VikingDB vs Qdrant

VikingDB9.1Visit Qdrant8.7Visit

Higher score VikingDB #3 in Vector Databases 9.1/10 Paid ✓ 3 of 3 features Visit VikingDB

vs

Qdrant #7 in Vector Databases 8.7/10 Free plan Free plan✓ 3 of 3 features Visit Qdrant

• VikingDB fits best

  Cloud users needing multimodal vector retrieval

• Qdrant fits best

  Modern teams wanting open hybrid search

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Side by side

Feature	VikingDB 9.1/10 Visit ↗	Qdrant 8.7/10 Visit ↗
At a glance
Editor score	9.1	8.7
Ranking	#3 in Vector Databases	#7 in Vector Databases
Best for	Cloud users needing multimodal vector retrieval	Modern teams wanting open hybrid search
Pricing model	Paid	Free plan + paid
Starting price	Not checked yet	—
Free plan	Not checked yet	✓ (best)
Free trial	—	—
Deployment	Cloud	Cloud, Self-hosted
Platforms	Web	Web, Windows, macOS, Linux
Support	Docs	Email, Tickets, Community, Docs
Built for	Small business, Mid-market, Enterprise	Small business, Mid-market, Enterprise
Features VikingDB 3/3 · Qdrant 3/3
Hybrid search	✓	✓
Metadata filtering	✓	✓
Sparse vectors	✓	✓
Specs
Deployment	Cloud	Both
Maximum dimensions	4096 dimensions	65535 dimensions (best)
Index types	HNSW, HNSW-Hybrid, FLAT, IVF, DiskANN	HNSW; sparse vector index; payload index; filterable HNSW
SDK languages	Python, Java, Go, Node.js	Python, JavaScript/TypeScript, Rust, Go, .NET, Java
Our review
Pros	Supports dense, sparse, hybrid, filtered, and multimodal search Offers HNSW, IVF, and DiskANN indexing Provides streaming updates plus API and SDK access	Hybrid dense and sparse search with metadata filtering Open-source self-hosting plus managed cloud deployment Multivectors, named vectors, REST, and gRPC APIs
Cons	Cloud deployment may not suit on-premises requirements Hourly postpaid billing can complicate fixed-budget planning Documented integration coverage centers on LangChain	The Free Tier is limited to a single node Free resources are limited to 0.5 vCPU, 1 GB RAM, and 4 GB disk Paid cloud tiers use usage-based pricing rather than fixed plan amounts
Our verdict	VikingDB is a cloud vector database from Volcengine for storing, indexing, and searching machine-learning vectors. It is aimed at teams building RAG systems, semantic search, recommendations, image similarity, video retrieval, data… Read the review →	Qdrant is an open-source vector search engine and vector database for developers building semantic search, recommendations, retrieval-augmented generation, and other AI applications. It supports self-hosted deployment and Qdrant Cloud,… Read the review →

Strengths and trade-offs

• VikingDB — where it wins

  • Supports dense, sparse, hybrid, filtered, and multimodal search
  • Offers HNSW, IVF, and DiskANN indexing
  • Provides streaming updates plus API and SDK access

  Where it doesn't

  • Cloud deployment may not suit on-premises requirements
  • Hourly postpaid billing can complicate fixed-budget planning
  • Documented integration coverage centers on LangChain

• Qdrant — where it wins

  • Hybrid dense and sparse search with metadata filtering
  • Open-source self-hosting plus managed cloud deployment
  • Multivectors, named vectors, REST, and gRPC APIs

  Where it doesn't

  • The Free Tier is limited to a single node
  • Free resources are limited to 0.5 vCPU, 1 GB RAM, and 4 GB disk
  • Paid cloud tiers use usage-based pricing rather than fixed plan amounts

• VikingDB9.1/10 · Paid

  A cloud vector database with dense, sparse, hybrid, filtered, and multimodal retrieval.

  Visit VikingDBFull verdict →
• Qdrant8.7/10 · Free plan

  A flexible vector database for teams building hybrid search and AI retrieval.

  Visit QdrantFull verdict →