Product
Zilliz Cloud Delivers Better Performance and Lower Costs with Arm Neoverse-based AWS Graviton
Zilliz Cloud adopts Arm-based AWS Graviton3 CPUs to cut costs, speed up AI vector search, and power billion-scale RAG and semantic search workloads.
Product
Zilliz Cloud Enterprise Vector Search Powers High-Performance AI on AWS
Zilliz Cloud on AWS powers secure, scalable, ultra-fast vector search for enterprise AI apps, with BYOC, sub-10ms latency, and zero-DevOps simplicity.
Engineering
How AI Is Transforming Information Retrieval and What’s Next for You
This blog will summarize the monumental changes AI brought to Information Retrieval (IR) in 2024.
Engineering
Elasticsearch Was Great, But Vector Databases Are the Future
Purpose-built vector databases outperform dual-system setups by unifying Sparse-BM25 and semantic search in a single, efficient implementation.
Product
Safe RAG with HydroX AI and Zilliz: PII Masking for Responsible GenAI
Organizations can ensure privacy at every layer of their data pipeline by anonymizing or masking PII using the PII Marker before data reaches the vector database.
Engineering
Unlock AI-powered search with Fivetran and Milvus
Fivetran supports the Milvus vector database as a destination, making it easier to onboard every data source for RAG and AI-powered search.
Engineering
Tame High-Cardinality Categorical Data in Agentic SQL Generation with VectorDBs
Explores how integrating vector databases with agentic text-to-SQL systems can address High-Cardinality Categorical Data problems.
Engineering
The Landscape of GenAI Ecosystem: Beyond LLMs and Vector Databases
Initially, Large Language Models (LLMs) and vector databases captured the most attention. However, the GenAI ecosystem is much broader and more complex than just these two components.
Engineering
Optimizing RAG with Rerankers: The Role and Trade-offs
Rerankers can enhance the accuracy and relevance of answers in RAG systems, but these benefits come with increased latency and computational costs.