voices into confident decisions | [.md](https://openaitools.app/tool/voxpopme-com.md) |
| 7 | [Leapsome](https://openaitools.app/tool/leapsome) | 290,483 | -9.6% | Contact for Pricing | Agentic HR platform uniting HRIS, performance and talent management | [.md](https://openaitools.app/tool/leapsome.md) |
| 8 | [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) |
| 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 | [Confident AI](https://openaitools.app/tool/confident-ai) | 83,400 | -3.3% | Freemium, Paid, Contact for Pricing | Focuses strictly on LLM evaluation, testing, and observability rather than raw data annotation and training dataset curation. | [.md](https://openaitools.app/tool/confident-ai.md) |

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

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