Side by side
LlamaIndex vs Qdrant
A fact-based comparison built only from each tool's verified directory profile — pricing summaries, plan availability, platforms, and stated strengths and limitations. No rankings, no sponsored placement.
| Criterion | LlamaIndex | Qdrant |
|---|---|---|
| Category | RAG & Vector Databases | RAG & Vector Databases |
| In one sentence | Open-source framework for connecting LLMs to your data, plus the commercial LlamaParse document platform. | Open-source vector search engine written in Rust, available self-hosted or as a managed cloud. |
| Problem it solves | Getting messy documents into a form that LLMs can search and reason over accurately. | Fast similarity search with filters for RAG and search apps, with the option to keep data on your own infrastructure. |
| Free plan | Yes | Yes |
| Developer API | Yes — docs | Yes — docs |
| Open source | Yes | Yes |
| Platforms | Python, Web (LlamaCloud) | Docker, Kubernetes, Qdrant Cloud, Linux, macOS, Windows |
| Pricing (summary) | The LlamaIndex framework is free under MIT. LlamaParse plans: Free with 10K credits, Starter $50/month (40K credits), Pro $500/month (400K credits) and custom Enterprise; 1,000 credits cost $1.25. Checked 2026-10-19 | The engine is free and open source. Qdrant Cloud has a free-forever single-node cluster; Standard is billed hourly by resources used; Premium, Hybrid and Private Cloud pricing is on request. Checked 2026-10-19 |
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How to choose
Both profiles link to the vendors' official pricing — plan details change often, so verify there before buying. Pick based on the criterion rows above that matter to your workflow, not on rankings: we don't rank tools, because the right choice depends on your needs.