# Augment Code vs stagewise

Augment Code records 326,215 monthly visits with +24.1% month-over-month growth and Freemium, Free Trial, Contact for Pricing pricing. stagewise records 1,110 monthly visits with -73% growth and Free, Enterprise (contact for pricing) pricing.

| Metric | [Augment Code](https://openaitools.app/tool/augment-code) | [stagewise](https://openaitools.app/tool/stagewise) |
| :--- | :--- | :--- |
| Website URL | https://www.augmentcode.com | https://stagewise.io |
| Categories | AI Agent, AI Code Assistant, AI Code Generator, AI Developer Tools | AI Agent, AI Developer Tools, AI Code Assistant, AI Design Assistant |
| Pricing Model | Freemium, Free Trial, Contact for Pricing | Free, Enterprise (contact for pricing) |
| Monthly Visits | 326,215 | 1,110 |
| MoM Growth Rate | +24.1% | -73% |
| Primary Market | United States | Italy |
| Direct Visits | 137,070 | 430 |
| 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)

## stagewise

stagewise describes itself as a next-gen agent orchestrator rather than a simple autocomplete plugin. The core idea is "model sovereignty": you choose the model behind every agent, whether that is a frontier model such as Claude, GPT or Gemini, an open-weight model like DeepSeek, Qwen, GLM, Kimi or MiniMax, or something running locally through Ollama, vLLM or llama.cpp. The IDE ships with an optional cloud inference layer so you can start without any API keys, but you can swap in your own keys or a custom endpoint at any time.

The engineering focus is on running agents for long stretches without burning through tokens. stagewise keeps the conversation prefix stable between turns to maximize prompt cache hits and sends a compact state delta when the environment changes instead of rebuilding the whole context. It also compresses context automatically as a task grows, which keeps agents aware of terminal output and browser state at a fraction of the usual cost. The site advertises an average cache hit rate of roughly 87%.

The codebase is published under AGPLv3 on GitHub, the company is stagewise GmbH and is backed by Y Combinator, and an enterprise offering is available for teams that need to run stagewise on their own infrastructure with their own models. The team has also announced Klex Bots, a related product aimed at end-to-end coding tasks from planning through to a merged pull request.

**Pricing & Plans:** The stagewise agentic IDE is open source and free to use (AGPLv3), with an optional hosted account that provides preconfigured cloud inference access without your own API keys. You can also bring your own provider keys or a custom endpoint. Enterprise pricing is quoted on request for on-premises or self-hosted deployments.

- **Parallel multi-agent orchestration:** Run several agents at the same time, each independently implementing a feature or fixing a bug, from a single command center.
- **Full model sovereignty:** Point each agent at frontier APIs, open-weight models or local inference through Ollama, vLLM and llama.cpp, as long as the provider speaks a compatible API.
- **Cache-optimized runtime:** A stable conversation prefix and compact state deltas keep prompt cache hit rates high, making long-running agent sessions considerably cheaper.
- **Bring your own subscription:** Connect existing coding subscriptions and providers such as OpenAI, Anthropic, Google Gemini, DeepSeek, Kimi, Qwen and MiniMax, or use an aggregator.
- **Open source and self-hostable:** The full AGPLv3 codebase is on GitHub, and enterprises can run stagewise on their own infrastructure with their own model endpoints.

**Strengths:** Free and open source under AGPLv3, with the code publicly available; Genuine model choice, including local and open-weight models, not a single-vendor lock-in; Deliberate engineering for token efficiency on long-running agent tasks; Backed by Y Combinator and offering an enterprise deployment path

**Limitations:** Aimed at developers — non-technical users will find the setup heavy; Local model use depends on your own hardware, which gets expensive at scale; Requires a reasonably large context window (around 150k tokens recommended) for good results

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

## Frequently Asked Questions

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

Augment Code has 326,215 monthly visits, while stagewise has 1,110. 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%; stagewise has -73%.

### 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. stagewise: Free, Enterprise (contact for pricing). The stagewise agentic IDE is open source and free to use (AGPLv3), with an optional hosted account that provides preconfigured cloud inference access without your own API keys. You can also bring your own provider keys or a custom endpoint. Enterprise pricing is quoted on request for on-premises or self-hosted deployments.

## 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 | [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) |
| 6 | [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) |
| 7 | [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) |
| 8 | [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) |
| 9 | [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) |
| 10 | [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) |

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

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