Zilliz Cloud vs Google Vertex AI
Comparing managed Milvus with Google's AI platform vector search in 2025
Our Recommendation
Zilliz Cloud
Specialized vector database with GPU acceleration
Best for:
Teams needing high-performance vector search with flexibility
Google Vertex AI
Comprehensive AI platform with vector capabilities
Best for:
GCP users building complete AI/ML applications
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
Quick Comparison
| Feature | Zilliz Cloud | Google Vertex AI |
|---|---|---|
| Platform Type | Dedicated Vector DB | AI Platform + Vector |
| Starting Price | $65/month | $0.025/hour + storage |
| GPU Support | Yes (Optional) | No |
| Cloud Support | Multi-cloud | GCP only |
| ML Integration | External | Native |
| Embedding Models | BYO | Built-in |
| Max Vectors | 10B+ | 10B+ |
| Open Source Base | Yes (Milvus) | No |
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
Vertex AI Performance
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
from pymilvus import connections
# Connect to Zilliz
connections.connect(
alias="default",
uri="your-endpoint",
token="your-api-key"
)
# Direct vector operations
collection.insert(vectors)
results = collection.search(
data=query_vectors,
anns_field="embedding",
param={"metric_type": "L2", "params": {"nprobe": 10}},
limit=10
) Developer Benefits
- ✓ Cloud-agnostic deployment
- ✓ Milvus compatibility
- ✓ Fine-grained control
- ✓ Active community
Vertex AI DX
Getting Started
from google.cloud import aiplatform # Initialize aiplatform.init(project="my-project") # Create index index = aiplatform.MatchingEngineIndex.create_tree_ah_index( display_name="my-index", dimensions=768 ) # Deploy endpoint endpoint = aiplatform.MatchingEngineIndexEndpoint.create( display_name="my-endpoint" ) endpoint.deploy_index(index=index)
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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