mirror of
https://github.com/qdrant/landing_page.git
synced 2026-10-04 10:28:29 +02:00
24 lines
1.1 KiB
Markdown
24 lines
1.1 KiB
Markdown
---
|
||
title: Embed faster. Query faster. Go hybrid or multimodal.
|
||
cards:
|
||
- id: 0
|
||
icon:
|
||
src: /img/cloud-inference-features/ai.svg
|
||
alt: AI
|
||
title: Vector search with built-in embeddings
|
||
description: Generate embeddings inside the network of your Qdrant Cloud cluster. No separate model server or pipeline needed.
|
||
- id: 1
|
||
icon:
|
||
src: /img/cloud-inference-features/bars-growth.svg
|
||
alt: Bars growth
|
||
title: In-cluster inference, lower latency
|
||
description: Generate embeddings and run search in-region on AWS, Azure, or GCP (US only). No external hops, no extra egress. Ideal for real-time apps that can’t afford delays or data transfer overhead.
|
||
- id: 2
|
||
icon:
|
||
src: /img/cloud-inference-features/cloud-data.svg
|
||
alt: Cloud data
|
||
title: Supports Dense, Sparse & Image Models
|
||
description: Build vector search the way you need. Use dense models like all-MiniLM-L6-v2 for fast semantic match, sparse models like splade-pp-en-v1 or bm25 for keyword recall, or CLIP-style models for image and text. Need Hybrid and/or multimodal search? Covered.
|
||
sitemapExclude: true
|
||
---
|