# Devin vs Ultralytics

Devin records 2,549,346 monthly visits with +1% month-over-month growth and Paid pricing. Ultralytics records 793,423 monthly visits with +8.7% growth and Freemium pricing.

| Metric | [Devin](https://openaitools.app/tool/devin) | [Ultralytics](https://openaitools.app/tool/ultralytics) |
| :--- | :--- | :--- |
| Website URL | https://devin.ai | https://www.ultralytics.com |
| Categories | AI Code Assistant, AI Agent, AI Developer Tools | AI Developer Tools, AI Image Recognition, AI Models, No-Code&Low-Code |
| Pricing Model | Paid | Freemium |
| Monthly Visits | 2,549,346 | 793,423 |
| MoM Growth Rate | +1% | +8.7% |
| Primary Market | United States | China |
| Direct Visits | 1,386,401 | 239,747 |
| Review Score | 4.3 | 4.6 |
| Saved Count | 0 | 0 |
| Supported Platforms | website | Website |

## Devin

Devin sits at the agentic end of the AI coding spectrum. Instead of suggesting the next line, it takes a ticket-level goal - "add SAML SSO for enterprise customers" or "investigate this API latency spike" - explores the codebase across repositories, implements changes, runs tests, and explains the resulting pull request in plain language. It maintains a wiki-style map of the codebase so it can navigate unfamiliar services and keep documentation current as the code changes.

A defining feature is that Devin works on its own machine. It provisions Windows, Mac, and Linux virtual machines as needed, builds the project, exercises it in a browser, and can record itself clicking through the feature it just built. Long-running work continues in the cloud when your laptop is closed, and large tasks can be split across parallel agents that Devin coordinates and reports on.

For teams, Devin extends beyond one-off prompts. You can tag @Devin in Slack or Teams so it reads a thread and replies with a PR, set up automations that fix failing tests or patch vulnerable dependencies as they appear, and run security reviews. Pricing is seat-based and enterprise-oriented, with an Ultra tier for heavier autonomous usage, and Cognition has marketed large security-credit programs for enterprises.

**Pricing & Plans:** Paid, enterprise-oriented. Devin is sold on a per-seat subscription with tiered plans (including higher-capacity tiers for heavier autonomous use) plus usage allowances; Cognition also runs credit programs and enterprise agreements. Because plans and credit packages change, check Cognition's official pricing page for current numbers.

- **End-to-end autonomous coding:** Devin plans, writes code, runs tests, and opens pull requests across complex, multi-repository codebases without waiting for line-by-line approval.
- **Its own virtual machines:** Devin provisions Windows, Mac, and Linux VMs, builds and runs the project, and tests in a real browser - even recording itself using the feature it built.
- **Slack, Teams, and alert integration:** Mention @Devin in a thread to delegate a fix, or connect CI, PagerDuty, and issue trackers so it reacts to failures and alerts automatically.
- **Parallel cloud agents:** Delegate a large task and Devin splits it across parallel cloud agents that keep working after you close your laptop, then reports back.
- **Codebase wiki and automations:** Devin maintains a living map of the architecture and supports recurring automations such as dependency upgrades, test fixes, and security scans.

**Strengths:** Handles ticket-level autonomous work rather than only suggesting code completions; Tests in its own browser and VMs, which catches issues that pure code generation misses; Deep workflow integration with Slack, Teams, CI, and alerting tools; Keeps working in parallel cloud agents even when your machine is off

**Limitations:** Premium, seat-based pricing puts it out of reach for casual individual use; Complex or poorly specified tasks still need human review before merging; Maximum value depends on having a well-structured repo and clean test setup

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

## Ultralytics

Ultralytics is best known as the maintainer of the YOLO model family, one of the most widely deployed families of real-time computer vision models. The same codebase spans seven vision tasks — detection, instance segmentation, pose estimation, oriented bounding boxes, classification, tracking, and more — so teams can move from a quick prototype to production inference without switching frameworks. Models export to a long list of runtimes including ONNX, TensorRT, CoreML, and other formats for edge and embedded targets.

Beyond the models, Ultralytics offers a hosted Platform that handles the operational side of vision projects. You can label datasets with SAM-powered assisted annotation, fine-tune or train models on cloud GPUs (including AutoTrain), and deploy to managed regions that scale to zero when idle. An agent-style workflow builder lets teams chain models, logic, and alerting into pipelines, which suits industrial inspection, safety monitoring, and analytics products.

Licensing is dual-track. The open-source path is released under AGPL-3.0, which is fine for open projects but imposes copyleft obligations on closed-source products. Companies shipping YOLO inside proprietary software typically purchase an enterprise license instead, which removes those obligations and adds on-premise or private-cloud deployment, broad export format support, and direct engineering support. Ultralytics also publishes extensive documentation and tutorials, which is a large part of why the ecosystem is so widely adopted by students, researchers, and commercial integrators alike.

**Pricing & Plans:** Freemium. The YOLO models and tooling are available open source under AGPL-3.0 at no cost; the Ultralytics Platform offers free and paid cloud tiers, and commercial use inside closed-source products requires a paid enterprise license with custom support terms.

- **YOLO real-time vision models:** One model family covering detection, instance segmentation, pose estimation, oriented boxes, classification, and tracking, tuned for real-time inference on GPU, CPU, and edge devices.
- **Ultralytics Platform for the full lifecycle:** Label data with SAM-assisted annotation, train on cloud GPUs with AutoTrain, and deploy to managed regions that scale to zero when idle.
- **Broad export and runtime support:** Ship trained models to ONNX, TensorRT, CoreML, TFLite, and many other formats for cloud servers, mobile devices, and embedded hardware.
- **Dual licensing for open and commercial use:** Adopt the open-source AGPL-3.0 release, or purchase an enterprise license to embed YOLO in closed-source products without copyleft obligations.
- **Workflow and agent builder:** Chain vision models, business logic, and Slack-style alerts into automated pipelines for monitoring, inspection, and analytics.

**Strengths:** Mature, heavily documented model family with millions of daily inference usages and a large open-source community; Covers the whole lifecycle — annotation, training, deployment — so teams avoid stitching together separate vendors; Wide export format support makes it practical to target cloud, mobile, and embedded hardware from one codebase; Enterprise licensing removes AGPL obligations for closed-source commercial products

**Limitations:** AGPL-3.0 on the open-source release is a blocker for many commercial closed-source deployments without a paid license; Achieving top accuracy still requires careful dataset curation and hyperparameter tuning rather than one-click results; Heavy dependence on the hosted Platform for managed features can create lock-in for teams that later want to self-host

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

## Frequently Asked Questions

### How do Devin and Ultralytics compare in monthly traffic?

Devin has 2,549,346 monthly visits, while Ultralytics has 793,423. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Devin has a month-over-month growth rate of +1%; Ultralytics has +8.7%.

### How does pricing compare?

Devin: Paid. Paid, enterprise-oriented. Devin is sold on a per-seat subscription with tiered plans (including higher-capacity tiers for heavier autonomous use) plus usage allowances; Cognition also runs credit programs and enterprise agreements. Because plans and credit packages change, check Cognition's official pricing page for current numbers. Ultralytics: Freemium. Freemium. The YOLO models and tooling are available open source under AGPL-3.0 at no cost; the Ultralytics Platform offers free and paid cloud tiers, and commercial use inside closed-source products requires a paid enterprise license with custom support terms.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Cursor](https://openaitools.app/tool/cursor) | 23,297,751 | +8.4% | Freemium | An AI-native code editor providing advanced multi-file code generation and editing, though less fully autonomous end-to-end than Devin. | [.md](https://openaitools.app/tool/cursor.md) |
| 2 | [Manus](https://openaitools.app/tool/manus) | 19,195,779 | -1.7% | Free Trial | A general autonomous agent platform executing multi-step goals and browsers, suitable for broader web and coding workflows. | [.md](https://openaitools.app/tool/manus.md) |
| 3 | [Google Antigravity](https://openaitools.app/tool/google-antigravity) | 16,054,109 | +8.1% | Freemium | Google's agentic development platform running parallel AI coding agents across an IDE and CLI workspace. | [.md](https://openaitools.app/tool/google-antigravity.md) |
| 4 | [Replit](https://openaitools.app/tool/replit) | 10,016,437 | -7.7% | Freemium, Paid | A cloud development platform featuring an AI agent that builds, runs, and deploys full-stack apps automatically. | [.md](https://openaitools.app/tool/replit.md) |
| 5 | [Qoder](https://openaitools.app/tool/qoder) | 5,145,040 | +81.8% | Freemium, Free Trial, Free | An agentic coding platform and IDE plugin that reads entire codebases to execute complex multi-file tasks. | [.md](https://openaitools.app/tool/qoder.md) |
| 6 | [Kiro](https://openaitools.app/tool/kiro) | 3,330,476 | +4.6% | Freemium | An AWS spec-driven AI IDE and agent platform that plans, builds, reviews, and verifies software engineering work. | [.md](https://openaitools.app/tool/kiro.md) |
| 7 | [Command Code](https://openaitools.app/tool/command-code) | 2,358,177 | -12.1% | Freemium, Contact for Pricing | The coding agent that learns your coding taste | [.md](https://openaitools.app/tool/command-code.md) |
| 8 | [Roboflow](https://openaitools.app/tool/roboflow) | 1,431,204 | +6.6% | Freemium, Contact for Pricing | Provides an all-in-one ecosystem for dataset annotation, model training, and edge deployment. | [.md](https://openaitools.app/tool/roboflow.md) |
| 9 | [Orca](https://openaitools.app/tool/orca) | 1,025,891 | +1% | Free, Open Source, Enterprise on request | Run many coding agents in parallel, each in its own worktree | [.md](https://openaitools.app/tool/orca.md) |
| 10 | [Labelbox](https://openaitools.app/tool/labelbox) | 762,803 | -22.8% | Contact for Pricing | Enterprise-grade labeling and data curation platform for complex computer vision training pipelines. | [.md](https://openaitools.app/tool/labelbox.md) |

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Source: https://openaitools.app/compare/devin-vs-ultralytics

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