# Lab Laps vs Labelbox

Lab Laps records 36,077 monthly visits with +203.3% month-over-month growth and Freemium, Contact for Pricing pricing. Labelbox records 762,803 monthly visits with -22.8% growth and Contact for Pricing pricing.

| Metric | [Lab Laps](https://openaitools.app/tool/lab-laps) | [Labelbox](https://openaitools.app/tool/labelbox) |
| :--- | :--- | :--- |
| Website URL | https://www.lablaps.com | https://labelbox.com |
| Categories | AI Image Recognition, AI Image Scanning | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools |
| Pricing Model | Freemium, Contact for Pricing | Contact for Pricing |
| Monthly Visits | 36,077 | 762,803 |
| MoM Growth Rate | +203.3% | -22.8% |
| Primary Market | United States | United States |
| Direct Visits | 14,179 | 370,024 |
| Review Score | 4.4 | 4.4 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## Lab Laps

Lab Laps is built around the reality of bench work, where a researcher juggles a protocol on paper, a phone timer, a spreadsheet for dilutions and separate imaging software for colony counts. The app consolidates those tasks into one environment: you write or import step-by-step protocols with timers and to-dos built in, run them at the bench, and share them with your lab. Cloud sync means the same projects open in the browser and in the iOS or Android app, and sharing links let colleagues view or import projects into their own workspace.

The AI features target the most repetitive parts of the day. Drop in a photo of a plate and the colony counter automatically boxes every colony, letting you correct the few it misses before exporting the count. A blood cell counter handles RBC, WBC and platelet counting, while gel and blot tools turn ladder and band images into publication-ready figures. Plate labelling supports colour-coded maps from 6 to 384 wells, so a layout can travel from screen to bench without transcription errors.

The calculation layer covers the arithmetic that quietly consumes lab time. Serial and reverse dilution, buffer and medium recipes, molarity, master mix and PCR setup are all handled with exact pipetting and weighing volumes, and a unit converter, custom equation calculator and periodic table round out the toolkit. Lab Laps reports being trusted by more than 70,000 researchers and offers a free tier with limited AI counts and up to five projects, with Pro removing those limits.

**Pricing & Plans:** Lab Laps is freemium. The free plan includes web lab tools with up to five projects, limited AI colony counts and access to the mobile app with up to two protocol projects. Pro unlocks unlimited projects, unlimited AI colony counting, premium tools and the full Pro experience in the iOS and Android apps. Enterprise and lab-wide arrangements are handled via contact.

- **Protocol tracking with built-in timers:** Write step-by-step protocols with timers and to-dos embedded, run multiple timers simultaneously at the bench, and share protocols with your whole lab.
- **AI colony and cell counting:** Upload a plate photo and the AI boxes every colony for quick correction; separate counters handle blood cell counting for RBC, WBC and platelets.
- **Lab calculators and conversions:** Serial and reverse dilution, buffer and medium recipes, molarity, master mix and PCR setup deliver exact volumes, alongside a unit converter, custom equation calculator and periodic table.
- **Gel, blot and plate tools:** Turn ladder and band images into labelled, publishable figures and build colour-coded plate maps from 6 up to 384 wells.
- **Cloud sync and project sharing:** Data syncs across browser and mobile so nothing is lost between devices, and share links let collaborators view or import projects within 30 days.

**Strengths:** Consolidates protocols, timers, calculators and counting that normally need several apps; AI colony counting meaningfully reduces a repetitive daily task; Free tier is genuinely usable, with paid Pro removing limits; Cross-device sync plus sharing links work well for team labs

**Limitations:** Free tier limits the number of projects and AI counts; AI counting accuracy still needs a human correction pass; Best value depends on adopting it lab-wide rather than by one person

[Profile (Markdown)](https://openaitools.app/tool/lab-laps.md)

## 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)

## Frequently Asked Questions

### How do Lab Laps and Labelbox compare in monthly traffic?

Lab Laps has 36,077 monthly visits, while Labelbox has 762,803. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Lab Laps has a month-over-month growth rate of +203.3%; Labelbox has -22.8%.

### How does pricing compare?

Lab Laps: Freemium, Contact for Pricing. Lab Laps is freemium. The free plan includes web lab tools with up to five projects, limited AI colony counts and access to the mobile app with up to two protocol projects. Pro unlocks unlimited projects, unlimited AI colony counting, premium tools and the full Pro experience in the iOS and Android apps. Enterprise and lab-wide arrangements are handled via contact. 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.

## 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 | [Pl@ntNet](https://openaitools.app/tool/plantnet-org) | 439,904 | +34.9% | Free | Identify plants from photos and contribute to biodiversity science | [.md](https://openaitools.app/tool/plantnet-org.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 | [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) |
| 9 | [Viso Now](https://openaitools.app/tool/viso-now) | 111,150 | -3% | Freemium, Contact for Pricing | Go from an idea to a live computer-vision app, agentically | [.md](https://openaitools.app/tool/viso-now.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/lab-laps-vs-labelbox

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