Vertex AI Vector Search vs VikingDB

Vertex AI Vector Search9.2Visit VikingDB9.1Visit

Higher score Vertex AI Vector Search #2 in Vector Databases 9.2/10 Paid ✓ 3 of 3 features Visit Google Cloud

vs

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

• Vertex AI Vector Search fits best

  Google Cloud teams needing managed hybrid search

• VikingDB fits best

  Cloud users needing multimodal vector retrieval

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Side by side

Feature	Vertex AI Vector Search 9.2/10 Visit ↗	VikingDB 9.1/10 Visit ↗
At a glance
Editor score	9.2	9.1
Ranking	#2 in Vector Databases	#3 in Vector Databases
Best for	Google Cloud teams needing managed hybrid search	Cloud users needing multimodal vector retrieval
Pricing model	Paid	Paid
Starting price	Not checked yet	Not checked yet
Free plan	Not checked yet	Not checked yet
Free trial	—	—
Deployment	Cloud	Cloud
Platforms	Web	Web
Support	Community, Docs	Docs
Built for	Enterprise	Small business, Mid-market, Enterprise
Features Vertex AI Vector Search 3/3 · VikingDB 3/3
Hybrid search	✓	✓
Metadata filtering	✓	✓
Sparse vectors	✓	✓
Specs
Deployment	Cloud	Cloud
Maximum dimensions	Not checked yet	4096 dimensions
Index types	Tree-AH, brute force	HNSW, HNSW-Hybrid, FLAT, IVF, DiskANN
SDK languages	Python, Java	Python, Java, Go, Node.js
Our review
Pros	Supports dense, sparse, hybrid, and filtered vector search Offers batch and streaming index updates Provides public and private endpoints with SDK, REST, and gRPC access	Supports dense, sparse, hybrid, filtered, and multimodal search Offers HNSW, IVF, and DiskANN indexing Provides streaming updates plus API and SDK access
Cons	Usage-based costs span serving, building, and updating indexes Cloud deployment limits portability across infrastructure providers Requires Google Cloud services and client libraries for its ecosystem fit	Cloud deployment may not suit on-premises requirements Hourly postpaid billing can complicate fixed-budget planning Documented integration coverage centers on LangChain
Our verdict	Vertex AI Vector Search is a managed cloud vector search engine from Google Cloud for semantic search, recommendations, and generative AI applications. It is aimed at teams that want to create, deploy, update, and query indexes through… Read the review →	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 →

Strengths and trade-offs

• Vertex AI Vector Search — where it wins

  • Supports dense, sparse, hybrid, and filtered vector search
  • Offers batch and streaming index updates
  • Provides public and private endpoints with SDK, REST, and gRPC access

  Where it doesn't

  • Usage-based costs span serving, building, and updating indexes
  • Cloud deployment limits portability across infrastructure providers
  • Requires Google Cloud services and client libraries for its ecosystem fit

• 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

• Vertex AI Vector Search9.2/10 · Paid

  A managed Google Cloud option for teams building dense, sparse, and hybrid search.

  Visit Google CloudFull verdict →
• VikingDB9.1/10 · Paid

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

  Visit VikingDBFull verdict →