# ExportTok vs Labelbox

ExportTok records 2,782,229 monthly visits with +9.2% month-over-month growth and Freemium pricing. Labelbox records 762,803 monthly visits with -22.8% growth and Contact for Pricing pricing.

| Metric | [ExportTok](https://openaitools.app/tool/exporttok) | [Labelbox](https://openaitools.app/tool/labelbox) |
| :--- | :--- | :--- |
| Website URL | https://exporttok.com | https://labelbox.com |
| Categories | Other | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools |
| Pricing Model | Freemium | Contact for Pricing |
| Monthly Visits | 2,782,229 | 762,803 |
| MoM Growth Rate | +9.2% | -22.8% |
| Primary Market | United States | United States |
| Direct Visits | 1,199,074 | 370,024 |
| Review Score | 4.1 | 4.4 |
| Saved Count | 1 | 0 |
| Supported Platforms | Website | Website |

## ExportTok

ExportTok solves a very specific headache: TikTok gives creators, marketers and researchers no native way to get comment data out of the app in bulk. Screenshotting or copy-pasting hundreds of comments is slow, error-prone and impossible to analyse at scale. ExportTok takes a public video URL and returns the full comment set as a CSV or XLSX file you can sort, filter and chart in Excel, Google Sheets or a BI tool.

The workflow is deliberately minimal. You copy a video link from the TikTok app or web player, paste it into the search box on the landing page, and press export. Before downloading you can cap the number of comments, pick CSV or Excel, and switch replies on or off so long threads come through complete rather than truncated. Batch handling lets you work through a list of videos in one sitting, which matters when you are auditing a whole campaign rather than a single post.

Every exported row carries the fields analysts actually need: comment text, comment ID, user ID, username and nickname, timestamp, like count, reply count, pinned status and profile links. Because no TikTok account is connected, there is no login risk and no chance of your own account being flagged. ExportTok uses a credit system, starting with 2,000 free comments and scaling up to millions per month on the paid tiers.

**Pricing & Plans:** Freemium with a credit model. The free plan covers 2,000 comments. Paid tiers are Essential at $9.99/month (500,000 credits), Growth at $14.99/month (1,000,000 credits) and Pro at $19.99/month (3,000,000 credits), all with yearly discounts of roughly 50%.

- **One-click URL export:** Paste any public TikTok video URL and download its comment thread without installing anything or writing code.
- **CSV and Excel output:** Choose CSV for pipelines and scripts or XLSX for spreadsheets, so the file drops straight into your existing analysis workflow.
- **No-login anonymous browsing:** Comments are pulled without connecting a TikTok account, so there is no ban risk and no personal identity tied to the export.
- **Full thread detail including replies:** Each row carries comment text, IDs, username and nickname, timestamp, likes, reply count, pinned status and profile links, with nested replies optional.
- **Batch and credit-based scaling:** Queue up multiple videos at once and spend credits against plans ranging from 500,000 to 3,000,000 comments per month.

**Strengths:** Works without a TikTok login, removing any account-suspension risk; Exports structured fields including IDs, timestamps and like counts, not just raw text; Generous free tier of 2,000 comments for testing and small jobs; Batch export across multiple video URLs in a single session

**Limitations:** Only public videos with visible comments can be exported; Credit consumption scales with comment volume, so large studies get expensive quickly; TikTok's displayed comment count is not real-time, so totals may differ slightly from what the app shows

[Profile (Markdown)](https://openaitools.app/tool/exporttok.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 ExportTok and Labelbox compare in monthly traffic?

ExportTok has 2,782,229 monthly visits, while Labelbox has 762,803. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

ExportTok has a month-over-month growth rate of +9.2%; Labelbox has -22.8%.

### How does pricing compare?

ExportTok: Freemium. Freemium with a credit model. The free plan covers 2,000 comments. Paid tiers are Essential at $9.99/month (500,000 credits), Growth at $14.99/month (1,000,000 credits) and Pro at $19.99/month (3,000,000 credits), all with yearly discounts of roughly 50%. 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 | [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 | [Gumloop](https://openaitools.app/tool/gumloop) | 379,557 | -21.1% | Freemium, Paid, Contact for Pricing | Builds custom AI scraping workflows using no-code blocks, but lacks dedicated pre-built templates specifically for TikTok comments. | [.md](https://openaitools.app/tool/gumloop.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 | [PhantomBuster](https://openaitools.app/tool/phantombuster) | 279,012 | -10.1% | Freemium, Free Trial, Paid | Automates cloud-based data extraction across social platforms, but requires configuring workflow parameters rather than instant one-click downloads. | [.md](https://openaitools.app/tool/phantombuster.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 | [Octoparse](https://openaitools.app/tool/octoparse) | 184,251 | +1.2% | Freemium, Free Trial, Paid, Contact for Pricing | Offers powerful visual web scraping for complex sites, but requires local software setup rather than instant browser exports. | [.md](https://openaitools.app/tool/octoparse.md) |
| 10 | [Defined.ai](https://openaitools.app/tool/definedcrowd-com) | 146,251 | -21% | Contact for Pricing | Enterprise AI training data, annotation and evaluation services | [.md](https://openaitools.app/tool/definedcrowd-com.md) |

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

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