# Agenta vs Ollama

Agenta records 45,760 monthly visits with +29.5% month-over-month growth and Freemium pricing. Ollama records 9,421,973 monthly visits with +4.3% growth and Free, Freemium, Paid pricing.

| Metric | [Agenta](https://openaitools.app/tool/agenta) | [Ollama](https://openaitools.app/tool/ollama) |
| :--- | :--- | :--- |
| Website URL | https://www.agenta.ai | https://ollama.com |
| Categories | AI Developer Tools, Prompt Engineering, Large Language Models (LLMs), AI Agent | Large Language Models (LLMs), AI Developer Tools, Open Source AI Models |
| Pricing Model | Freemium | Free, Freemium, Paid |
| Monthly Visits | 45,760 | 9,421,973 |
| MoM Growth Rate | +29.5% | +4.3% |
| Primary Market | United States | China |
| Direct Visits | 17,206 | 4,020,134 |
| Review Score | 4.4 | 4.8 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | app, open-source, website |

## Agenta

Agenta positions itself as an open-source workspace where teams build AI agents and AI co-workers rather than one-off prompts. You can start from pre-built agent templates, give an agent access to models and tools, and run it from a chat surface, Slack or Telegram so the work happens where your team already communicates. Agents can browse, read and write files, and run scheduled automations that trigger from your connected systems.

What sets Agenta apart is its focus on operational control. Agents connect to more than a thousand integrations and MCP servers, secrets live in a vault the agent can use but not read, and permissions and approval steps decide which actions run automatically and which need a human sign-off. Every run is logged with duration, token usage and cost, so you can trace why an agent produced a certain result and iterate on it with feedback.

For engineering teams, Agenta ships as MIT-licensed software you can self-host with Docker Compose or Helm, keeping agents and data on your own infrastructure with no vendor lock-in. A managed cloud option is available for teams that prefer not to run the stack themselves, and both share the same workspace concepts of agents, skills, sessions and automations.

**Pricing & Plans:** Freemium. The open-source edition is MIT-licensed and free to self-host; cloud access is offered alongside it, with a demo available for larger teams. Exact seat and usage limits are listed on the pricing page.

- **Agent and co-worker builder:** Assemble agents from templates or from scratch, give them models, skills and tools, and run them from a chat UI, Slack or Telegram.
- **MCP connections and 1,000+ integrations:** Wire agents into Notion, GitHub, HubSpot, Slack, Linear, Drive and more over MCP, and swap tools or skills without rewriting the agent.
- **Evaluations and run observability:** Score agent outputs, collect feedback and inspect every run with duration, token usage and cost logging so improvements are measurable.
- **Guardrails, approvals and a secret vault:** Decide which actions run unattended and which require approval, while credentials stay in a vault your agent can use but cannot read.
- **Self-hosting with MIT license:** Deploy the full workspace on your own infrastructure with Docker Compose or Helm charts and keep agents and data on-premises.

**Strengths:** Open-source and MIT-licensed, so it can be self-hosted without vendor lock-in; Connects to over a thousand tools and MCP servers instead of a fixed integration list; Built-in evaluation and full run logging with token and cost tracking; Approval gates and a secret vault keep agents within human control

**Limitations:** The workspace concept is broad, so new users need time to map it to their workflow; Self-hosting requires someone comfortable running containers and Helm charts; Some enterprise features are only fully explained after contacting sales

[Profile (Markdown)](https://openaitools.app/tool/agenta.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 Agenta and Ollama compare in monthly traffic?

Agenta has 45,760 monthly visits, while Ollama has 9,421,973. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Agenta has a month-over-month growth rate of +29.5%; Ollama has +4.3%.

### How does pricing compare?

Agenta: Freemium. Freemium. The open-source edition is MIT-licensed and free to self-host; cloud access is offered alongside it, with a demo available for larger teams. Exact seat and usage limits are listed on the pricing page. 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 | [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) |
| 3 | [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) |
| 4 | [Open WebUI](https://openaitools.app/tool/open-webui) | 804,534 | -2.8% | Freemium | Feature-rich self-hosted web chat interface designed to manage and query local models like Ollama. | [.md](https://openaitools.app/tool/open-webui.md) |
| 5 | [LobeHub](https://openaitools.app/tool/lobehub) | 654,548 | -25% | Freemium | Put always-on AI agents on your team chat | [.md](https://openaitools.app/tool/lobehub.md) |
| 6 | [AnythingLLM](https://openaitools.app/tool/anythingllm) | 512,269 | +12.9% | Freemium | Privacy-first desktop app focused specifically on local document chat and RAG, unlike Ollama's general-purpose CLI runtime. | [.md](https://openaitools.app/tool/anythingllm.md) |
| 7 | [LlamaIndex](https://openaitools.app/tool/llamaindex) | 387,910 | +19.9% | Freemium, Contact for Pricing | Development framework for connecting local models to external document stores and structured data sources. | [.md](https://openaitools.app/tool/llamaindex.md) |
| 8 | [Jan](https://openaitools.app/tool/jan) | 291,894 | -9.6% | Free | Open-source desktop app for running models locally with a graphical interface, but heavier than Ollama's lightweight CLI. | [.md](https://openaitools.app/tool/jan.md) |
| 9 | [Arize AI](https://openaitools.app/tool/arize-com) | 249,028 | +3.2% | Paid, Contact for Pricing | Agent observability, evaluation, and continuous improvement for production AI | [.md](https://openaitools.app/tool/arize-com.md) |
| 10 | [Sim AI](https://openaitools.app/tool/sim-ai) | 140,953 | +20.6% | Freemium, Paid (per user/month), and Enterprise with custom pricing; self-hosting free under Apache 2.0 | Open-source workspace to build, deploy and govern AI agents | [.md](https://openaitools.app/tool/sim-ai.md) |

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Source: https://openaitools.app/compare/agenta-vs-ollama

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