Ollama vs Qdrant
Side-by-side comparison of two AI agent tools
Short answer
- Ollama is growing faster: +2,491 GitHub stars in the last 30 days vs +792 for Qdrant.
- Pick Ollama for: get up and running with Kimi-K2.5, GLM-5, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. Pick Qdrant for: vector similarity search engine and database written in Rust, with payload filtering and managed cloud service.
From GitHub data refreshed daily.
Ollamaopen-source
Get up and running with Kimi-K2.5, GLM-5, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.
Qdrantopen-source
Vector similarity search engine and database written in Rust, with payload filtering and managed cloud service
Metrics
| Ollama | Qdrant | |
|---|---|---|
| Stars | 182.1k | 34.9k |
| Star velocity /mo | 2.5k | 792.4736842105264 |
| Commits (90d) | 299 | 754 |
| Releases (6m) | 10 | 6 |
| Overall score | 0.8430554752532844 | 0.7225127920473718 |
Pros
- +完全本地运行，确保数据隐私和安全，无需将敏感信息发送到外部服务器
- +支持广泛的开源模型生态，包括最新的 Kimi-K2.5、GLM-5、DeepSeek 等前沿模型
- +丰富的集成生态系统，可与 Claude Code、OpenClaw 等工具连接，快速构建跨平台 AI 应用
- +High-performance Rust implementation delivers fast vector operations and reliable performance under heavy loads with proven benchmarks
- +Advanced filtering capabilities allow complex queries combining vector similarity with metadata filtering for sophisticated search scenarios
- +Production-ready with both self-hosted and managed cloud options, including comprehensive APIs and client libraries for easy integration
Cons
- -依赖本地计算资源，运行大型模型需要较高的 CPU/GPU 和内存配置
- -模型推理速度受限于本地硬件性能，可能不如云端专用硬件快
- -需要手动管理模型版本更新和依赖关系
- -Specialized focus on vector operations means additional tools needed for traditional database operations and non-vector data storage
- -Requires understanding of vector embeddings and similarity search concepts, creating a learning curve for teams new to vector databases
Use Cases
- •企业级私有部署，在内网环境中运行大语言模型，确保敏感数据不外泄
- •开发者工具集成，通过 Claude Code 等编码助手在本地环境中获得 AI 代码建议
- •多平台聊天机器人开发，使用 OpenClaw 将本地模型部署到 Slack、Discord 等通讯平台
- •Semantic search applications that need to find similar documents, images, or content based on meaning rather than exact keywords
- •Recommendation systems that match user preferences with product catalogs or content libraries using neural network embeddings
- •Neural network-based matching for applications like duplicate detection, content classification, or similarity-based grouping
FAQ
- Which is more popular, Ollama or Qdrant?
- Ollama has more GitHub stars (182,082 vs 34,908).
- Which is more actively developed, Ollama or Qdrant?
- Qdrant had more commits in the last 90 days (754 vs 299).
- Should I use Ollama or Qdrant?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.