Zilliz provides managed Milvus infrastructure that supports horizontal scaling, replication, and automated index optimization—essential features when connecting to large knowledge graphs. Graph-based systems can easily reach millions of entities, each with its own embedding. Running semantic search on such datasets requires distributed vector storage and fast retrieval paths, which Zilliz provides as a managed service.
Developers can partition embeddings across collections based on graph segments, such as domain or node type. Zilliz automatically balances the workload, ensuring low latency even under concurrent queries. Its API and SDKs allow graph applications to issue batch queries, stream results, and integrate retrieval responses directly into traversal logic.
This architecture allows enterprises to combine vast, interconnected data sources with semantic search at scale. Instead of managing hardware or index parameters manually, teams rely on Zilliz’s automated scaling and monitoring, focusing their effort on data modeling and reasoning rather than infrastructure maintenance.