Vertex AI Vector Search vs Amazon OpenSearch Service

Vertex AI Vector Search8.3Visit Amazon OpenSearch Service8.2Visit

Higher score Vertex AI Vector Search #7 in Ai Databases 8.3/10 Paid ✓ 2 of 3 features Visit Google Cloud

vs

Amazon OpenSearch Service #8 in Ai Databases 8.2/10 Paid ✓ 2 of 3 features Visit AWS

• Vertex AI Vector Search fits best

  Enterprises standardizing on managed cloud vector search

• Amazon OpenSearch Service fits best

  AWS users building managed vector search and RAG

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Side by side

Feature	Vertex AI Vector Search 8.3/10 Visit ↗	Amazon OpenSearch Service 8.2/10 Visit ↗
At a glance
Editor score	8.3	8.2
Ranking	#7 in Ai Databases	#8 in Ai Databases
Best for	Enterprises standardizing on managed cloud vector search	AWS users building managed vector search and RAG
Pricing model	Paid	Paid
Starting price	Not checked yet	Not checked yet
Free plan	Not checked yet	—
Free trial	—	—
Deployment	Cloud	Cloud
Platforms	Web	Web
Support	Community, Docs	Live chat, Tickets, Docs
Built for	Enterprise	Small business, Mid-market, Enterprise
Features Vertex AI Vector Search 2/3 · Amazon OpenSearch Service 2/3
Metadata filtering	✓	✓
Hybrid search	✓	✓
Managed backups	Not checked yet	Not checked yet
Specs
Deployment	Not checked yet	Not checked yet
Index types	Tree-AH, brute force	Not checked yet
Max vector dimensions	Not checked yet	16000
Our review
Pros	Supports dense, sparse, and hybrid embedding search Combines Tree-AH and brute-force index approaches Offers metadata filtering, autoscaling, and private endpoints	Combines vector and lexical search, with metadata filtering. Offers serverless collections with automatic scaling and scale-to-zero. Connects with AWS services including S3, Kinesis, and Bedrock.
Cons	Cloud-only deployment does not fit self-hosted requirements Usage-based costs vary with infrastructure and update activity Index and deployment choices require careful architecture planning	Paid, usage-based pricing varies by deployment model and Region. Requires AWS ecosystem fit for its broadest integration value. Managed domains require choices for compute and storage configuration.
Our verdict	Vertex AI Vector Search is a managed Google Cloud vector search engine for semantic search, recommendation systems, retrieval-augmented generation, and other AI-powered applications. It serves enterprise teams that want hosted… Read the review →	For teams building search, analytics, observability, or AI applications on AWS, Amazon OpenSearch Service provides managed OpenSearch domains and serverless collections. Its vector features support k-nearest-neighbor retrieval, semantic… Read the review →

Strengths and trade-offs

• Vertex AI Vector Search — where it wins

  • Supports dense, sparse, and hybrid embedding search
  • Combines Tree-AH and brute-force index approaches
  • Offers metadata filtering, autoscaling, and private endpoints

  Where it doesn't

  • Cloud-only deployment does not fit self-hosted requirements
  • Usage-based costs vary with infrastructure and update activity
  • Index and deployment choices require careful architecture planning

• Amazon OpenSearch Service — where it wins

  • Combines vector and lexical search, with metadata filtering.
  • Offers serverless collections with automatic scaling and scale-to-zero.
  • Connects with AWS services including S3, Kinesis, and Bedrock.

  Where it doesn't

  • Paid, usage-based pricing varies by deployment model and Region.
  • Requires AWS ecosystem fit for its broadest integration value.
  • Managed domains require choices for compute and storage configuration.

• Vertex AI Vector Search8.3/10 · Paid

  Enterprise teams get dense, sparse, and hybrid vector search with managed Google Cloud operations.

  Visit Google CloudFull verdict →
• Amazon OpenSearch Service8.2/10 · Paid

  A managed AWS option for teams combining vector retrieval with lexical search.

  Visit AWSFull verdict →