Vector Databases

Google Vertex AI vs Redis Vector

Comparing Google's AI platform with in-memory vector search in 2025 — 10 min read

Our Recommendation

A quick look at which tool fits your needs best

Google Vertex AI

  • Integrated ML ecosystem
  • Native embedding models
  • End-to-end AI platform

Redis Vector

  • Sub-millisecond latency
  • Cache + vector combo
  • Mature ecosystem

Quick Decision Guide

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

Platform Details

Google Vertex AI

Google Cloud

Strengths

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

Weaknesses

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

Best For

End-to-end AI workflowsGCP-native applicationsML pipeline integrationGoogle AI model users

Redis Vector

Redis Inc.

Strengths

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

Weaknesses

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

Best For

Real-time applicationsSmall-medium datasetsCache + search comboLow 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

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 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.

Need Help Choosing the Right Tool?

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