Zilliz Cloud vs Redis Vector

Persistent Design

Built on Milvus with cloud-native enhancements. Separates compute and storage for scalability.

Infrastructure

• • Distributed architecture
• • Persistent storage layer
• • GPU acceleration options
• • Kubernetes-based

In-Memory First

Everything in RAM for maximum performance. RediSearch module adds vector capabilities.

Infrastructure

• • Single-threaded core
• • Memory-only by default
• • Optional persistence
• • Master-replica model

Getting Started

from pymilvus import connections, Collection

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

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

# Insert vectors
collection.insert([vectors, metadata])

Developer Benefits

• ✓ Unlimited scale
• ✓ Rich feature set
• ✓ Persistent by default
• ✓ GPU acceleration

Getting Started

import redis
from redis.commands.search import VectorField

# Connect
r = redis.Redis()

# Create index
r.ft("idx").create_index([
  VectorField("vector", "HNSW", {
    "TYPE": "FLOAT32",
    "DIM": 768,
    "DISTANCE_METRIC": "COSINE"
  })
])

# Add vectors instantly
r.hset("doc1", mapping={
  "vector": vector_bytes
})

Developer Benefits

• ✓ Lightning fast
• ✓ Simple operations
• ✓ Familiar Redis commands
• ✓ Cache integration