Vector Databases

Zilliz Cloud vs Google Vertex AI

Comparing managed Milvus with Google's AI platform vector search in 2025 — 10 min read

Our Recommendation

A quick look at which tool fits your needs best

Zilliz Cloud

  • Dedicated vector database
  • GPU acceleration support
  • Open-source foundation

Google Vertex AI

  • Integrated ML ecosystem
  • Native embedding models
  • End-to-end AI platform

Quick Decision Guide

Choose Zilliz Cloud if you need:

  • Best vector search performance
  • GPU acceleration options
  • Cloud-agnostic deployment
  • Cost-effective scaling

Choose Vertex AI if you need:

  • End-to-end ML pipelines
  • Google AI model integration
  • Unified AI platform
  • GCP ecosystem benefits

Platform Details

Zilliz Cloud

Zilliz Inc.

Strengths

  • Dedicated vector database
  • GPU acceleration support
  • Open-source foundation
  • Advanced filtering
  • Cost-effective scaling

Weaknesses

  • Limited AI integrations
  • Separate from ML pipeline
  • Newer service
  • Manual embedding generation

Best For

Pure vector workloadsGPU-accelerated searchCost-conscious teamsMilvus users

Google Vertex AI

Google Cloud

Strengths

  • Integrated ML ecosystem
  • Native embedding models
  • End-to-end AI platform
  • Google scale infrastructure
  • AutoML capabilities

Weaknesses

  • GCP lock-in
  • Complex pricing model
  • Overkill for simple vectors
  • Steeper learning curve

Best For

End-to-end AI workflowsGCP-native applicationsML pipeline integrationGoogle AI model users

Architecture & Design Philosophy

Zilliz Cloud Architecture

Vector-First Philosophy

Built on Milvus with enhancements for cloud deployment. Focuses exclusively on vector similarity search excellence.

Infrastructure

  • • Cloud-native Kubernetes
  • • Separated compute/storage
  • • Optional GPU acceleration
  • • Multi-index support

Key Insight: Zilliz Cloud provides maximum flexibility for vector search workloads.

Vertex AI Architecture

Integrated AI Platform

Vector search as component of comprehensive ML platform including training, serving, and monitoring.

Infrastructure

  • • GCP-native services
  • • Integrated with AI models
  • • Managed endpoints
  • • AutoML integration

Key Insight: Vertex AI excels when vector search is part of larger AI workflows.

Performance Deep Dive

Vector Search Performance (10M vectors, 768 dimensions)

Zilliz Cloud Performance

Index Time 5-10 min
Query Latency (p50) 8ms
Query Latency (p99) 30ms
Throughput (CPU) 12,000 QPS
Throughput (GPU) 40,000 QPS

Vertex AI Performance

Index Time 10-20 min
Query Latency (p50) 20ms
Query Latency (p99) 85ms
Throughput 5,000 QPS
GPU Support Not available

Note: Zilliz Cloud GPU instances provide 3-5x performance improvement for large-scale workloads.

AI/ML Integration Comparison

Embedding Generation

Zilliz Cloud

Requires external embedding models (OpenAI, Cohere, etc.). Flexible choice but additional integration needed.

Vertex AI

Native integration with Google's embedding models (Gecko, PaLM embeddings). Seamless pipeline from text to vectors.

ML Pipeline Integration

Zilliz Cloud

Standalone service requiring custom integration. Works with any ML framework via APIs.

Vertex AI

Part of unified platform with training, serving, and monitoring. Native integration with Vertex AI Pipelines.

Total Cost of Ownership (TCO)

Pricing Comparison

Configuration Zilliz Cloud Vertex AI
Small (1M vectors) $65/month ~$50/month
Medium (10M vectors) $240/month ~$200/month
Large (100M vectors) $1,200/month ~$800/month
With GPU +$500/month Not available
Embedding Costs External Included

Zilliz Cloud Economics

  • • Superior price-performance ratio
  • • GPU option for demanding workloads
  • • Predictable scaling costs
  • • No vendor lock-in

Vertex AI Economics

  • • Integrated embedding costs
  • • GCP committed use discounts
  • • Unified billing
  • • Platform synergies

Developer Experience Comparison

Zilliz Cloud DX

Getting Started

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Developer Benefits

  • ✓ Cloud-agnostic deployment
  • ✓ Milvus compatibility
  • ✓ Fine-grained control
  • ✓ Active community

Vertex AI DX

Getting Started

Developer Benefits

  • ✓ Integrated ML pipeline
  • ✓ Built-in embeddings
  • ✓ GCP ecosystem
  • ✓ Enterprise support

Real-World Use Case Analysis

When Zilliz Cloud Wins

1. Multi-Cloud RAG Platform

Enterprise requirements:

  • • Deploy across AWS, Azure, GCP
  • • GPU-accelerated search
  • • Avoid vendor lock-in

Zilliz's flexibility essential

2. High-Performance Search API

SaaS platform needs:

  • • 50,000+ QPS requirement
  • • Sub-10ms latency
  • • Cost-effective scaling

Zilliz GPU performance wins

When Vertex AI Excels

1. End-to-End AI Application

ML-driven product needs:

  • • Model training pipeline
  • • Embedding generation
  • • Vector search integration

Vertex AI's platform ideal

2. Google AI Integration

AI-first company using:

  • • PaLM/Gemini models
  • • AutoML capabilities
  • • GCP infrastructure

Vertex AI seamlessly integrates

Ecosystem & Integration

Platform Integration Comparison

Zilliz Cloud Ecosystem

Framework Support

LangChain, LlamaIndex, Haystack integration

Embedding Models

Works with any embedding provider

Deployment Options

AWS, GCP, Azure, On-premise

Vertex AI Ecosystem

Google AI Models

Native PaLM, Gemini, Imagen access

GCP Services

BigQuery, Dataflow, Cloud Functions

ML Operations

Integrated monitoring and serving

Decision Matrix

Requirement Best Choice Reasoning
GPU acceleration needed Zilliz Cloud Native GPU support
GCP-native application Vertex AI Deep GCP integration
Multi-cloud deployment Zilliz Cloud Cloud-agnostic platform
ML pipeline integration Vertex AI Unified AI platform
Best price-performance Zilliz Cloud Superior performance/dollar
Google AI models needed Vertex AI Native model access

The Verdict

Zilliz Cloud: The Performance Champion

Zilliz Cloud delivers superior vector search performance with its dedicated architecture and GPU acceleration options. Its cloud-agnostic approach, better price-performance ratio, and Milvus foundation make it ideal for teams that prioritize vector search excellence and deployment flexibility.

Bottom Line: Choose Zilliz Cloud for best-in-class vector search performance with maximum flexibility.

Vertex AI: The Integrated Platform

Vertex AI Vector Search shines as part of Google's comprehensive AI ecosystem. Its native integration with Google's AI models, seamless ML pipeline support, and unified platform approach make it compelling for teams building end-to-end AI applications on Google Cloud.

Bottom Line: Choose Vertex AI when building integrated AI applications within the Google Cloud ecosystem.

🎯 Our Recommendation

For pure vector search workloads, Zilliz Cloud's specialized design, GPU support, and superior performance make it the better choice. However, if you're building end-to-end AI applications on Google Cloud and need integrated ML capabilities, Vertex AI provides compelling value as part of a unified platform.

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