# Labelbox vs Numerous.ai

Labelbox records 762,803 monthly visits with -22.8% month-over-month growth and Contact for Pricing pricing. Numerous.ai records 128,828 monthly visits with -17% growth and Freemium, Free Trial, Paid pricing.

| Metric | [Labelbox](https://openaitools.app/tool/labelbox) | [Numerous.ai](https://openaitools.app/tool/numerous) |
| :--- | :--- | :--- |
| Website URL | https://labelbox.com | https://numerous.ai |
| Categories | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools | AI Spreadsheet, Excel AI, AI Excel Formula Generator, AI Writing Assistants |
| Pricing Model | Contact for Pricing | Freemium, Free Trial, Paid |
| Monthly Visits | 762,803 | 128,828 |
| MoM Growth Rate | -22.8% | -17% |
| Primary Market | United States | United States |
| Direct Visits | 370,024 | 32,733 |
| Review Score | 4.4 | 4.5 |
| Saved Count | 0 | 4 |
| 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)

## Numerous.ai

Numerous.ai targets a very practical gap: most teams work in spreadsheets, but most AI chat tools live in a separate browser tab. The add-on closes that gap by exposing AI as a spreadsheet function. You type a prompt in one cell, reference other cells in it, and drag the result down a column — the AI then returns a distinct answer for every row, whether that is a rewritten product description, a categorised support ticket or a normalised country name.

Because it is built for bulk use, the product emphasises cost control and reuse. Identical queries are cached rather than re-run, which keeps token consumption down across large sheets, and the pricing is expressed in characters of input and output per person rather than opaque credits. There is no need to hold your own API keys — the service manages provider access, and a single plan covers everyone on the team.

The typical audience is operations, marketing and research teams. Marketers generate ad copy variants, keyword lists and campaign structures at scale; researchers summarise, extract and classify survey or social responses; data teams clean and standardise messy inputs; product people prototype prompts and share results. Setup is a one-click add-on install for both Google Sheets and Excel, and support is handled by email from a US-based team.

**Pricing & Plans:** Paid subscription with a paid trial: 7-day trial for $1, then a per-person plan that includes 1,000,000 characters of ChatGPT input and output per person, usable in both Google Sheets and Excel. Annual billing saves around 57%. Cancel anytime and keep remaining tokens; custom plans available on request.

- **AI as a spreadsheet formula:** Use AI in any cell through a simple function — no API keys, no external chat window, no copy-paste between tabs.
- **Bulk processing with caching:** Drag a single prompt down a column to process thousands of rows, with identical queries cached so you are not charged twice for the same work.
- **Summarisation and classification:** Summarise, rewrite, extract and classify long bodies of text such as surveys, articles or social posts into clean structured columns.
- **Marketing content at scale:** Generate ad copy, keyword lists, SEO content and campaign structures directly from your sheet's own data.
- **Team-friendly plans and support:** One plan covers the whole team, results are shareable within the sheet, and priority email support is included.

**Strengths:** Works inside the tools teams already use, so no new workflow to learn; No API keys or provider accounts to set up; Query caching keeps bulk runs cost-efficient, especially across thousands of rows; One plan covers the whole team in both Google Sheets and Excel

**Limitations:** Requires a paid trial and subscription rather than a permanent free tier; Usage is capped by character allowance, which heavy bulk users can exhaust; It depends on third-party AI providers, so model behaviour and availability are outside your control

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

## Frequently Asked Questions

### How do Labelbox and Numerous.ai compare in monthly traffic?

Labelbox has 762,803 monthly visits, while Numerous.ai has 128,828. 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%; Numerous.ai has -17%.

### 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. Numerous.ai: Freemium, Free Trial, Paid. Paid subscription with a paid trial: 7-day trial for $1, then a per-person plan that includes 1,000,000 characters of ChatGPT input and output per person, usable in both Google Sheets and Excel. Annual billing saves around 57%. Cancel anytime and keep remaining tokens; custom plans available on request.

## 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 | [GPT Workspace](https://openaitools.app/tool/gpt-workspace-1) | 431,507 | +43.8% | Freemium, Free Trial, Paid | Bring ChatGPT, Claude and Gemini into Google Docs, Sheets, Slides and Gmail | [.md](https://openaitools.app/tool/gpt-workspace-1.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 | [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 | [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 | [GPT for Work (Autosheet)](https://openaitools.app/tool/gpt-for-work) | 160,670 | -13.5% | Free Trial, Paid | AI spreadsheet agent for Excel and Google Sheets | [.md](https://openaitools.app/tool/gpt-for-work.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 | [GPTExcel](https://openaitools.app/tool/gptexcel) | 120,862 | -14.8% | Freemium | AI spreadsheet assistant for formulas, pivots and analysis | [.md](https://openaitools.app/tool/gptexcel.md) |

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

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