# Labelbox vs Orca

Labelbox records 762,803 monthly visits with -22.8% month-over-month growth and Contact for Pricing pricing. Orca records 1,025,891 monthly visits with +1% growth and Free, Open Source, Enterprise on request pricing.

| Metric | [Labelbox](https://openaitools.app/tool/labelbox) | [Orca](https://openaitools.app/tool/orca) |
| :--- | :--- | :--- |
| Website URL | https://labelbox.com | https://www.onorca.dev |
| Categories | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools | AI Code Assistant, AI Agent, AI Developer Tools |
| Pricing Model | Contact for Pricing | Free, Open Source, Enterprise on request |
| Monthly Visits | 762,803 | 1,025,891 |
| MoM Growth Rate | -22.8% | +1% |
| Primary Market | United States | United States |
| Direct Visits | 370,024 | 344,591 |
| Review Score | 4.4 | 4.6 |
| Saved Count | 0 | 1 |
| 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)

## Orca

Orca positions itself as an "agent IDE" rather than a classic code editor. The core idea is that IDEs were designed around a single human editor, while AI-era workflows revolve around many agents working at the same time. Orca therefore organises work into projects, workspaces and worktrees: each task gets an isolated environment, and any agent CLI can be pointed at it.

The app bundles the pieces developers normally stitch together by hand — chat or terminal interfaces, a built-in browser with a design-inspection mode, split panes for diffs and files, a notes/editor surface and Git integration. A command-line interface mirrors the GUI, so agents themselves can create worktrees, start workers, drive the browser and schedule automations through Orca's own CLI.

Orca is cross-platform (macOS, Windows, Linux) with companion iOS and Android apps that notify you when an agent needs input so you can reply from your phone. It is distributed as free open-source software, and the project also offers a documented enterprise track and a plugin system for extending the environment.

**Pricing & Plans:** Orca is free and open source; the download and core product require no payment. A separate Enterprise offering is available, priced by contacting the team. The site does not publish a public price list for enterprise plans.

- **Parallel worktrees:** Each agent gets its own isolated Git worktree, so multiple agents can edit, test and build concurrently without conflicts.
- **Bring your own agent CLI:** Works with Claude Code, Codex, Gemini, Cursor CLI, OpenCode, Copilot, Cline and many other command-line agents using subscriptions you already have.
- **Integrated browser and design mode:** A built-in browser lets you click any element to copy its HTML, styles and selector, or screenshot a page and drop that context straight into an agent prompt.
- **Terminal, diffs and split panes:** Review AI output, run commands and arrange terminals, browsers, files and diffs in panes shaped to the task at hand.
- **CLI, plugins and mobile companion:** Orca's own CLI lets agents automate worktrees and workers, a plugin system extends the app, and iOS/Android apps keep you in the loop remotely.

**Strengths:** Free and open source, with no licence cost for the core product; Genuinely parallel workflow thanks to per-task Git worktrees; Vendor-neutral: works with many agent CLIs instead of locking you to one; Cross-platform with iOS and Android companion apps and an extensible plugin system

**Limitations:** Not a traditional IDE, so developers wanting a full editor experience may need a second tool; Newer product with fast-moving releases, which can mean occasional rough edges; Enterprise pricing is only available on request, so costs are not transparent up front

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

## Frequently Asked Questions

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

Labelbox has 762,803 monthly visits, while Orca has 1,025,891. 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%; Orca has +1%.

### 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. Orca: Free, Open Source, Enterprise on request. Orca is free and open source; the download and core product require no payment. A separate Enterprise offering is available, priced by contacting the team. The site does not publish a public price list for enterprise plans.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Cursor](https://openaitools.app/tool/cursor) | 23,297,751 | +8.4% | Freemium | An AI-native code editor optimized for multi-file generation rather than managing multiple parallel terminal agent worktrees. | [.md](https://openaitools.app/tool/cursor.md) |
| 2 | [Google Antigravity](https://openaitools.app/tool/google-antigravity) | 16,054,109 | +8.1% | Freemium | Provides an extensive platform and desktop command center for parallel AI coding agents, but with a heavier proprietary footprint. | [.md](https://openaitools.app/tool/google-antigravity.md) |
| 3 | [Devin](https://openaitools.app/tool/devin) | 2,549,346 | +1% | Paid | Cognition's autonomous software engineer that ships real pull requests | [.md](https://openaitools.app/tool/devin.md) |
| 4 | [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) |
| 5 | [Trae](https://openaitools.app/tool/trae) | 702,366 | -7.8% | Freemium | An AI IDE that handles full-project coding tasks, but lacks Orca's multi-agent parallel git worktree orchestration. | [.md](https://openaitools.app/tool/trae.md) |
| 6 | [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) |
| 7 | [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) |
| 8 | [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) |
| 9 | [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) |
| 10 | [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) |

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

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