Amazon OpenSearch vs Google Vertex AI

Search Platform Heritage

Built on Elasticsearch fork with focus on search, analytics, and observability. Vector search added via k-NN plugin.

Infrastructure

• • AWS-native service
• • Master-data nodes
• • Plugin architecture
• • IAM integration

AI-First Design

Comprehensive ML platform where vector search is integrated with model training, serving, and monitoring.

Infrastructure

• • GCP-native platform
• • Managed endpoints
• • ML pipelines
• • AutoML integration

Getting Started

from opensearchpy import OpenSearch

# AWS Auth
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": {
      "properties": {
        "title": {"type": "text"},
        "vector": {
          "type": "knn_vector",
          "dimension": 768
        }
      }
    }
  }
)

Developer Experience

• ⚡ Elasticsearch familiarity
• ⚡ Rich query DSL
• ⚡ AWS IAM integration
• ⚡ Hybrid search native

Getting Started

from google.cloud import aiplatform

# Initialize
aiplatform.init(project="my-project")

# Create embeddings
embeddings = aiplatform.gapic.PredictionServiceClient().predict(
  endpoint=embedding_endpoint,
  instances=[{"content": text}]
)

# Create index
index = aiplatform.MatchingEngineIndex.create_tree_ah_index(
  display_name="products",
  dimensions=768
)

# Deploy endpoint
endpoint = aiplatform.MatchingEngineIndexEndpoint.create(
  display_name="products-endpoint"
)

Developer Experience

• ⚡ Integrated ML pipeline
• ⚡ Built-in embeddings
• ⚡ GCP IAM native
• ⚡ AutoML features