# Augment Code vs ML.ai

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

| Metric | [Augment Code](https://openaitools.app/tool/augment-code) | [ML.ai](https://openaitools.app/tool/ml-ai) |
| :--- | :--- | :--- |
| Website URL | https://www.augmentcode.com | https://ml.ai |
| Categories | AI Agent, AI Code Assistant, AI Code Generator, AI Developer Tools | AI Code Assistant, AI Code Generator, AI Code Review, AI Developer Tools |
| Pricing Model | Freemium, Free Trial, Contact for Pricing | Paid |
| Monthly Visits | 326,215 | 0 |
| MoM Growth Rate | +24.1% | 0% |
| Primary Market | United States | Global |
| Direct Visits | 137,070 | 0 |
| Review Score | 4.5 | 4.4 |
| 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)

## ML.ai

ML.ai positions itself as the coding agent that never runs out - a direct jab at metered agents that stop mid-task when a token budget is exhausted. The product is a full agent, not a completion engine: you hand it an issue, it reads the relevant repository files, writes an explicit task plan, makes the edits, runs type checks, lint, unit and load tests, and opens a pull request. Nothing is written to disk until you allow it, and nothing merges until you approve it.

Two design choices stand out. First, a plan-and-approve guard: the agent declares which files it expects to change, you pick the tasks to run, and any edit that reaches outside the declared scope halts and asks first - asked once per turn rather than nagged continuously. Second, model routing: you set the outcome and an effort level (low, medium or high) and the agent routes each step to the model that fits it, rather than making you choose a vendor per request. In the vendor's own example, a five-step migration touches two models while staying inside a single task.

It runs anywhere you already work, from one account: a desktop app for macOS and Windows, IDE extensions for VS Code and JetBrains, and a CLI installable with a single curl script. Teams can launch many jobs in parallel across projects, each in its own sandbox, and end up with a set of pull requests to review. ML.ai publishes its own benchmark results (SWE-bench Verified 95.0%, SWE-bench Pro 92.6%, LiveCodeBench v6 83.4% on its flagship model) and cites enterprise customers, though as with all vendor benchmarks these should be treated as directional.

**Pricing & Plans:** Paid, per seat. Every plan includes the full agent and the differentiator is concurrency - how many jobs run in parallel - rather than a token budget. Individual plans target solo builders, while Team and Enterprise tiers add more parallel jobs; enterprise buyers can contact sales for a quote. Long-running jobs are not cut off partway by credit exhaustion.

- **End-to-end task completion:** Give it an issue and it reproduces the bug, edits the code, runs your tests and opens a pull request while you focus on decisions.
- **Plan-and-approve scope guard:** The agent proposes a task list and names the files it expects to change. You select the tasks, and edits outside the declared scope stop and ask first.
- **Model routing and effort levels:** Each step of a task is routed to the model that fits it, with the vendor abstracted behind an effort setting and a stated outcome.
- **Parallel sandboxed jobs:** Launch many jobs across projects at the same time. Each agent runs in its own sandbox and reports back with a reviewable diff.
- **App, IDE and CLI in one account:** The same account connects a desktop app for macOS and Windows, extensions for VS Code and JetBrains, and a terminal CLI via a single install script.

**Strengths:** Completes whole tasks including tests and PR creation; Scope guard halts edits outside declared files; Per-seat pricing removes mid-task budget stops; Same agent in desktop app, IDE and CLI

**Limitations:** Per-seat pricing is less flexible than pay-per-token for very occasional users; Claimed benchmark numbers come from the vendor's own evaluation runs; Full agent capability requires the desktop app, IDE extension or CLI rather than a browser-only workflow

[Profile (Markdown)](https://openaitools.app/tool/ml-ai.md)

## Frequently Asked Questions

### How do Augment Code and ML.ai compare in monthly traffic?

Augment Code has 326,215 monthly visits, while ML.ai has 0. 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%; ML.ai has 0%.

### 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. ML.ai: Paid. Paid, per seat. Every plan includes the full agent and the differentiator is concurrency - how many jobs run in parallel - rather than a token budget. Individual plans target solo builders, while Team and Enterprise tiers add more parallel jobs; enterprise buyers can contact sales for a quote. Long-running jobs are not cut off partway by credit exhaustion.

## 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 | The AI coding agent that turns ideas into shipped software | [.md](https://openaitools.app/tool/cursor-sh.md) |
| 4 | [Kiro](https://openaitools.app/tool/kiro) | 3,330,476 | +4.6% | Freemium | A spec-driven AI IDE and agent platform that plans, builds, reviews, and verifies real engineering work. | [.md](https://openaitools.app/tool/kiro.md) |
| 5 | [Devin](https://openaitools.app/tool/devin) | 2,549,346 | +1% | Paid | Cognition's autonomous software engineer that ships real pull requests | [.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 | The coding agent that learns your coding taste | [.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 | Run many coding agents in parallel, each in its own worktree | [.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 | An AI IDE and coding agent that works on your project, not just your cursor | [.md](https://openaitools.app/tool/trae.md) |
| 10 | [Macroscope](https://openaitools.app/tool/macroscope) | 106,170 | +39.6% | Free Trial, Paid, Contact for Pricing | AI code review and status updates that keep every repo on track | [.md](https://openaitools.app/tool/macroscope.md) |

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

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