---
title: "rag-time vs ollama"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/microsoft-rag-time-vs-ollama-ollama"
tools: ["microsoft-rag-time", "ollama-ollama"]
---

# rag-time vs ollama

*GraphCanon updated Jul 11, 2026*

## Verdict

Pick rag-time when rag-time is primarily Jupyter Notebook; ollama is Go; pick ollama when ollama is primarily Go; rag-time is Jupyter Notebook.

[rag-time](https://github.com/microsoft/rag-time) reports 894 GitHub stars, 316 forks, and 4 open issues, last pushed Jun 17, 2025. [ollama](https://ollama.com) has 176k stars, 17k forks, and 3.4k open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [rag-time's repository](https://github.com/microsoft/rag-time) and [ollama's repository](https://github.com/ollama/ollama).

| | [rag-time](/tools/microsoft-rag-time.md) | [ollama](/tools/ollama-ollama.md) |
| --- | --- | --- |
| Tagline | RAG Time: A 5-week Learning Journey to Mastering RAG | Get up and running with various large language models using Ollama. |
| Stars | 894 | 175,936 |
| Forks | 316 | 16,939 |
| Open issues | 4 | 3,423 |
| Language | Jupyter Notebook | Go |
| Adopt for | - | Ollama is a Go-based platform that provides tools for deploying and managing large language models (LLMs) like Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma using docker images, package managers, cloud and |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | MIT license - permissive open-source licensing that allows for broad use of the tool. |
| Categories | Inference & Serving, LLM Frameworks, Vector Databases | Inference & Serving, LLM Frameworks |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [rag-time](/tools/microsoft-rag-time.md) | [ollama](/tools/ollama-ollama.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Very active (96%) |
| Days since push | 388d | 1d |
| Open issues (now) | 4 | 3.4k |
| Security scan | No lockfile | 52 low (52 low) |
| Full report | [trust report](/tools/microsoft-rag-time/trust.md) | [trust report](/tools/ollama-ollama/trust.md) |

## Decision facts: ollama

- **Hosting:** self hosted - Ollama supports self-hosted and cloud-deployable models using Docker, Helm charts, and various package managers.
- **Adopt for:** Ollama is a Go-based platform that provides tools for deploying and managing large language models (LLMs) like Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma using docker images, package managers, cloud and
- **License detail:** MIT license - permissive open-source licensing that allows for broad use of the tool.

## Choose when

### Choose rag-time if…

- rag-time is primarily Jupyter Notebook; ollama is Go.
- Tags unique to rag-time: ai, azure, binary-quantization, generative-ai.
- Also covers Vector Databases.

### Choose ollama if…

- ollama is primarily Go; rag-time is Jupyter Notebook.
- Ollama supports self-hosted and cloud-deployable models using Docker, Helm charts, and various package managers.
- Tags unique to ollama: deepseek, gemma, glm, go.
- ollama ships Docker support for self-hosted deployment.
- Use Ollama when you require a multi-model platform supporting several large language models such as Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and intend to deploy in various cloud or

## When NOT to use rag-time

- Last GitHub push was 389 days ago (dormant maintenance, Jun 17, 2025). Validate activity before betting a new project on rag-time.
- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

## When NOT to use ollama

- Avoid using Ollama if you are only interested in a single LLM deployment and seek simplified, model-specific solutions with tailored support rather than a comprehensive multi-model platform.

## Common questions

### What is the difference between rag-time and ollama?

rag-time: RAG Time: A 5-week Learning Journey to Mastering RAG. ollama: Get up and running with various large language models using Ollama.. See the comparison table for live GitHub stats and shared categories.

### When should I choose rag-time over ollama?

Choose rag-time over ollama when rag-time is primarily Jupyter Notebook; ollama is Go; Tags unique to rag-time: ai, azure, binary-quantization, generative-ai; Also covers Vector Databases.

### When should I choose ollama over rag-time?

Choose ollama over rag-time when ollama is primarily Go; rag-time is Jupyter Notebook; Ollama supports self-hosted and cloud-deployable models using Docker, Helm charts, and various package managers; Tags unique to ollama: deepseek, gemma, glm, go; ollama ships Docker support for self-hosted deployment; Use Ollama when you require a multi-model platform supporting several large language models such as Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and intend to deploy in various cloud or.

### When should I avoid rag-time?

Last GitHub push was 389 days ago (dormant maintenance, Jun 17, 2025). Validate activity before betting a new project on rag-time. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

### When should I avoid ollama?

Avoid using Ollama if you are only interested in a single LLM deployment and seek simplified, model-specific solutions with tailored support rather than a comprehensive multi-model platform.

### Is rag-time or ollama more popular on GitHub?

ollama has more GitHub stars (175,936 vs 894). Stars measure visibility, not whether either tool fits your constraints.

### Are rag-time and ollama open source?

Yes - both are open-source projects on GitHub (rag-time: MIT, ollama: MIT).

### Where can I find alternatives to rag-time or ollama?

GraphCanon lists graph-backed alternatives at [rag-time alternatives](/tools/microsoft-rag-time/alternatives) and [ollama alternatives](/tools/ollama-ollama/alternatives) ([rag-time markdown twin](/tools/microsoft-rag-time/alternatives.md), [ollama markdown twin](/tools/ollama-ollama/alternatives.md)), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at [this comparison](/compare/microsoft-rag-time-vs-ollama-ollama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, rag-time or ollama?

rag-time: Dormant. ollama: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for rag-time and ollama?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [rag-time trust report](/tools/microsoft-rag-time/trust); [ollama trust report](/tools/ollama-ollama/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=microsoft-rag-time`](/api/graphcanon/graph?tool=microsoft-rag-time)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_