# Augment Code vs Ollama

Augment Code records 326,215 monthly visits with +24.1% month-over-month growth and Freemium, Free Trial, Contact for Pricing pricing. Ollama records 9,421,973 monthly visits with +4.3% growth and Free, Freemium, Paid pricing.

| Metric | [Augment Code](https://openaitools.app/tool/augment-code) | [Ollama](https://openaitools.app/tool/ollama) |
| :--- | :--- | :--- |
| Website URL | https://www.augmentcode.com | https://ollama.com |
| Categories | AI Agent, AI Code Assistant, AI Code Generator, AI Developer Tools | Large Language Models (LLMs), AI Developer Tools, Open Source AI Models |
| Pricing Model | Freemium, Free Trial, Contact for Pricing | Free, Freemium, Paid |
| Monthly Visits | 326,215 | 9,421,973 |
| MoM Growth Rate | +24.1% | +4.3% |
| Primary Market | United States | China |
| Direct Visits | 137,070 | 4,020,134 |
| Review Score | 4.5 | 4.8 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | app, open-source, 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)

## Ollama

Ollama has become the default way many developers get started with open models. A single install gives you a local server and a command line that pulls model weights, manages versions, and exposes a simple HTTP API on your machine. Because everything runs locally, prompts and responses never leave your computer, which makes it a natural fit for privacy-sensitive prototyping, offline work, and experiments that should not be metered per token.

The project has expanded well beyond a local runtime. Ollama now also serves open models in the cloud, so you can reach larger or more capable checkpoints without owning a high-end GPU. The company reports over nine million monthly installs, more than a billion model downloads, and hundreds of trillions of tokens served, which reflects how widely the tool is embedded in AI developer workflows.

Pricing stays approachable: running models on your own hardware is free and unlimited, while cloud usage is metered per token, with subscription plans for individuals, power users, and teams. Model weights are supplied by the upstream publishers, and Ollama states that prompt and response data are never logged or used for training.

**Pricing & Plans:** Running models on your own hardware is free. Cloud use is pay-as-you-go per million tokens, with Pro around $20/month, Max around $100/month, Team around $500/month including shared usage credits, and custom Enterprise pricing. Yearly billing discounts are offered.

- **One-command local models:** Install once and pull open-weight models by name; Ollama handles weights, quantization and versioning for you.
- **Local HTTP API and CLI:** A built-in server exposes a straightforward REST API, so scripts, editors and agents can talk to local models with minimal setup.
- **Cloud models for bigger jobs:** Route requests to hosted open models when your laptop cannot hold the checkpoint you need, with per-token pricing and concurrency tiers.
- **Broad model catalog:** Access families such as Llama, Qwen, DeepSeek, Gemma, Mistral and gpt-oss from a single interface.
- **Privacy by default:** Local inference keeps prompts and outputs on your own device, with no logging or training on your data.

**Strengths:** Extremely quick to install and use, with a friendly CLI and API; Local inference means strong privacy and no per-token cost on your own hardware; Wide, actively updated catalog of open-weight models; Cloud option scales to larger models without new hardware

**Limitations:** Local performance depends heavily on your GPU and RAM; Cloud usage is billed per token and large models add up quickly; Advanced serving features are less extensive than dedicated inference servers

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

## Frequently Asked Questions

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

Augment Code has 326,215 monthly visits, while Ollama has 9,421,973. 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%; Ollama has +4.3%.

### 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. Ollama: Free, Freemium, Paid. Running models on your own hardware is free. Cloud use is pay-as-you-go per million tokens, with Pro around $20/month, Max around $100/month, Team around $500/month including shared usage credits, and custom Enterprise pricing. Yearly billing discounts are offered.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [OpenRouter](https://openaitools.app/tool/openrouter-ai) | 19,941,437 | -6.2% | Paid | Cloud API gateway providing instant access to thousands of models, trading local hardware privacy for cloud compute. | [.md](https://openaitools.app/tool/openrouter-ai.md) |
| 2 | [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) |
| 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 | 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) |
| 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 | 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) |
| 8 | [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) |
| 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 | [Dify](https://openaitools.app/tool/dify-ai) | 819,691 | -15% | Freemium | Open-source platform for building visual agentic workflows and RAG pipelines using local or cloud models. | [.md](https://openaitools.app/tool/dify-ai.md) |

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

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