Vertex AI Vector Search vs Qdrant

Vertex AI Vector Search9.2Visit Qdrant8.7Visit

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

vs

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

• Vertex AI Vector Search fits best

  Google Cloud teams needing managed hybrid search

• Qdrant fits best

  Modern teams wanting open hybrid search

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Side by side

Feature	Vertex AI Vector Search 9.2/10 Visit ↗	Qdrant 8.7/10 Visit ↗
At a glance
Editor score	9.2	8.7
Ranking	#2 in Vector Databases	#7 in Vector Databases
Best for	Google Cloud teams needing managed hybrid search	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	Community, Docs	Email, Tickets, Community, Docs
Built for	Enterprise	Small business, Mid-market, Enterprise
Features Vertex AI Vector Search 3/3 · Qdrant 3/3
Hybrid search	✓	✓
Metadata filtering	✓	✓
Sparse vectors	✓	✓
Specs
Deployment	Cloud	Both
Maximum dimensions	Not checked yet	65535 dimensions
Index types	Tree-AH, brute force	HNSW; sparse vector index; payload index; filterable HNSW
SDK languages	Python, Java	Python, JavaScript/TypeScript, Rust, Go, .NET, Java
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	Hybrid dense and sparse search with metadata filtering Open-source self-hosting plus managed cloud deployment Multivectors, named vectors, REST, and gRPC APIs
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	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	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 →	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

• 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

• 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

• Vertex AI Vector Search9.2/10 · Paid

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

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

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

  Visit QdrantFull verdict →