Google Vertex AI vs Redis Vector

Comparing Google's AI platform with in-memory vector search in 2025

Published June 19, 2025 • 10 min read
Google Vertex AI

Google Vertex AI

Google Cloud

VS
Redis Vector

Redis Vector

Redis Inc.

Quick Comparison

Feature Google Vertex AI Redis Vector
Type AI Platform + Vector Search In-Memory Database + Vector
Best For AI/ML Workflows Real-time Applications
Primary Strength ML Integration Sub-ms Latency
Deployment Google Cloud Any Infrastructure
Learning Curve Steep Moderate
Google Vertex AI

Google Vertex AI

Google Cloud

✓ Strengths

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

✗ Limitations

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

🎯 Best For

  • • End-to-end AI workflows
  • • GCP-native applications
  • • ML pipeline integration
  • • Google AI model users
Redis Vector

Redis Vector

Redis Inc.

✓ Strengths

  • • Sub-millisecond latency
  • • Cache + vector combo
  • • Mature ecosystem
  • • Simple operations
  • • Real-time performance

✗ Limitations

  • • Memory constraints
  • • Limited to HNSW
  • • No GPU support
  • • Expensive at scale

🎯 Best For

  • • Real-time applications
  • • Small-medium datasets
  • • Cache + search combo
  • • Low latency needs

Performance Analysis

Query Latency

Google Vertex AI 5-50ms
Redis Vector <1ms

ML Integration

Google Vertex AI Native
Redis Vector External

When to Choose Each

Choose Google Vertex AI if:

  • You need end-to-end ML workflows integrated with vector search
  • Your team uses Google Cloud and AI services extensively
  • You want native embedding generation and model hosting
  • AutoML and model training are part of your workflow

Choose Redis Vector if:

  • Ultra-low latency is your primary requirement
  • You need both caching and vector search capabilities
  • Your vector dataset fits comfortably in memory
  • You prefer simple, direct vector operations without ML overhead

Cost Considerations

Google Vertex AI Google Vertex AI

Vector Search $0.40/1M queries
Node Hours $0.50-$2.00/hour

Complex pricing with compute, storage, and API usage components. ML features add additional costs.

Redis Vector Redis Vector

Redis Cloud $5-$1000+/month
Self-hosted Infrastructure only

Predictable memory-based pricing. Self-hosted option provides cost control but requires management expertise.

Our Recommendation

Choose Google Vertex AI if you need a comprehensive AI/ML platform with integrated vector search, native embedding models, and end-to-end workflow management.

Choose Redis Vector if you prioritize ultra-low latency performance and want to combine caching with vector search in a single, fast system.