Zilliz Cloud vs Amazon OpenSearch

Vector-First Design

Built on Milvus, the leading open-source vector database, with cloud-native enhancements for production workloads.

Infrastructure

• • Kubernetes-native deployment
• • GPU acceleration options
• • Separated compute/storage
• • Multiple index types

Search Platform Design

General-purpose search engine with k-NN plugin, balancing full-text search, analytics, and vector capabilities.

Infrastructure

• • Elasticsearch foundation
• • AWS service integrations
• • Master-data node architecture
• • Plugin-based vectors

Getting Started

from pymilvus import connections, Collection

# Connect to Zilliz Cloud
connections.connect(
  alias="default",
  uri="your-endpoint",
  token="your-token"
)

# Create collection with schema
collection = Collection(
  name="products",
  schema=schema,
  using="default"
)

Developer Benefits

• ✓ Milvus compatibility
• ✓ Rich vector features
• ✓ GPU acceleration
• ✓ Advanced filtering

Getting Started

from opensearchpy import OpenSearch

# AWS auth setup
client = OpenSearch(
  hosts=[{'host': 'domain.aws.com', 'port': 443}],
  http_auth=awsauth,
  use_ssl=True
)

# Create k-NN index
client.indices.create(
  index='products',
  body={
    "settings": {"index.knn": True},
    "mappings": {...}
  }
)

Developer Considerations

• ⚡ AWS IAM integration
• ⚡ Complex configuration
• ⚡ Full search capabilities
• ⚡ Enterprise features