# Labelbox vs ModAstera

Labelbox records 762,803 monthly visits with -22.8% month-over-month growth and Contact for Pricing pricing. ModAstera records 0 monthly visits with -100% growth and Contact for Pricing pricing.

| Metric | [Labelbox](https://openaitools.app/tool/labelbox) | [ModAstera](https://openaitools.app/tool/modastera) |
| :--- | :--- | :--- |
| Website URL | https://labelbox.com | https://www.modastera.com |
| Categories | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools | AI Developer Tools, AI Healthcare, AI Agent, AI Models |
| Pricing Model | Contact for Pricing | Contact for Pricing |
| Monthly Visits | 762,803 | 0 |
| MoM Growth Rate | -22.8% | -100% |
| Primary Market | United States | Global |
| Direct Visits | 370,024 | 0 |
| 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)

## ModAstera

ModAstera was built around a common observation: AI projects in healthcare and manufacturing rarely fail on modeling—they fail on data preparation, validation friction, and tooling that domain experts cannot operate. The company packages these stages into a single system so teams stop losing time to disconnected tools, hand-offs, and internal rebuilds.

Its flagship platform, MAEA, covers the full pipeline. Assisted pre-labeling and review workflows let experts correct AI-drafted labels instead of annotating every sample from scratch, while metadata and dataset lineage are captured before training starts. From there, teams define experiments, compare versions, run subgroup and drift checks, and manage compute in one workspace. Validation evidence, model cards, and deployment context stay connected, so moving an approved model to a secure endpoint does not require a separate DevOps project.

ModAstera is designed for operational environments where data quality, workflow speed, traceability, and deployment readiness all matter. Published customer stories describe a mobile screening app reaching users without a separate infrastructure build, a university team shortening an MRI brain-age prototype from a 90-day window to under a week, and regulatory teams screening document queues before manual review backs up.

**Pricing & Plans:** ModAstera uses a contact-for-pricing model. There are no published self-serve tiers or listed prices; teams are guided through a demo and quoted based on their workflow, deployment, and support needs.

- **Assisted labeling and data preparation:** Turn raw datasets into training-ready assets with assisted labeling, review loops, structured metadata, and captured dataset lineage before training begins.
- **Model training and experimentation:** Define experiments, compare model versions, track metrics, subgroup checks, and drift context, and manage compute in one place.
- **Traceable deployment handoff:** Package validated models with environment controls, model cards, audit trails, and deployment records that lead to secure production endpoints.
- **Healthcare and manufacturing solutions:** Purpose-built paths for clinical datasets and annotation as well as inspection, sensor, and operational data for quality and reliability.
- **No-code and full-code access:** Clinicians, researchers, and quality engineers can label, review, and validate without rebuilding infrastructure or operating complex ML tooling.

**Strengths:** Covers the whole AI delivery lifecycle in one traceable workflow rather than separate tools; Domain-expert-friendly labeling and review loops designed for clinicians and quality engineers; Strong traceability: lineage, evidence, model cards, and audit logs for regulated environments; Tailored solutions for both healthcare and manufacturing use cases

**Limitations:** No public self-serve pricing; you must book a demo to get a quote; Wide scope (data labeling through deployment) means onboarding can take real time; Best value in regulated healthcare/manufacturing; lighter general ML teams may find it heavier than needed

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

## Frequently Asked Questions

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

Labelbox has 762,803 monthly visits, while ModAstera 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%; ModAstera has -100%.

### 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. ModAstera: Contact for Pricing. ModAstera uses a contact-for-pricing model. There are no published self-serve tiers or listed prices; teams are guided through a demo and quoted based on their workflow, deployment, and support needs.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [n8n](https://openaitools.app/tool/n8n) | 4,522,449 | -7.4% | Freemium | Open-source workflow automation built for AI agents and technical teams | [.md](https://openaitools.app/tool/n8n.md) |
| 2 | [MindStudio](https://openaitools.app/tool/mindstudio) | 1,793,337 | +14.2% | Contact for Pricing | No-code builder for custom AI agents and workflows | [.md](https://openaitools.app/tool/mindstudio.md) |
| 3 | [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) |
| 4 | [Invisible Technologies](https://openaitools.app/tool/invisible-technologies-inc) | 1,364,520 | +64.4% | Contact for Pricing | Enterprise AI infrastructure where software and expert humans work together | [.md](https://openaitools.app/tool/invisible-technologies-inc.md) |
| 5 | [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) |
| 6 | [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) |
| 7 | [Taskade](https://openaitools.app/tool/taskade) | 539,714 | -9.4% | Freemium | Turn one prompt into live business apps, AI agents and automated workflows | [.md](https://openaitools.app/tool/taskade.md) |
| 8 | [SuperAnnotate](https://openaitools.app/tool/super-annotate) | 386,034 | +37.4% | Free Trial, Contact for Pricing | AI data infrastructure for building production-grade models | [.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 | [Activepieces](https://openaitools.app/tool/activepieces) | 258,553 | +1.5% | Freemium | Open-source AI automation that unifies agents, flows and 770+ app integrations | [.md](https://openaitools.app/tool/activepieces.md) |

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

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