# Labelbox vs Skyelight

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

| Metric | [Labelbox](https://openaitools.app/tool/labelbox) | [Skyelight](https://openaitools.app/tool/skeylight) |
| :--- | :--- | :--- |
| Website URL | https://labelbox.com | https://www.skyelight.ai |
| Categories | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools | AI Developer Tools, AI Product Manager, AI Productivity Tools |
| Pricing Model | Contact for Pricing | Freemium, Contact for Pricing |
| Monthly Visits | 762,803 | 0 |
| MoM Growth Rate | -22.8% | 0% |
| Primary Market | United States | Global |
| Direct Visits | 370,024 | 0 |
| Review Score | 4.4 | 4.5 |
| 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)

## Skyelight

Skyelight replaces the familiar mess of screenshots, email chains and Slack messages with feedback that is anchored to the thing being discussed. Reviewers — designers, clients, product managers, freelancers, stakeholders who join as free reviewers — click an element on the real page, from localhost prototype through to production, and drop a comment on it. Each thread carries context such as the component, the source file and line, the branch and the commit, so nobody has to guess what "this button is cut off on iPhone" refers to.

A distinguishing feature is automatic classification: as a comment is typed, Skyelight tags it as a bug, feedback, idea, design inspiration, page copy or another custom type, and you can filter and bulk-manage threads by category. Teams can also publish Claude artifacts and design surfaces into Skyelight so external stakeholders or clients can comment on a hosted link, and everything returns to the same workspace.

What makes it unusual is agent access. An MCP server (and a REST API) is available on every plan, so Claude, Claude Code, Cursor, Codex, Grok, Windsurf, Lovable and Bolt can list open threads, fix them in the codebase and reply on each thread with a link to the commit. The vendor positions this as taking feedback loops beyond Slack, Teams, email and Google Docs while keeping the human review workflow intact for people who like working collaboratively. Skyelight ships a free tier with MCP and REST API included, plus paid Pro and Team plans for heavier use.

**Pricing & Plans:** Freemium. A free plan is available with MCP server and REST API included on every plan; paid tiers are shown on the site (Pro at $29/month and Team at $59/month at the time of review). Annual pricing and enterprise arrangements require contacting the vendor.

- **Element-pinned annotations:** Comment on any element of any page — prototype, staging, localhost or production — so every thread is attached to the exact element it refers to.
- **MCP server and REST API on every plan:** Claude, Claude Code, Cursor, OpenAI Codex, Grok, Windsurf, Lovable and Bolt can list threads, apply fixes and reply with the commit that closed them.
- **Automatic feedback classification:** Comments are classified as they are typed — bug, feedback, idea, design inspiration or page copy — and can be sorted or bulk-managed by type.
- **Client and stakeholder review links:** Publish Claude artifacts or design surfaces to a hosted link; external reviewers join free and their comments land back in your workspace.
- **Rich context on every thread:** Threads carry the pinned component, file path and line number, branch and commit, so developers and agents work in the right place.

**Strengths:** Feedback is anchored to the actual element, so there is no ambiguity about what is being discussed; MCP server and REST API included on every plan, including free; Automatic classification and filtering keeps bug, copy and idea threads separate; Clients and stakeholders can join as free reviewers, keeping costs predictable

**Limitations:** Another tool to introduce into an existing Slack/Teams workflow; Value depends on your team or agent actually reading the threads; Agent integrations need some setup knowledge (MCP configuration, repo access)

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

## Frequently Asked Questions

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

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

### 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. Skyelight: Freemium, Contact for Pricing. Freemium. A free plan is available with MCP server and REST API included on every plan; paid tiers are shown on the site (Pro at $29/month and Team at $59/month at the time of review). Annual pricing and enterprise arrangements require contacting the vendor.

## 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 | [Jam](https://openaitools.app/tool/jamgpt) | 570,598 | -6% | Freemium, Paid | Screen recorder that captures bugs with full technical context | [.md](https://openaitools.app/tool/jamgpt.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 | [Productboard](https://openaitools.app/tool/productboard) | 297,500 | -9.3% | Freemium, Free Trial, Paid, Contact for Pricing | Agentic product management system for customer-driven roadmaps | [.md](https://openaitools.app/tool/productboard.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 | [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-skeylight

More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)