# Devin vs Scale

Devin records 2,549,346 monthly visits with +1% month-over-month growth and Paid pricing. Scale records 616,915 monthly visits with +12.5% growth and Contact for Pricing pricing.

| Metric | [Devin](https://openaitools.app/tool/devin) | [Scale](https://openaitools.app/tool/scale) |
| :--- | :--- | :--- |
| Website URL | https://devin.ai | https://scale.com |
| Categories | AI Code Assistant, AI Agent, AI Developer Tools | AI Developer Tools, Large Language Models (LLMs), AI Models, AI Agent |
| Pricing Model | Paid | Contact for Pricing |
| Monthly Visits | 2,549,346 | 616,915 |
| MoM Growth Rate | +1% | +12.5% |
| Primary Market | United States | United States |
| Direct Visits | 1,386,401 | 191,408 |
| Review Score | 4.3 | 4.7 |
| Saved Count | 0 | 3 |
| 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)

## Scale

Scale positions itself as the infrastructure layer for reliable AI - the data, evaluations and deployment support that sit underneath working systems rather than a single consumer-facing model. Founded in 2016, it built its reputation supplying training data and labelling capacity to the autonomous vehicle and then the generative AI wave, and now describes itself as powering a large share of the world's leading generative AI model builders.

The offering spans three broad areas. Data covers sourcing and expert annotation for training and fine-tuning, including RLHF-style preference data. Evaluation covers public leaderboards and private benchmarks used to measure model capability and safety. Applications covers the build-and-deploy side: identifying a viable use case, constructing the system and owning the outcome, with agentic AI for enterprise operations.

Scale's customer list leans heavily enterprise and public sector, including large technology companies, healthcare providers, media organisations, energy companies and government defence programmes. Because engagements are scoped rather than self-serve, pricing is arranged through a sales process, and the platform is aimed at organisations with serious data volume, strict compliance needs and a mandate to put AI into production.

**Pricing & Plans:** Contact for pricing. Scale does not publish rates; engagements are scoped through a sales conversation and depend on data volume, services and programme complexity.

- **Data Engine:** Human-sourced and expert-annotated training data, including specialist contributors with advanced degrees, at the quality bar frontier labs require.
- **Model evaluation & benchmarks:** Public leaderboards plus private benchmarks that test model capability and safety for the most ambitious AI developers.
- **Enterprise AI applications:** Platform and services teams that pick the right use case, build the system and take ownership of the outcome.
- **Agentic deployment:** Agentic systems designed around measurable business outcomes across private equity portfolios, energy and healthcare operations.
- **Domain expertise:** Programmes spanning healthcare, robotics, defence, public sector and enterprise with human oversight built in.

**Strengths:** Backed by a long track record in training data and model evaluation.; End-to-end coverage from data sourcing through to deployed systems.; Experienced with regulated, classified and safety-critical environments.; Benchmarks and leaderboards give an independent view of model capability.

**Limitations:** No public pricing or self-serve signup - you need a sales conversation to get started.; Enterprise-scale contracting and data operations require real internal commitment.; Overkill for small teams with modest data or evaluation needs.; Some projects involve sensitive government and defence work with strict compliance overhead.

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

## Frequently Asked Questions

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

Devin has 2,549,346 monthly visits, while Scale has 616,915. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Devin has a month-over-month growth rate of +1%; Scale has +12.5%.

### 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. Scale: Contact for Pricing. Contact for pricing. Scale does not publish rates; engagements are scoped through a sales conversation and depend on data volume, services and programme complexity.

## 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 | Streamlines computer vision dataset annotation and model training, but is less suited for general LLM text tasks. | [.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 | Focuses heavily on enterprise data labeling and training data curation, similar to Scale's core annotation business. | [.md](https://openaitools.app/tool/labelbox.md) |

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

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