# Labelbox vs Step1

Labelbox records 762,803 monthly visits with -22.8% month-over-month growth and Contact for Pricing pricing. Step1 records 0 monthly visits with — growth and Free pricing.

| Metric | [Labelbox](https://openaitools.app/tool/labelbox) | [Step1](https://openaitools.app/tool/step1) |
| :--- | :--- | :--- |
| Website URL | https://labelbox.com | https://chromewebstore.google.com/detail/step1/aghjfakpkmlhmppddoingmepnlignjgd |
| Categories | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools | Web3, Graph AI, AI For Data Analytics |
| Pricing Model | Contact for Pricing | Free |
| Monthly Visits | 762,803 | 0 |
| MoM Growth Rate | -22.8% | — |
| Primary Market | United States | Global |
| Direct Visits | 370,024 | 0 |
| Review Score | 4.4 | 1.8 |
| Saved Count | 0 | 1 |
| Supported Platforms | Website | Browser Extension |

## 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)

## Step1

Step1 is a Chrome extension that describes itself as an AI-based Web3 user graph. Rather than judging accounts on raw activity alone, it analyses behaviour and reputation signals to build a profile of each participant, then estimates how much that user deserves from reward and incentive programs.

The problem it targets is a familiar one in Web3: distinguishing genuine contributors from freeloaders and automated farming accounts. Incentive campaigns are frequently drained by bots and low-effort wallets, while real users are under-rewarded. Step1's stated goal is to shift that balance by scoring quality of engagement and proposing economic models that reward it.

It is a lightweight extension installed from the Chrome Web Store, offered by Step1 official. It collects web history to perform its analysis and declares that this data is not sold to third parties or used for creditworthiness decisions. Note that the listing has not been updated since mid-2023 and has a small user base, so it is best treated as an early-stage Web3 experiment rather than a mainstream productivity tool.

**Pricing & Plans:** Free to install from the Chrome Web Store. No paid tier is listed.

- **AI-driven user graph:** AI and big-data methods combine on-chain signals into a picture of who a Web3 user is and how they behave.
- **Reward optimisation:** Scores and compares participants so that genuine contributors stand out from freeloaders and farm accounts.
- **Fair incentive models:** Aimed at making incentive programs fairer by rewarding high-quality participation rather than volume.
- **Simple Chrome extension:** Installs from the Chrome Web Store and runs alongside your normal Web3 browsing.
- **Cross-platform analysis:** Analyses user activity and reputation across Web3 rather than relying on a single protocol's data.

**Strengths:** Free Chrome extension with no paid tier; Targets a real Web3 problem: separating real users from freeloaders; Runs in the browser with minimal setup; Declares data is not sold to third parties

**Limitations:** Very small install base (roughly 73 users), so there is little community or documentation support; Extension has not been updated since July 2023, which is a long gap for a Web3 product; Requires a Chrome-based browser and an active Web3 wallet to be useful; Claims and metrics around reward optimization are hard to verify independently

[Profile (Markdown)](https://openaitools.app/tool/step1.md)

## Frequently Asked Questions

### How do Labelbox and Step1 compare in monthly traffic?

Labelbox has 762,803 monthly visits, while Step1 has 0. 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%; Step1 has —.

### 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. Step1: Free. Free to install from the Chrome Web Store. No paid tier is listed.

## 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 | [Thunderbit](https://openaitools.app/tool/thunderbit) | 391,726 | -21.9% | Freemium, Contact for Pricing | Agentic web scraper that turns any page into ready-to-use data | [.md](https://openaitools.app/tool/thunderbit.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 | [BlockSurvey](https://openaitools.app/tool/block-survey) | 302,480 | -14.9% | Freemium, Paid | Privacy-first AI surveys with end-to-end encrypted responses | [.md](https://openaitools.app/tool/block-survey.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 | [Simple Analytics](https://openaitools.app/tool/simpleanalytics-com) | 106,690 | -9.3% | Freemium, Paid | Cookie-free web analytics you can run without consent banners or personal data | [.md](https://openaitools.app/tool/simpleanalytics-com.md) |

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Source: https://openaitools.app/compare/labelbox-vs-step1

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