# Labelbox vs super.AI

Labelbox records 762,803 monthly visits with -22.8% month-over-month growth and Contact for Pricing pricing. super.AI records 23,674 monthly visits with +7.4% growth and Freemium, Free Trial, Contact for Pricing pricing.

| Metric | [Labelbox](https://openaitools.app/tool/labelbox) | [super.AI](https://openaitools.app/tool/super-ai) |
| :--- | :--- | :--- |
| Website URL | https://labelbox.com | https://super.ai |
| Categories | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools | AI Document Extraction, AI OCR, AI Workflow, AI Agent |
| Pricing Model | Contact for Pricing | Freemium, Free Trial, Contact for Pricing |
| Monthly Visits | 762,803 | 23,674 |
| MoM Growth Rate | -22.8% | +7.4% |
| Primary Market | United States | Indonesia |
| Direct Visits | 370,024 | 9,537 |
| Review Score | 4.4 | 4.4 |
| 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)

## super.AI

super.AI targets the documents that break traditional OCR: mixed-language invoices, multi-page contract bundles, hand-corrected forms, low-quality scans and tables with merged cells. Instead of drawing a flowchart node by node, users describe the job conversationally. The agent selects the appropriate models, structures the output fields, and wires up triggers, filters and integrations, leaving every decision — model choice, prompt, confidence, routing logic — visible and editable on a visual canvas.

Workflows in super.AI are deterministic graphs rather than a single opaque model call. Flows can branch with if/else logic and loops, are versioned, and can be rolled back. An optional Human Review step pauses a run so assigned reviewers correct the extracted values before the flow continues with the reviewed data. The full execution history, including screenshots and inputs for every step, is retained as an audit trail that teams can export for compliance reviews.

Integration is built around REST APIs, webhooks, email and storage triggers (S3, Drive) with direct database write-back and ERP connections. Credentials are held in a KMS-backed encrypted vault scoped per organisation and per workflow, and MFA is supported during runs. Self-service and Growth accounts sit under self-service terms; the SOC 2 Type II report, trust-center access and contractual security commitments are Enterprise-only. super.AI is headquartered in San Francisco with a Berlin office, and offers a free, no-credit-card start along with a book-a-demo path for larger deployments.

**Pricing & Plans:** Free to start with no credit card: you can create an account and build your first workflow at no cost. Paid self-service plans (including a Growth tier) and Enterprise agreements are quoted on request — Enterprise adds the SOC 2 Type II report, trust-center access, contractual security commitments and options such as VPC or on-premise deployment. Contact sales for exact pricing.

- **Conversational workflow builder:** Describe the extraction job in plain language — 'skip anything under $50', 'add a tax ID field' — and the agent creates and updates a real flow with executors, schemas, routing logic and validators.
- **Inspectable agent decisions:** Every classification, extraction and routing call is visible on the canvas, including the model used, the prompt, the confidence score and the alternatives considered.
- **Human-in-the-loop review:** Insert a review step so assigned reviewers check and correct extracted values before the run continues; the flow resumes with the corrected data.
- **Deterministic, versioned flows:** Workflows are visual graphs you can branch, loop, version and roll back, with full step-by-step execution logs and exportable audit trails.
- **Broad integration surface:** Trigger flows from REST APIs, webhooks, email or storage, and deliver results by webhook, direct database write-back, S3/Drive or ERP integration.

**Strengths:** Plain-language workflow creation removes the need for an IT project or code; Fully inspectable agent reasoning — model, prompt, confidence and routing are all visible; Human Review steps let teams verify output before it reaches downstream systems; Strong audit, credentials and delivery story for regulated enterprise environments

**Limitations:** Exact pricing for paid and enterprise plans is not published and requires contacting sales; SOC 2 Type II reporting and contractual security commitments apply to Enterprise accounts only

[Profile (Markdown)](https://openaitools.app/tool/super-ai.md)

## Frequently Asked Questions

### How do Labelbox and super.AI compare in monthly traffic?

Labelbox has 762,803 monthly visits, while super.AI has 23,674. 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%; super.AI has +7.4%.

### 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. super.AI: Freemium, Free Trial, Contact for Pricing. Free to start with no credit card: you can create an account and build your first workflow at no cost. Paid self-service plans (including a Growth tier) and Enterprise agreements are quoted on request — Enterprise adds the SOC 2 Type II report, trust-center access, contractual security commitments and options such as VPC or on-premise deployment. Contact sales for exact pricing.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Zapier](https://openaitools.app/tool/zapier-com) | 5,065,248 | +0.4% | Freemium, Free Trial, Paid | Automate work across thousands of apps — no code required | [.md](https://openaitools.app/tool/zapier-com.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 | [Nanonets](https://openaitools.app/tool/nanonets) | 221,826 | -10.6% | Free Trial, Paid, Contact for Pricing | AI agents that run document-heavy business processes end to end | [.md](https://openaitools.app/tool/nanonets.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 | [V7 Go](https://openaitools.app/tool/v7-lab) | 116,919 | -11.6% | Free Trial, Contact for Pricing | AI workflow platform for document-heavy back-office operations | [.md](https://openaitools.app/tool/v7-lab.md) |

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Source: https://openaitools.app/compare/labelbox-vs-super-ai

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