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RAG pipelines require databases that can ingest and update embeddings frequently, retrieve with low latency, and return results with structured metadata. A typical RAG architecture consists of four distinct layers:<\/p>\n
Conflating the Transactional System of Record with the Vector Store creates governance and performance risks. Storing embeddings in the same unstructured store as PII or financial records increases the attack surface and complicates compliance with Malaysia’s Personal Data Protection Act (PDPA). While PDPA does not create a blanket data-residency mandate, it requires strict separation of sensitive and non-sensitive data. A monolithic "AI database" that forces all data into a single schema often fails to enforce this separation effectively.<\/p>\n
Multi-model databases attempt to solve this by supporting multiple data models within one system. For example, KingbaseES supports Relational, Document, Spatial, Time Series, Text Data, Vector embeddings, Key-Value, and Property Graph\/RDF. This allows a single platform to manage both structured transactions and vector embeddings. However, the architectural roles remain distinct. The database must still enforce strict isolation between the vector retrieval engine and the transactional engine to prevent latency spikes in OLTP workloads from affecting AI inference speed.<\/p>\n
The performance of vector retrieval depends heavily on the indexing method. Native ANN indexing, such as HNSW (Hierarchical Navigable Small World) or IVF (Inverted File Index), is designed for high-dimensional vector similarity search. Traditional SQL engines, however, rely on B-tree or Hash indexes, which are optimized for exact match and range queries, not cosine similarity.<\/p>\n
When a traditional database attempts to perform vector similarity search without native ANN support, it often resorts to full-table scans or inefficient SQL-based approximations. This leads to significant latency penalties as the vector dimensionality increases. Query fan-out in a hybrid architecture, where the application layer handles vector retrieval separately from the database, can also impact latency due to network overhead and serialization costs.<\/p>\n
Multi-model databases like KingbaseES include Vector embeddings as a supported data model. This indicates a structural capability to handle vector data types. However, specific baseline performance metrics for vector retrieval, such as latency thresholds for high-concurrency RAG pipelines, are not explicitly documented in the available evidence. Architects must validate these performance boundaries through Proof of Concept (PoC) testing. The key is to ensure that vector operations do not degrade OLTP performance. This requires isolating vector workloads, either through dedicated hardware resources or by using a multi-model database that can tune query plans for vector-specific operations without impacting transactional throughput.<\/p>\n
Maintaining separate vector and relational stacks incurs hidden costs. Licensing, infrastructure scaling, and engineering overhead for hybrid setups often exceed the cost of a unified multi-model platform. The TCO analysis below compares these approaches.<\/p>\n