# ML.ai vs Trae

ML.ai records 0 monthly visits with 0% month-over-month growth and Paid pricing. Trae records 702,366 monthly visits with -7.8% growth and Freemium pricing.

| Metric | [ML.ai](https://openaitools.app/tool/ml-ai) | [Trae](https://openaitools.app/tool/trae) |
| :--- | :--- | :--- |
| Website URL | https://ml.ai | https://www.trae.ai |
| Categories | AI Code Assistant, AI Code Generator, AI Code Review, AI Developer Tools | AI Code Assistant, AI Code Generator, AI Agent, AI Developer Tools |
| Pricing Model | Paid | Freemium |
| Monthly Visits | 0 | 702,366 |
| MoM Growth Rate | 0% | -7.8% |
| Primary Market | Global | China |
| Direct Visits | 0 | 373,236 |
| Review Score | 4.4 | 4.7 |
| Saved Count | 0 | 6 |
| Supported Platforms | Website | Website |

## 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)

## Trae

Trae is an AI development platform that puts a coding agent at the centre of the editing experience. Its main product, TraeCode, is a full IDE with an integrated AI panel: instead of merely completing the next line, the assistant can inspect your project, reason about the change you want, edit several files at once, and run commands to verify the result. The diff-based review flow keeps you in charge of what is applied.

The stated positioning is "Collaborate with Intelligence" — the assistant works alongside you rather than replacing the editor. Practical uses include generating boilerplate and new modules, refactoring messy code, explaining an unfamiliar repository, fixing failing tests, and translating a plain-language task into working changes. Because the agent has project context, results tend to be more coherent across a codebase than single-file completion.

Alongside TraeCode, TraeWork applies the same assistant model to general work tasks, so the platform covers both engineering and everyday productivity. Trae ships as a desktop download and can also be used in the browser, making it easy to adopt without disrupting a whole toolchain.

**Pricing & Plans:** Free to start, with paid upgrades that increase usage limits for premium models; enterprises can purchase through BytePlus. Usage is measured per AI request and varies by model, so heavy users should review the pricing page for current allowances before committing.

- **Project-aware code context:** Trae reads your project files and dependencies, so its suggestions account for how the codebase is actually structured rather than treating each file in isolation.
- **Agentic code generation and edits:** Describe a task and the agent plans and applies multi-file edits, shows a diff for review, and can run terminal commands as part of the change.
- **Debugging and code explanation:** Ask questions about unfamiliar code, get inline explanations, and have bugs or failing tests diagnosed and fixed with context.
- **TraeCode plus TraeWork:** TraeCode covers the coding workflow, while TraeWork applies an AI assistant to everyday knowledge tasks — both download for desktop, with a web option.
- **Free start with upgradable limits:** You can try it for free and scale up by choosing the plan that raises your usage limits, with enterprise options available.

**Strengths:** Free tier makes it easy to evaluate before paying; Agentic workflow handles multi-file changes and tests, not just autocomplete; Deep project context improves the relevance of suggestions; Covers both coding (TraeCode) and general work (TraeWork)

**Limitations:** Heavy usage of premium models quickly hits plan limits; It is a newer entrant, so some integrations and plugins are still maturing; Most of the polish is in the coding flow; other workflows are less developed; Usage-based limits can be confusing to predict for large teams

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

## Frequently Asked Questions

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

ML.ai has 0 monthly visits, while Trae has 702,366. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

ML.ai has a month-over-month growth rate of 0%; Trae has -7.8%.

### How does pricing compare?

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. Trae: Freemium. Free to start, with paid upgrades that increase usage limits for premium models; enterprises can purchase through BytePlus. Usage is measured per AI request and varies by model, so heavy users should review the pricing page for current allowances before committing.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Claude](https://openaitools.app/tool/claude-2) | 1,253,439,413 | +23.8% | Freemium | Anthropic's frontier AI workspace powered by Claude 5.5 for agentic coding, analysis, and nuanced writing. | [.md](https://openaitools.app/tool/claude-2.md) |
| 2 | [Cursor](https://openaitools.app/tool/cursor) | 23,297,751 | +8.4% | Freemium | Offers much deeper IDE integration and superior multi-file editing, but requires a paid subscription for full features. | [.md](https://openaitools.app/tool/cursor.md) |
| 3 | [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) |
| 4 | [Replit](https://openaitools.app/tool/replit) | 10,016,437 | -7.7% | Freemium, Paid | Excels at instant browser-based cloud deployment and full-stack app generation, but is less suited for heavy local multi-repo workflows. | [.md](https://openaitools.app/tool/replit.md) |
| 5 | [VS Code](https://openaitools.app/tool/vs-code) | 7,397,370 | -3.8% | Freemium | Provides ultimate extensibility and open-source flexibility, but requires assembling your own extension stack to match Trae's native AI features. | [.md](https://openaitools.app/tool/vs-code.md) |
| 6 | [Qoder](https://openaitools.app/tool/qoder) | 5,145,040 | +81.8% | Freemium, Free Trial, Free | Provides powerful codebase-wide agentic planning and completion, but operates primarily as a plugin rather than a dedicated IDE. | [.md](https://openaitools.app/tool/qoder.md) |
| 7 | [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) |
| 8 | [Kiro](https://openaitools.app/tool/kiro) | 3,330,476 | +4.6% | Freemium | Uses rigorous spec-driven engineering plans to verify changes, though with a more structured and rigid workflow than Trae. | [.md](https://openaitools.app/tool/kiro.md) |
| 9 | [Devin](https://openaitools.app/tool/devin) | 2,549,346 | +1% | Paid | Operates as a fully autonomous software engineer that opens pull requests, but is much more expensive and task-oriented. | [.md](https://openaitools.app/tool/devin.md) |
| 10 | [Command Code](https://openaitools.app/tool/command-code) | 2,358,177 | -12.1% | Freemium, Contact for Pricing | Focuses on terminal-first workflows with open-weight models, but lacks the graphical multi-file interface of Trae. | [.md](https://openaitools.app/tool/command-code.md) |

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

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