Vertex AI Vector Search vs Milvus

Vertex AI Vector Search9.2Visit Milvus8.8Visit

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

vs

Milvus #6 in Vector Databases 8.8/10 Free plan Free plan✓ 3 of 3 features Visit Milvus

• Vertex AI Vector Search fits best

  Google Cloud teams needing managed hybrid search

• Milvus fits best

  Open-source teams needing maximum vector depth

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Side by side

Feature	Vertex AI Vector Search 9.2/10 Visit ↗	Milvus 8.8/10 Visit ↗
At a glance
Editor score	9.2	8.8
Ranking	#2 in Vector Databases	#6 in Vector Databases
Best for	Google Cloud teams needing managed hybrid search	Open-source teams needing maximum vector depth
Pricing model	Paid	Free
Starting price	Not checked yet	Free plan
Free plan	Not checked yet	✓ (best)
Free trial	—	—
Deployment	Cloud	Cloud, Self-hosted
Support	Community, Docs	Community, Docs
Built for	Enterprise	Solo, Small business, Mid-market, Enterprise
Features Vertex AI Vector Search 3/3 · Milvus 3/3
Hybrid search	✓	✓
Metadata filtering	✓	✓
Sparse vectors	✓	✓
Specs
Deployment	Cloud	Both
Maximum dimensions	Not checked yet	Not checked yet
Index types	Tree-AH, brute force	HNSW, IVF, FLAT, DiskANN, SCANN
SDK languages	Python, Java	Python, Go, Java, Node.js, C++
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, binary, hybrid, filtered, and reranked search Runs locally, with Docker, or across distributed Kubernetes deployments Offers broad index coverage and SDKs for Python, Go, Java, Node.js, and C++
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	Self-hosted deployment places infrastructure and operations work on your team Its broad index catalog can make architecture decisions more involved Managed cloud is provided separately through Zilliz Cloud
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 →	Milvus is an open-source vector database for GenAI applications and similarity search. It suits teams building retrieval-augmented generation, image search, multimodal search, and large-scale production systems that need more than a single… 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

• Milvus — where it wins

  • Supports dense, sparse, binary, hybrid, filtered, and reranked search
  • Runs locally, with Docker, or across distributed Kubernetes deployments
  • Offers broad index coverage and SDKs for Python, Go, Java, Node.js, and C++

  Where it doesn't

  • Self-hosted deployment places infrastructure and operations work on your team
  • Its broad index catalog can make architecture decisions more involved
  • Managed cloud is provided separately through Zilliz Cloud

• Vertex AI Vector Search9.2/10 · Paid

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

  Visit Google CloudFull verdict →
• Milvus8.8/10 · Free plan

  A deep, flexible open-source choice for demanding vector and hybrid search workloads.

  Visit MilvusFull verdict →