# Labelbox vs Oak

Labelbox records 762,803 monthly visits with -22.8% month-over-month growth and Contact for Pricing pricing. Oak records 21,112 monthly visits with +36.5% growth and Contact for Pricing pricing.

| Metric | [Labelbox](https://openaitools.app/tool/labelbox) | [Oak](https://openaitools.app/tool/oak) |
| :--- | :--- | :--- |
| Website URL | https://labelbox.com | https://www.oak.id |
| Categories | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools | Other |
| Pricing Model | Contact for Pricing | Contact for Pricing |
| Monthly Visits | 762,803 | 21,112 |
| MoM Growth Rate | -22.8% | +36.5% |
| Primary Market | United States | Israel |
| Direct Visits | 370,024 | 9,157 |
| 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)

## Oak

Oak describes itself as an AI-native identity operating system, born complete rather than retrofitted for AI or assembled through acquisitions. Its core argument is that AI has pushed identity to the frontline of cybersecurity: identity data is fragmented across tools, reviews are rule-based and periodic, service accounts and AI agent behavior create blind spots, and teams often can see risks without being able to remediate them. Oak's answer is to own the full stack, from seeing every identity in an environment to controlling what each one can do based on intent and context.

The foundation is an Identity Intelligence Layer that acts as one live source of truth — a single data model and graph for the whole identity security stack. On top of it, Oak governs human identities, machine and service accounts, and AI agents, so autonomous agents can be inventoried with accountable owners, effective permissions, and a record of who they act for. Because Oak is positioned as "connect any," it aims to integrate with whatever environments an organization already runs and adapt as new systems are added.

For enterprises that want hands-on help, Oak offers White Gloves: a dedicated identity services team that builds, migrates, and manages identity programs end to end, including enterprise system integration, rapid deployment, and customized solutions. The company targets enterprise scale, with the Oak Promise offering implementation hours and all connectors to qualified Fortune 500 organizations.

**Pricing & Plans:** Contact for pricing. Oak does not publish public self-serve tiers; pricing is quoted to qualified organizations. The company advertises an Oak Promise (up to 500 free human hours of White Gloves work and all connectors included) available to qualified Fortune 500 enterprises.

- **Identity Intelligence Layer:** A centrally owned identity platform where every identity is discovered, seen, and governed from one live graph rather than scattered tool-specific lists.
- **Agentic identity governance:** Inventory and govern autonomous AI agents and MCP servers as identities, with owners, scoped permissions, and records of who they act for.
- **Connect-any integrations:** Connect to the identity, security, and enterprise systems you already run, aiming for one data model instead of a patchwork of acquired tools.
- **White Gloves managed services:** A dedicated services team that builds, migrates, and manages identity programs end to end, from discovery through ongoing management.
- **Context-aware, continuous governance:** Go beyond quarterly, rule-based reviews with a live source of truth that reflects what is happening across the environment now.

**Strengths:** One live identity graph as a single source of truth instead of fragmented tools; Covers human, machine, and AI agent identities in one platform; White Gloves services team handles implementation, migration, and management; Connect-any integrations aim to fit existing enterprise environments

**Limitations:** Pricing requires contacting sales rather than self-serve checkout; Designed for enterprises, so smaller teams may find the scope heavy; Value depends on connecting Oak to your existing identity and security stack; Newer category, so buyers should validate fit through a demo

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

## Frequently Asked Questions

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

Labelbox has 762,803 monthly visits, while Oak has 21,112. 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%; Oak has +36.5%.

### 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. Oak: Contact for Pricing. Contact for pricing. Oak does not publish public self-serve tiers; pricing is quoted to qualified organizations. The company advertises an Oak Promise (up to 500 free human hours of White Gloves work and all connectors included) available to qualified Fortune 500 enterprises.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Duck.ai](https://openaitools.app/tool/duck-ai) | 26,431,539 | +10.7% | Free | Private AI chat from the makers of DuckDuckGo | [.md](https://openaitools.app/tool/duck-ai.md) |
| 2 | [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) |
| 3 | [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) |
| 4 | [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) |
| 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 | [Dex — Personal CRM & LinkedIn Assistant](https://openaitools.app/tool/linkedin-gpt-assistant-by-dex) | 156,354 | +1.6% | Paid | The personal CRM that keeps your network warm — with your LinkedIn graph baked in | [.md](https://openaitools.app/tool/linkedin-gpt-assistant-by-dex.md) |
| 9 | [Defined.ai](https://openaitools.app/tool/definedcrowd-com) | 146,251 | -21% | Contact for Pricing | Enterprise AI training data, annotation and evaluation services | [.md](https://openaitools.app/tool/definedcrowd-com.md) |
| 10 | [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) |

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

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