# Augment Code vs Devzery

Augment Code records 326,215 monthly visits with +24.1% month-over-month growth and Freemium, Free Trial, Contact for Pricing pricing. Devzery records 2,378 monthly visits with -85.1% growth and Contact for Pricing pricing.

| Metric | [Augment Code](https://openaitools.app/tool/augment-code) | [Devzery](https://openaitools.app/tool/devzery) |
| :--- | :--- | :--- |
| Website URL | https://www.augmentcode.com | https://www.devzery.com |
| Categories | AI Agent, AI Code Assistant, AI Code Generator, AI Developer Tools | AI Testing, AI API, AI Agent, AI Developer Tools |
| Pricing Model | Freemium, Free Trial, Contact for Pricing | Contact for Pricing |
| Monthly Visits | 326,215 | 2,378 |
| MoM Growth Rate | +24.1% | -85.1% |
| Primary Market | United States | United States |
| Direct Visits | 137,070 | 945 |
| Review Score | 4.5 | 4.2 |
| Saved Count | 0 | 1 |
| 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)

## Devzery

Devzery is an AI-powered API regression testing platform for QA and engineering teams. Its core is a self-driving AI agent that runs end-to-end regression tests at the API level, verifying user-level functionality, validating integrations and tracking API changes in real time. The goal is to catch regressions earlier in the SDLC and shrink the time spent on manual test authoring.

The platform generates context-rich test suites from a PRD or project details, adapts tests automatically as APIs change, and produces AI-written bug reports with status, related test cases, expected versus actual results and error descriptions. It also generates Swagger documentation for APIs automatically, removing a common manual chore.

Devzery integrates with project management and CI/CD tools such as Jira, GitHub Actions and Jenkins so regression suites can be triggered on every merge or deployment. A middleware SDK with Node, Java, Python and Golang support logs API requests and responses for continuous regression coverage, and Devzery also publishes an open-source UI test case generator on GitHub.

**Pricing & Plans:** Devzery does not publish public list prices; pricing is available on request (the product site currently invites users to join a waitlist). Contact the Devzery team for plan details and a demo.

- **Self-driving API regression agent:** An AI agent runs end-to-end functional regression tests at the API level and tracks API changes in real time.
- **AI-generated test suites:** Devzery analyzes your PRD or project details to generate context-rich API test cases, including edge-case scenarios for broader coverage.
- **Automatic Swagger documentation:** AI analyses complex data structures and generates accurate, up-to-date Swagger schema definitions for your APIs.
- **CI/CD and project management integrations:** Plug into Jira, GitHub Actions and Jenkins to trigger regression suites automatically on merges and deployments.
- **Middleware SDK logging:** Middleware SDKs for Node, Java, Python and Golang log API requests and responses for continuous regression coverage.

**Strengths:** Automates API-level regression testing end to end; Generates test cases from PRDs and product context; Native integrations with Jira, GitHub Actions and Jenkins; Open-source UI test case generator available

**Limitations:** No public pricing; you must contact the team; Product is early-stage with a waitlist for new users; Some marketing claims (2x/3x multipliers) are vendor-reported

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

## Frequently Asked Questions

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

Augment Code has 326,215 monthly visits, while Devzery has 2,378. 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%; Devzery has -85.1%.

### 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. Devzery: Contact for Pricing. Devzery does not publish public list prices; pricing is available on request (the product site currently invites users to join a waitlist). Contact the Devzery team for plan details and a demo.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Google Antigravity](https://openaitools.app/tool/google-antigravity) | 16,054,109 | +8.1% | Freemium | Offers powerful parallel agentic workflows and dedicated IDE tools, but with less focus on proprietary enterprise codebase context. | [.md](https://openaitools.app/tool/google-antigravity.md) |
| 2 | [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) |
| 3 | [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) |
| 4 | [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) |
| 5 | [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) |
| 6 | [Command Code](https://openaitools.app/tool/command-code) | 2,358,177 | -12.1% | Freemium, Contact for Pricing | Delivers a terminal-first workflow optimized for open-weight models, but offers less graphical enterprise management tooling. | [.md](https://openaitools.app/tool/command-code.md) |
| 7 | [Orca](https://openaitools.app/tool/orca) | 1,025,891 | +1% | Free, Open Source, Enterprise on request | Provides an open-source agent development environment running parallel CLI coding agents, but requires manual configuration and setup. | [.md](https://openaitools.app/tool/orca.md) |
| 8 | [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) |
| 9 | [Trae](https://openaitools.app/tool/trae) | 702,366 | -7.8% | Freemium | Provides a fast AI-native IDE experience with deep project understanding, but lacks advanced enterprise context engines. | [.md](https://openaitools.app/tool/trae.md) |
| 10 | [Qase](https://openaitools.app/tool/qase) | 319,213 | -25.5% | Freemium, Free Trial, Contact for Pricing, Free | Ship with confidence: one system of record for manual, automated, and CI tests | [.md](https://openaitools.app/tool/qase.md) |

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

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