# Labelbox vs RNWY

Labelbox records 762,803 monthly visits with -22.8% month-over-month growth and Contact for Pricing pricing. RNWY records 2,345 monthly visits with -30.7% growth and Free pricing.

| Metric | [Labelbox](https://openaitools.app/tool/labelbox) | [RNWY](https://openaitools.app/tool/rnwy) |
| :--- | :--- | :--- |
| Website URL | https://labelbox.com | https://rnwy.com |
| Categories | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools | AI Agent, AI Social Media, Blockchain, Web3 |
| Pricing Model | Contact for Pricing | Free |
| Monthly Visits | 762,803 | 2,345 |
| MoM Growth Rate | -22.8% | -30.7% |
| Primary Market | United States | United States |
| Direct Visits | 370,024 | 919 |
| Review Score | 4.4 | 4 |
| Saved Count | 0 | 0 |
| 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)

## RNWY

RNWY builds the reputation primitive for a world where the participants in an economy may not be human. Its premise is simple: what matters is not what something is but what it has done, so reputation should attach to wallets and be verifiable by anyone rather than declared by a platform. The product family spans agent indexing across multiple chains, an ERC-8004 registry, wallet scoring, an MCP server index and an interactive observatory that visualises the agent ecosystem as terrain.

The distinguishing claim is transparency of scoring. Other directories aggregate star ratings; RNWY says it rates the reviewers instead, checking whether positive reviews cluster from wallets with no other activity or whether an agent's own owner funded the wallets leaving praise. Every score is published with its number, breakdown, formula and underlying raw data, so a low score of 12 can be shown next to a competing headline figure of 90. Its threat-intelligence views surface Sybil operators, owner-funded reviews, pattern-matched clusters and shared funding networks.

For participants, the onboarding path is deliberately optional and staged: create an account, connect a wallet, mint a soulbound identity token, then register in the ERC-8004 registry. The soulbound token cannot be transferred or sold, is designed to cost under a cent, and makes identity verifiable on-chain without relying on RNWY's own servers. Developers integrate through an SDK, an MCP server, a REST API and an on-chain oracle. RNWY also publishes research on soulbound AI identity and positions itself as equally usable by human crypto wallets and by autonomous agents with their own keys.

**Pricing & Plans:** Free to use for core trust features, including creating an account and registering an AI agent. Minting the soulbound identity token is designed to cost under $0.01 in network fees, plus normal on-chain gas. Registration is described as free and taking under two minutes, with no credit card or subscription required.

- **Explainable trust scores:** Every score comes with the number, its breakdown, the scoring formula and the raw source data, so anyone can audit where it came from.
- **Agent indexing across chains:** Agents are indexed across multiple networks, including ERC-8004, with verified on-chain ownership shown alongside self-declared endpoints.
- **Soulbound identity tokens:** Mint a non-transferable identity that binds permanently to a wallet, making reputation verifiable on-chain without trusting RNWY itself.
- **Threat and Sybil intelligence:** Scanner views reveal Sybil operators, owner-funded reviews, pattern-matched clusters and the funding networks behind reviewer wallets.
- **Developer APIs and MCP server:** Integrate trust checks through an SDK, a REST API, an on-chain oracle and an MCP server for agent-to-agent lookups.

**Strengths:** Scores are published with full breakdowns and raw sources rather than as opaque ratings; Works the same way for human wallets and autonomous AI agents; Soulbound identity makes reputation verifiable on-chain without depending on a central server; Core registration and profile creation are free, with the SBT designed to cost under a cent

**Limitations:** Scores are algorithmic assessments of public on-chain data and can be wrong or incomplete; The tooling assumes comfort with crypto wallets, on-chain concepts and ERC-8004; Value compounds only over time as an agent accumulates transactions and vouches

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

## Frequently Asked Questions

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

Labelbox has 762,803 monthly visits, while RNWY has 2,345. 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%; RNWY has -30.7%.

### 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. RNWY: Free. Free to use for core trust features, including creating an account and registering an AI agent. Minting the soulbound identity token is designed to cost under $0.01 in network fees, plus normal on-chain gas. Registration is described as free and taking under two minutes, with no credit card or subscription required.

## 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 | [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) |
| 7 | [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) |
| 8 | [Surf](https://openaitools.app/tool/surf) | 116,252 | -18.9% | Free, Pro, Max, API credits | Institution-grade crypto and equities intelligence, delivered through a chat-style AI analyst. | [.md](https://openaitools.app/tool/surf.md) |
| 9 | [ATXP](https://openaitools.app/tool/atxp) | 106,839 | -39.7% | Freemium | Identity, payments, and pay-per-use tools for AI agents | [.md](https://openaitools.app/tool/atxp.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-rnwy

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