Understand vector search with Qdrant on Google Axion

Why vector search on Google Axion C4A

Qdrant is an open-source vector database designed for efficient similarity search and high-performance vector indexing. Running Qdrant on Google Axion C4A Arm-based infrastructure enables efficient execution of AI and vector search workloads. Axion processors, based on the Arm Neoverse V2 architecture, provide high performance and improved energy efficiency for modern cloud-native applications and data services.

Explore Axion C4A Arm instances in Google Cloud

Google Axion C4A is a family of Arm-based virtual machines built on Google’s custom Axion CPU, which is based on Arm Neoverse-V2 cores. Designed for high-performance and energy-efficient computing, these virtual machines offer strong performance for data-intensive and analytics workloads such as big data processing, in-memory analytics, columnar data processing, and high-throughput data services.

The C4A series provides a cost-effective alternative to x86 virtual machines while leveraging the scalability, SIMD acceleration, and memory bandwidth advantages of the Arm architecture in Google Cloud.

These characteristics make Axion C4A instances well-suited for modern analytics stacks that rely on columnar data formats and memory-efficient execution engines.

To learn more, see the Google blog Introducing Google Axion Processors, our new Arm-based CPUs .

Qdrant for semantic search and AI retrieval

Vector databases like Qdrant are commonly used in modern AI systems to support applications such as semantic search, recommendation systems, anomaly detection, and Retrieval-Augmented Generation (RAG) pipelines. By storing embeddings and performing nearest-neighbor search, Qdrant allows applications to retrieve the most relevant information based on semantic meaning rather than simple keyword matching.

Using Qdrant on Axion allows you to achieve:

• High-performance vector similarity search for AI applications
• Efficient embedding, storage, and indexing for semantic retrieval
• Low-latency data access for chatbots and AI assistants
• Scalable infrastructure for Retrieval-Augmented Generation (RAG) pipelines
• Cost-efficient execution of vector database workloads on Arm-based cloud infrastructure

Common use cases include AI chatbots, semantic search engines, recommendation systems, enterprise knowledge assistants, document retrieval systems, and machine learning feature stores.

To learn more, visit the Qdrant documentation and explore how vector databases enable modern AI applications.

What you’ve learned and what’s next

In this section, you learned about:

• Google Axion C4A Arm-based VMs and their performance characteristics
• Qdrant as a vector database for storing and retrieving embeddings
• Semantic similarity search and how it powers AI retrieval systems
• How vector search enables chatbot and RAG-style knowledge retrieval

Next, you can explore how to extend this setup by integrating large language models (LLMs) to build a full Retrieval-Augmented Generation (RAG) pipeline, enabling AI systems to generate context-aware responses using information retrieved from the Qdrant vector database.