# Labelbox vs YepCode

Labelbox records 762,803 monthly visits with -22.8% month-over-month growth and Contact for Pricing pricing. YepCode records 13,298 monthly visits with +24.3% growth and Freemium, Paid, Contact for Pricing pricing.

| Metric | [Labelbox](https://openaitools.app/tool/labelbox) | [YepCode](https://openaitools.app/tool/yepcode) |
| :--- | :--- | :--- |
| Website URL | https://labelbox.com | https://yepcode.io |
| Categories | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools | AI Developer Tools, AI API, AI Agent, AI Workflow |
| Pricing Model | Contact for Pricing | Freemium, Paid, Contact for Pricing |
| Monthly Visits | 762,803 | 13,298 |
| MoM Growth Rate | -22.8% | +24.3% |
| Primary Market | United States | United States |
| Direct Visits | 370,024 | 5,199 |
| Review Score | 4.4 | 4.2 |
| 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)

## YepCode

YepCode targets developers who need real integrations rather than drag-and-drop automation. Instead of chaining pre-built connectors, you write Node.js or Python code that calls any API, database or cloud service, and YepCode handles the runtime: isolated sandboxes with CPU and memory limits, on-demand dependency installation from NPM and PyPI, encrypted secrets, structured logs and an audit trail of every change. Processes run against versions and aliases, so you can push, test and promote changes predictably.

The platform is equally usable as a service. Every process can be triggered on demand, on a cron schedule, from a webhook, via a form, or programmatically through the REST API and the YepCode SDK and CLI. Teams typically reach for it when they have outgrown Zapier or Make but do not want to stand up and maintain their own job-running infrastructure. The vendor reports that early teams cut time-to-deploy substantially compared with building the plumbing themselves.

Because each execution is isolated and auditable, YepCode is a common wrapper for AI-generated code: a model can propose a script, and the platform runs it with scoped credentials and full logging. The MCP server pushes this further, exposing any process as a tool an LLM agent can call, plus a built-in run_code tool for direct execution. Compliance features include SOC 2 and GDPR alignment, on-premise or private-cloud deployment, identity provider federation and external log and metrics streaming. Note that the site currently states YepCode is not taking on new customers.

**Pricing & Plans:** Freemium plus paid tiers and enterprise pricing. The free Developer plan includes 50K Yeps (seconds of execution), 300 executions per day, 30-second maximum runtime, one concurrent execution and a single team member. Starter and Growth are usage-based monthly plans at 15M and 150M Yeps with longer runtimes and more concurrency. Enterprise is quoted per company with customizable sandboxes, full on-premise deployment and custom SLAs. Yeps do not roll over between months.

- **Code-first integrations:** Write Node.js or Python in the web editor, the CLI or your IDE, push changes, version them with aliases and sync with a Git repository.
- **Secure isolated sandboxes:** Each process runs in an isolated container with strict boundaries; secrets are encrypted, access is logged and every change is auditable.
- **Automatic dependency management:** Import any NPM or PyPI package directly - the platform installs dependencies on demand, so there is no environment setup to maintain.
- **Multiple execution triggers:** Trigger processes on demand, on a cron schedule, from a form or webhook, or programmatically through the REST API and SDK.
- **MCP server for AI agents:** Any process can be published as an MCP tool, and a built-in run_code tool lets agents execute code safely against your systems.
- **Enterprise-grade controls:** SOC 2 and GDPR compliant, with on-premise or private-cloud deployment, identity provider integration, external log streaming and custom SLAs.

**Strengths:** Lets developers use any API, database or service with real code instead of rigid connectors; Dependencies, secrets, logs and audit trails are handled by the platform; Multiple trigger types cover on-demand, scheduled, webhook and API runs; MCP server exposes processes as tools for LLM agents

**Limitations:** Not accepting new customers at this time, which blocks new signups; Unlimited concurrency and audit modules sit on higher-priced tiers; Yeps are execution seconds and do not roll over month to month; On-premise deployments require the Enterprise plan

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

## Frequently Asked Questions

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

Labelbox has 762,803 monthly visits, while YepCode has 13,298. 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%; YepCode has +24.3%.

### 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. YepCode: Freemium, Paid, Contact for Pricing. Freemium plus paid tiers and enterprise pricing. The free Developer plan includes 50K Yeps (seconds of execution), 300 executions per day, 30-second maximum runtime, one concurrent execution and a single team member. Starter and Growth are usage-based monthly plans at 15M and 150M Yeps with longer runtimes and more concurrency. Enterprise is quoted per company with customizable sandboxes, full on-premise deployment and custom SLAs. Yeps do not roll over between months.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Google Antigravity](https://openaitools.app/tool/google-antigravity) | 16,054,109 | +8.1% | Freemium | Google's agent-first development platform for building and shipping software with AI agents | [.md](https://openaitools.app/tool/google-antigravity.md) |
| 2 | [Replit](https://openaitools.app/tool/replit) | 10,016,437 | -7.7% | Freemium, Paid | Describe your idea and Replit builds a working app in minutes | [.md](https://openaitools.app/tool/replit.md) |
| 3 | [Zapier](https://openaitools.app/tool/zapier-com) | 5,065,248 | +0.4% | Freemium, Free Trial, Paid | Automate work across thousands of apps — no code required | [.md](https://openaitools.app/tool/zapier-com.md) |
| 4 | [n8n](https://openaitools.app/tool/n8n) | 4,522,449 | -7.4% | Freemium | Open-source workflow automation built for AI agents and technical teams | [.md](https://openaitools.app/tool/n8n.md) |
| 5 | [Apify](https://openaitools.app/tool/apify) | 4,147,762 | +3.9% | Freemium, Contact for Pricing | Run thousands of ready-made web scrapers and AI agents from one cloud platform | [.md](https://openaitools.app/tool/apify.md) |
| 6 | [MindStudio](https://openaitools.app/tool/mindstudio) | 1,793,337 | +14.2% | Contact for Pricing | No-code builder for custom AI agents and workflows | [.md](https://openaitools.app/tool/mindstudio.md) |
| 7 | [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) |
| 8 | [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) |
| 9 | [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) |
| 10 | [Convex](https://openaitools.app/tool/convex) | 390,417 | -18.7% | Freemium, Paid | Reactive TypeScript backend with database, functions and AI gateway | [.md](https://openaitools.app/tool/convex.md) |

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

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