# Labelbox vs Rerun

Labelbox records 762,803 monthly visits with -22.8% month-over-month growth and Contact for Pricing pricing. Rerun records 54,018 monthly visits with -23.7% growth and Free pricing.

| Metric | [Labelbox](https://openaitools.app/tool/labelbox) | [Rerun](https://openaitools.app/tool/rerun) |
| :--- | :--- | :--- |
| Website URL | https://labelbox.com | https://rerun.io |
| Categories | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools | AI Developer Tools, Log Management, AI For Data Analytics, AI Monitor |
| Pricing Model | Contact for Pricing | Free |
| Monthly Visits | 762,803 | 54,018 |
| MoM Growth Rate | -22.8% | -23.7% |
| Primary Market | United States | United States |
| Direct Visits | 370,024 | 24,880 |
| 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)

## Rerun

Rerun is an open-source SDK and visualizer built for multi-rate, multimodal data, and it markets itself as the data layer for physical AI. Engineers use it to log sensor streams, computer-vision outputs, 3D transforms, and time-series metrics from the first recording through to large-scale review. Because it is a flexible framework rather than a locked platform, teams can extend the viewer with their own views and tools at each stage of the pipeline.

The SDK ships with Python, Rust, and C++ APIs, so it fits into existing robotics and perception stacks without a rewrite. Data is stored as column-chunks in rrd files, which supports full dataframe or SQL queries over any recording, down into the columns, time ranges, and values inside it rather than only metadata.

Beyond the free tooling, Rerun offers Rerun Hub, a production backend that catalogs recordings, indexes byte ranges, and turns object stores into a queryable, streamable foundation. Hub supports running transforms on the edge or close to the data and streaming datasets directly to GPUs for training without an export step.

**Pricing & Plans:** The Rerun SDK and viewer are open source and free to use. Rerun also offers Rerun Hub, a production backend, whose pricing is arranged separately with the team. Check the Rerun website and GitHub repository for current terms.

- **Multimodal data visualization:** Log, store, and view images, point clouds, 3D transforms, and time series together in one viewer.
- **Open-source logging SDK:** Record multi-rate, multimodal data as column-chunks in rrd files, or convert from other formats.
- **Query and transform:** Run full dataframe or SQL queries over robotics data, down into columns, time ranges, and values.
- **Edge and stream transforms:** Add derived columns and evolve schemas without breaking history, on the edge or near the data.
- **Rerun Hub backend:** A hosted backend with catalog, byte-range indexing, and retrieval that turns object stores into a queryable dataset.

**Strengths:** Open-source core with permissive use across research and production; Handles multimodal and multi-rate data in one viewer; APIs for Python, Rust, and C++; Query and transform data without an export step

**Limitations:** Requires coding in Rust, Python, or C++ to get full value; The hosted Rerun Hub backend is a newer, additional product beyond the free SDK; Visualization work is aimed at engineering teams, not general business users; Advanced production features assume existing logging pipelines

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

## Frequently Asked Questions

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

Labelbox has 762,803 monthly visits, while Rerun has 54,018. 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%; Rerun has -23.7%.

### 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. Rerun: Free. The Rerun SDK and viewer are open source and free to use. Rerun also offers Rerun Hub, a production backend, whose pricing is arranged separately with the team. Check the Rerun website and GitHub repository for current terms.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Datadog](https://openaitools.app/tool/datadog) | 5,175,760 | -4.8% | Freemium, Free Trial, Paid, Contact for Pricing, Free | Unified observability, security and incident response for cloud-scale engineering teams | [.md](https://openaitools.app/tool/datadog.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 | [Arize AI](https://openaitools.app/tool/arize-com) | 249,028 | +3.2% | Paid, Contact for Pricing | Agent observability, evaluation, and continuous improvement for production AI | [.md](https://openaitools.app/tool/arize-com.md) |
| 8 | [Encord](https://openaitools.app/tool/encord-com) | 244,204 | +9.4% | Contact for Pricing | The multimodal data layer for computer vision and physical AI teams | [.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 | Label any data. Evaluate any AI. | [.md](https://openaitools.app/tool/label-studio.md) |
| 10 | [Maxim AI](https://openaitools.app/tool/maxim-ai) | 121,351 | +10% | Contact for Pricing, Free Open Source | Evaluate, monitor, and govern your AI applications in production | [.md](https://openaitools.app/tool/maxim-ai.md) |

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

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