# Augment Code vs Clean

Augment Code records 326,215 monthly visits with +24.1% month-over-month growth and Freemium, Free Trial, Contact for Pricing pricing. Clean records 25,981 monthly visits with -44.9% growth and Contact for Pricing pricing.

| Metric | [Augment Code](https://openaitools.app/tool/augment-code) | [Clean](https://openaitools.app/tool/clean) |
| :--- | :--- | :--- |
| Website URL | https://www.augmentcode.com | https://tryclean.ai |
| Categories | AI Agent, AI Code Assistant, AI Code Generator, AI Developer Tools | AI Code Assistant, AI Agent, AI Developer Tools, AI Copilot |
| Pricing Model | Freemium, Free Trial, Contact for Pricing | Contact for Pricing |
| Monthly Visits | 326,215 | 25,981 |
| MoM Growth Rate | +24.1% | -44.9% |
| Primary Market | United States | United States |
| Direct Visits | 137,070 | 18,763 |
| Review Score | 4.5 | 4.1 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## Augment Code

Augment Code is an AI coding platform aimed at engineering organisations rather than individual autocomplete users. Its foundation is the Context Engine, which builds a structured understanding of an existing repository — dependencies, architecture, conventions — so that AI suggestions and generated changes fit the codebase instead of reading like generic boilerplate. The product works inside common IDEs and also in the terminal via a CLI.

The platform's recent focus is Cosmos, an agent orchestration layer for the software development lifecycle. Instead of a single assistant answering prompts, Cosmos runs connected loops: a ticket becomes a reviewed pull request, every new PR gets a first-pass code review, failed CI runs get diagnosed, Dependabot and Snyk alerts get triaged into prioritised issues, and on-call engineers inherit a completed investigation. Each loop hands its outcome to the next, and quality gates pause work for human review rather than shipping automatically.

Agents can be deployed in managed cloud sandboxes, on your own machines for code that cannot leave the building, or a mix of both. Native triggers cover GitHub, GitLab, Slack, Linear, Jira, schedules and typed webhooks, with integrations for CI systems. There is a free tier for individuals to try the assistant, while team and enterprise adoption is quote-based; the company has announced it is joining Harness to push the autonomous SDLC from idea to production.

**Pricing & Plans:** Freemium with a free tier for individual developers to start, plus paid team plans and enterprise/contact-for-pricing options. Enterprise deployment includes self-hosted and hybrid agent execution; exact seat and usage pricing is quoted by sales.

- **Context Engine codebase understanding:** Indexes architecture, dependencies and conventions across large repositories so generated code and reviews match how your team actually writes software.
- **Cosmos agent orchestration:** Runs connected SDLC loops — code review, ticket-to-PR, incident triage, dependency upgrades and security remediation — where each agent hands its result to the next.
- **Human-in-the-loop quality gates:** Work pauses at defined checkpoints so engineers approve plans and consequential actions instead of letting agents ship unreviewed changes.
- **Deploy anywhere:** Execute agents in managed cloud sandboxes, on local machines and dev VMs for sensitive code, or in your own AWS and GCP environments.
- **Expert registry and shared memory:** Choose from shipped agents, fork them, or build your own; corrections and shared knowledge improve future runs for the whole team.

**Strengths:** Deep codebase context means suggestions fit existing architecture and conventions; Loops connect across the SDLC instead of solving one isolated task; Quality gates keep humans in control of consequential changes; Flexible deployment including local and self-hosted execution for sensitive code

**Limitations:** Enterprise pricing is quote-based, which makes budgeting harder before a trial; The full value depends on wiring up integrations and triggers across your toolchain; Small teams and solo developers may find the orchestration layer heavier than needed

[Profile (Markdown)](https://openaitools.app/tool/augment-code.md)

## Clean

Clean positions itself as the opposite of a static lead list. Instead of exporting thousands of stale rows filtered by industry and headcount, it starts from what companies actually do: a funded building loan, a closed permit, an incoming shipment of machinery. Each of those records is dated, sourced and linked, and Clean reads them together until they add up to a reason to reach out now. The product then points to the person who owns that decision, with email and phone verified.

The platform is designed to work at two levels. Human sellers get a prospects workspace where an account's signals, employers, warm paths and campaign state sit side by side. At the same time, Clean exposes itself as tooling — an API and an MCP server — so agents in Claude, Codex or a homegrown stack can pull the same signals and launch campaigns without a human copying data between systems. Replies route back to the same place either way.

Clean leans heavily into verticals where physical-world events precede purchases: manufacturing and industrial software, healthcare, legal tech, fintech, insurance and developer infrastructure. Access is currently sales-led: the site funnels visitors to a demo request rather than listing public self-serve pricing, and it advertises traction among Y Combinator-backed startups.

**Pricing & Plans:** No public self-serve pricing tiers are listed; the site routes visitors to a "Request a demo" flow, suggesting quote-based, sales-assisted plans. Verify current terms directly with the vendor.

- **1,000+ live signal sources:** Continuous monitoring of permits, equipment shipments, filings, press releases, customs records and hiring posts, with every record dated and attributed.
- **Reason-to-buy synthesis:** Clean groups separate records at one company and reads them in date order to produce a single, explainable reason why the account is worth contacting now.
- **Verified decision-maker contacts:** For each qualified company, Clean names the person who owns the decision — for example a plant manager or VP of Operations — with email and phone verified.
- **Agent-ready API and MCP:** Hand prospects and campaign triggers to agents in Claude or Codex, or wire the API and MCP server into an internal stack, with replies returning to one place.
- **Company context and warm paths:** Open any prospect to see supporting evidence, the contact window, and colleagues who can introduce you.

**Strengths:** Signals come with dated, checkable evidence rather than a single opaque score; Purpose-built for agent workflows through API and MCP, not just a dashboard; Strong fit for industries with physical or regulatory events before a purchase; Combines company signals with verified decision-maker contact data

**Limitations:** No public pricing published, so evaluation requires a sales conversation; Effectiveness depends on signal coverage in your specific industry and geography; Requires connected inbox and CRM data to show full thread history

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

## Frequently Asked Questions

### How do Augment Code and Clean compare in monthly traffic?

Augment Code has 326,215 monthly visits, while Clean has 25,981. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Augment Code has a month-over-month growth rate of +24.1%; Clean has -44.9%.

### How does pricing compare?

Augment Code: Freemium, Free Trial, Contact for Pricing. Freemium with a free tier for individual developers to start, plus paid team plans and enterprise/contact-for-pricing options. Enterprise deployment includes self-hosted and hybrid agent execution; exact seat and usage pricing is quoted by sales. Clean: Contact for Pricing. No public self-serve pricing tiers are listed; the site routes visitors to a "Request a demo" flow, suggesting quote-based, sales-assisted plans. Verify current terms directly with the vendor.

## 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 | The AI coding agent for building ambitious software | [.md](https://openaitools.app/tool/cursor.md) |
| 2 | [Google Antigravity](https://openaitools.app/tool/google-antigravity) | 16,054,109 | +8.1% | Freemium | Google's agent-first development platform for building and shipping software with AI agents | [.md](https://openaitools.app/tool/google-antigravity.md) |
| 3 | [Replit](https://openaitools.app/tool/replit) | 10,016,437 | -7.7% | Freemium, Paid | Streamlines cloud-based full-stack app building and deployment from natural language, but less tailored for large legacy enterprise repos. | [.md](https://openaitools.app/tool/replit.md) |
| 4 | [Cursor](https://openaitools.app/tool/cursor-sh) | 4,790,129 | +6.5% | Freemium | Integrates directly into VS Code as a daily coding editor, but lacks enterprise-wide multi-agent SDLC orchestration. | [.md](https://openaitools.app/tool/cursor-sh.md) |
| 5 | [Apify](https://openaitools.app/tool/apify) | 4,147,762 | +3.9% | Freemium, Contact for Pricing | Run thousands of ready-made web scrapers and AI agents from one cloud platform | [.md](https://openaitools.app/tool/apify.md) |
| 6 | [Kiro](https://openaitools.app/tool/kiro) | 3,330,476 | +4.6% | Freemium | Focuses on spec-driven engineering and automated verification from AWS, whereas Augment targets broader multi-agent SDLC automation. | [.md](https://openaitools.app/tool/kiro.md) |
| 7 | [Devin](https://openaitools.app/tool/devin) | 2,549,346 | +1% | Paid | Operates as a fully autonomous software engineer tackling complex tickets independently, whereas Augment focuses on developer-in-the-loop workflows. | [.md](https://openaitools.app/tool/devin.md) |
| 8 | [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) |
| 9 | [Attio](https://openaitools.app/tool/attio) | 1,652,928 | -4.2% | Free Trial, Contact for Pricing | The AI-native CRM for agentic revenue teams | [.md](https://openaitools.app/tool/attio.md) |
| 10 | [Clay](https://openaitools.app/tool/clay-2-0) | 1,267,465 | -2.7% | Freemium, Free Trial, Contact for Pricing | Build GTM systems that turn data into revenue | [.md](https://openaitools.app/tool/clay-2-0.md) |

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Source: https://openaitools.app/compare/augment-code-vs-clean

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