Best Vector Databases for Google Cloud (GCP)
Google Cloud Platform offers a powerful AI ecosystem with Vertex AI, and pairing it with the right vector database unlocks production-grade RAG, semantic search, and recommendation systems. From Google's own Vertex AI Vector Search to third-party databases deployed on GKE, these options integrate with GCP services like Cloud Storage, BigQuery, and Cloud Run for a complete AI stack.
9 databases compatible with Google Cloud
Why use Google Cloud with a vector database?
Google Cloud Platform offers a powerful AI ecosystem with Vertex AI, and pairing it with the right vector database unlocks production-grade RAG, semantic search, and recommendation systems. From Google's own Vertex AI Vector Search to third-party databases deployed on GKE, these options integrate with GCP services like Cloud Storage, BigQuery, and Cloud Run for a complete AI stack.
How to get started with Google Cloud
- 1Choose Vertex AI Vector Search for native GCP integration, or deploy a third-party database on GKE
- 2Use Vertex AI Embeddings API or a custom model to generate vectors
- 3Configure your vector database to use GCS for storage backends where supported
- 4Build your retrieval pipeline using Cloud Functions or Cloud Run for serving
FAQ — Google Cloud & Vector Databases
What is Google's vector database?
Google Vertex AI Vector Search is Google's native offering, built on the ScaNN algorithm. It provides sub-10ms latency at scale and integrates directly with Vertex AI for embeddings and ML workflows.
Can I run open-source vector databases on GCP?
Yes. Milvus, Qdrant, Weaviate, and others can be deployed on Google Kubernetes Engine (GKE) with persistent disks and auto-scaling.
Which vector database works best with Vertex AI?
Vertex AI Vector Search has the deepest native integration. For third-party options, Pinecone and Weaviate offer easy GCP deployment and work well with Vertex AI Embeddings.