Qdrant
Listed at https://qdrant.tech
Overall Rank #26 ⭐ Consider
⚠️ Cloud runs on AWS Frankfurt / N. Virginia / Sydney regions, ~200-400ms latency from mainland China; OSS can be self-hosted in Tencent/Aliyun CN regions | 🌍 International
💰 Token Pricing
| Type | Price | Note |
|---|---|---|
| Input | Free 永久层:1 GB 存储 + 0.5M 向量,无限期;Cloud Standard 月付 $25 起(预付 $250/yr)按存储 $0.06/GB-月;Cloud Pro 月付 $80 起 按存储 $0.04/GB-月 + 高 IOPS;Dedicated 合同制 $2,500/月起 | per million tokens |
| Output | 按存储维度(GB)与可选性能包(Power Tiers)计费;无 API 调用费、无 per-vector 嵌入费;FastEmbed 按 token 数本地推理;BYO Embedding 由第三方 API 计费 | per million tokens |
💡 Free Credits: Free forever: 1 GB storage + 0.5M 1015-dim vectors; 2 CPU/0.5 GiB RAM instance; unlimited API requests; community Discord support
🤖 Supported Models (9)
FastEmbed: BAAI/bge-small-en-v1.5FastEmbed: BAAI/bge-base-en-v1.5FastEmbed: BAAI/bge-large-en-v1.5FastEmbed: sentence-transformers/all-MiniLM-L6-v2FastEmbed: intfloat/e5-base-v2FastEmbed: intfloat/e5-large-v2FastEmbed: jinaai/jina-embeddings-v2-small (multilingual)FastEmbed: clip-ViT-B-32 (multimodal image+text)BYO Embedding: any OpenAI / Cohere / Voyage / Mistral API
✨ Pros
- ✓Rust-based open-source vector database (Apache 2.0 + commercial QPL), production-grade performance with low memory footprint
- ✓Native hybrid search (Sparse + Dense Vectors) + Named Vectors (multiple models per collection)
- ✓FastEmbed with 9 built-in embedding models, local inference with zero extra cost
- ✓API Recommendations: auto-suggests vector index configs and HNSW parameters
- ✓GPU-accelerated indexing (100× speedup, 2026 GA); MMR / Discovery search for diversity
- ✓Cloud SOC 2 Type II + ISO 27001 + GDPR compliant; HIPAA (BAA) at Pro+ tier
⚠️ Cons
- ×High latency from mainland China (self-host OSS or BYOC for low latency)
- ×Cloud pricing lower than Weaviate Flex but $25 tier requires $250/yr prepaid
- ×FastEmbed model set slightly smaller than Weaviate Cloud's 8 but lighter weight
🎯 Best For
RAG apps needing Rust performance and low memory; GPU-accelerated large-scale (>10M vectors) indexing; hybrid search + named vectors for multimodal; BYOC for cloud cost control