# Autify vs Labelbox

Autify records 60,130 monthly visits with +6% month-over-month growth and Contact for Pricing pricing. Labelbox records 762,803 monthly visits with -22.8% growth and Contact for Pricing pricing.

| Metric | [Autify](https://openaitools.app/tool/autify-com) | [Labelbox](https://openaitools.app/tool/labelbox) |
| :--- | :--- | :--- |
| Website URL | https://autify.com | https://labelbox.com |
| Categories | AI Testing, AI Code Generator, AI Agent, No-Code&Low-Code | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools |
| Pricing Model | Contact for Pricing | Contact for Pricing |
| Monthly Visits | 60,130 | 762,803 |
| MoM Growth Rate | +6% | -22.8% |
| Primary Market | Japan | United States |
| Direct Visits | 21,141 | 370,024 |
| Review Score | 4.2 | 4.4 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## Autify

Autify is an AI platform built to accelerate and simplify the entire software testing lifecycle. According to its site, the company offers a family of products that pair test automation with generative AI: Aximo, an autonomous AI testing agent that uses natural language and visual recognition to generate and run end-to-end tests across web, mobile, and desktop apps; Autify Genesis, a GenAI-driven test design tool that turns requirements and source code into structured test cases, features, and viewpoints; and no-code test automation for web and mobile, built on Playwright for flexible, scalable coverage.

The platform emphasizes self-healing and flexible locators so that tests keep working as applications evolve, and it positions humans and AI as partners in the testing process. Core use cases include end-to-end testing, automated regression testing, and converting manual testing into automated coverage, with support for difficult targets such as Salesforce and Canvas/WebGL applications, plus desktop and mobile app testing.

Autify publishes customer success stories that stress speed and scalability, including reported reductions in test authoring time and teams expanding test coverage as they adopt the platform. It is positioned for QA and engineering organizations that want to cut maintenance overhead and keep pace with frequent releases. Pricing is quote-based, so teams need to contact sales for details.

**Pricing & Plans:** Pricing is quote-based; Autify asks interested teams to contact sales for a tailored plan. No public per-seat or per-credit pricing is listed on the main site.

- **Aximo AI testing agent:** An autonomous agent that uses natural language and visual recognition to generate and execute end-to-end tests across web, mobile, and desktop apps.
- **Autify Genesis:** Generative-AI test design that moves from product requirements and source code to structured features, testing viewpoints, and detailed test cases.
- **No-code mobile and web automation:** Build and maintain automated tests without scripting, using flexible, self-healing locators that adapt as the application changes.
- **Broad application coverage:** Support for end-to-end, regression, smoke, functional, and visual testing, including tricky targets like Salesforce and Canvas/WebGL apps.
- **AI-human testing partnership:** A workflow designed to keep humans in control while AI speeds up test creation, execution, and maintenance.

**Strengths:** Covers the full testing lifecycle, from test design to execution and maintenance, in one platform; AI agent plus GenAI test design reduces manual scripting and authoring effort; Self-healing, flexible locators lower ongoing test maintenance costs; Handles challenging targets such as Salesforce and Canvas/WebGL applications

**Limitations:** Pricing is quote-only, so costs are not transparent without contacting sales; The product family (Aximo, Genesis, NoCode) is broad and may take time to adopt fully; Best value comes from committing to its ecosystem rather than mixing many separate tools

[Profile (Markdown)](https://openaitools.app/tool/autify-com.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 Autify and Labelbox compare in monthly traffic?

Autify has 60,130 monthly visits, while Labelbox has 762,803. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Autify has a month-over-month growth rate of +6%; Labelbox has -22.8%.

### How does pricing compare?

Autify: Contact for Pricing. Pricing is quote-based; Autify asks interested teams to contact sales for a tailored plan. No public per-seat or per-credit pricing is listed on the main site. 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 | [KaneAI](https://openaitools.app/tool/kaneai) | 901,340 | +13.2% | Freemium, Paid, Contact for Pricing | GenAI-native end-to-end QA agent that plans, writes and heals tests in plain language | [.md](https://openaitools.app/tool/kaneai.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 | [Katalon](https://openaitools.app/tool/katalon-studio) | 158,090 | -29.6% | Freemium, Free Trial, Paid, Contact for Pricing | AI-augmented quality platform for web, mobile, API and desktop testing | [.md](https://openaitools.app/tool/katalon-studio.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 | [TestSprite](https://openaitools.app/tool/testsprite) | 114,205 | +15.8% | Freemium | Agentic end-to-end testing that writes, runs and heals your test suite | [.md](https://openaitools.app/tool/testsprite.md) |

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Source: https://openaitools.app/compare/autify-com-vs-labelbox

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