# Augment Code vs Promptr

Augment Code records 326,215 monthly visits with +24.1% month-over-month growth and Freemium, Free Trial, Contact for Pricing pricing. Promptr records 0 monthly visits with 0% growth and Pay-as-you-go pricing.

| Metric | [Augment Code](https://openaitools.app/tool/augment-code) | [Promptr](https://openaitools.app/tool/promptr) |
| :--- | :--- | :--- |
| Website URL | https://www.augmentcode.com | https://promptr.dev |
| Categories | AI Agent, AI Code Assistant, AI Code Generator, AI Developer Tools | Other |
| Pricing Model | Freemium, Free Trial, Contact for Pricing | Pay-as-you-go |
| 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 | 3.6 |
| 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)

## Promptr

Promptr targets a very specific cost problem: long prompts sent to large language models are billed by the token, and repeating the same lengthy instructions hundreds of times adds up quickly. Its API takes the text of a prompt and returns a denser, compressed form that keeps the meaning while dropping redundant wording. The site's own example compresses a 136-token instruction about an AI agent designing a toy company website down to roughly 75 tokens, which it reports as a 45% reduction.

Integration is deliberately minimal. You POST your prompt as a JSON string to the compress endpoint with an API token in the header, and you receive the compressed text back as plain text. Documentation provides ready-made snippets for curl, Python, JavaScript and C#, so the service can be inserted as one extra step before your existing model call. You can also control behaviour inline with tags: wrap text in a promptr compress="false" block to leave it untouched and unbilled, or set an explicit ratio such as 0.5 or 0.8 to decide how aggressively a section is shortened.

The pricing page frames the value in terms of savings. At $1 per 1M input tokens processed by Promptr, it illustrates a one-off gpt-4-turbo prompt costing $10 without compression versus $7 with it, roughly 30% saved, and larger savings when a prompt is reused many times. Access currently runs through a waitlist. Because compression is lossy by nature and output quality is task-dependent, the sensible approach is to benchmark Promptr on your own prompts before rolling it out broadly.

**Pricing & Plans:** Pay-as-you-go at $1 per 1M input tokens processed, with no hidden costs according to the site. Sections marked as uncompressed are not billed. Access is currently gated behind a waitlist.

- **One-call prompt compression:** Send your prompt to a single REST endpoint and receive a shorter version back as plain text, ready to pass to your model.
- **Up to ~40% cost savings:** The vendor reports up to 40% lower LLM input cost, with a worked example showing a 45% reduction on a sample prompt.
- **Multi-language snippets:** Copy-paste examples are provided for curl, Python, JavaScript and C#, so integration takes minutes rather than days.
- **Inline ratio control:** Wrap sections in tags to exclude them from compression or to set a specific compression ratio for that block.
- **Uncompressed sections are free:** Text marked to be left alone is not billed, letting you protect critical instructions from being altered.

**Strengths:** Simple single-endpoint API that is easy to slot into existing code; Clear, explicit pay-as-you-go price of $1 per 1M input tokens; Code snippets for four common languages reduce integration effort; Inline tags let you protect sensitive or critical text from compression

**Limitations:** Access requires joining a waitlist rather than signing up directly; Compression is inherently lossy, so it can change model output quality; Real savings depend on your prompt length and reuse, so benefits must be tested case by case

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

## Frequently Asked Questions

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

Augment Code has 326,215 monthly visits, while Promptr 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%; Promptr 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. Promptr: Pay-as-you-go. Pay-as-you-go at $1 per 1M input tokens processed, with no hidden costs according to the site. Sections marked as uncompressed are not billed. Access is currently gated behind a waitlist.

## 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 | [Unsloth AI](https://openaitools.app/tool/unsloth-ai) | 887,457 | -17.6% | Free | Open-source, memory-efficient fine-tuning and local model running | [.md](https://openaitools.app/tool/unsloth-ai.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) |

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

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