# aiCode.fail vs Augment Code

aiCode.fail records 0 monthly visits with 0% month-over-month growth and Free Trial, Paid pricing. Augment Code records 326,215 monthly visits with +24.1% growth and Freemium, Free Trial, Contact for Pricing pricing.

| Metric | [aiCode.fail](https://openaitools.app/tool/aicode-fail) | [Augment Code](https://openaitools.app/tool/augment-code) |
| :--- | :--- | :--- |
| Website URL | https://aicode.fail | https://www.augmentcode.com |
| Categories | AI Checker, AI Code Review, AI Developer Tools, AI Testing | AI Agent, AI Code Assistant, AI Code Generator, AI Developer Tools |
| Pricing Model | Free Trial, Paid | Freemium, Free Trial, Contact for Pricing |
| Monthly Visits | 0 | 326,215 |
| MoM Growth Rate | 0% | +24.1% |
| Primary Market | Global | United States |
| Direct Visits | 0 | 137,070 |
| Review Score | 3.9 | 4.5 |
| Saved Count | 1 | 0 |
| Supported Platforms | Website | Website |

## aiCode.fail

aiCode.fail addresses a specific and familiar failure mode: AI coding assistants confidently produce code that subtly does not work. Because the model that wrote a snippet also tends to overlook its own mistakes, the tool takes a deliberately different approach. It analyses your code outside the context of the original chat, giving it a fresh perspective and a better chance of spotting contradictions, invented APIs or unsafe patterns.

The workflow centres on a browser-based Monaco editor. You paste or type the generated code, ask for an audit, and review the results, which are grouped around three themes: hallucination detection, security exposure and debugging support. The service states that it works with any programming language, is compatible with all major LLMs, and never compiles your code — the analysis is static, so nothing has to build or run before you get feedback. That makes it suitable as a quick checkpoint between generating code and committing it.

Pricing is straightforward. A free tier lets you try the checker with a limited number of audits. Paid plans remove that cap: an annual plan is advertised at roughly $5 per month with a 40% saving over the monthly option, and the month-to-month plan at roughly $9 per month, both including a 14-day trial and instant copy of results. The product appears on Product Hunt and is aimed squarely at developers who lean on AI assistants during sprints.

**Pricing & Plans:** Free tier with limited audits. Paid plans cost about $5/month billed annually (40% saving) or about $9/month month-to-month; both include a 14-day trial and unlimited audits.

- **Hallucination detection:** A dedicated model reviews generated code outside the original chat context, where assistants rarely spot their own mistakes.
- **Security issue exposure:** Audits flag security-relevant patterns such as unsafe handling of input, secrets or dependencies in the reviewed snippet.
- **Debugging acceleration:** Findings point to suspicious logic and likely faults so you can fix problems before they compound in later commits.
- **Works with all LLMs:** The checker is model-agnostic, so output from any AI assistant or coding agent can be audited in the same workspace.
- **Browser editor, no compilation:** You work in a familiar Monaco editor and your code is never compiled, keeping the audit lightweight and fast.

**Strengths:** Specifically targets AI-generated code rather than generic linting; Reviews code outside the original chat context, which helps surface mistakes assistants overlook; Works with any programming language and all major LLMs; Free tier available, with clearly listed paid tiers and a 14-day trial

**Limitations:** Static analysis cannot guarantee a program will run correctly end to end; Free tier restricts the number of audits, so heavier use requires a paid plan; It audits snippets in a browser editor rather than integrating deeply into a full project workflow

[Profile (Markdown)](https://openaitools.app/tool/aicode-fail.md)

## 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)

## Frequently Asked Questions

### How do aiCode.fail and Augment Code compare in monthly traffic?

aiCode.fail has 0 monthly visits, while Augment Code has 326,215. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

aiCode.fail has a month-over-month growth rate of 0%; Augment Code has +24.1%.

### How does pricing compare?

aiCode.fail: Free Trial, Paid. Free tier with limited audits. Paid plans cost about $5/month billed annually (40% saving) or about $9/month month-to-month; both include a 14-day trial and unlimited audits. 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.

## 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 | [Aikido Security](https://openaitools.app/tool/aikido-security) | 757,491 | +9.6% | Freemium | All-in-one AppSec platform that finds and auto-fixes vulnerabilities | [.md](https://openaitools.app/tool/aikido-security.md) |
| 10 | [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) |

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

More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)