On-premises AI data infrastructure architecture

On-premises AI Data Infrastructure Architecture

Building an on-premises AI data infrastructure requires moving beyond the concept of a single monolithic database. IT Directors and Chief Data Officers face the challenge of supporting high-throughput vector retrieval for Retrieval-Augmented Generation (RAG) while maintaining data sovereignty. The solution is a federated architecture that separates the ‘System of Record’ from the ‘AI Retrieval Layer.’

In this model, KingbaseES serves as the transactional foundation. It handles structured data integrity, ACID compliance, and governance. It does not replace dedicated vector stores or document repositories. Instead, it provides the secure, compliant data layer that AI inference engines consume. This separation allows sensitive proprietary data to remain within the enterprise perimeter while enabling the AI layer to perform semantic search without compromising system reliability.

Architectural Boundaries: Why On-Premises AI Requires a Federated Stack

A monolithic database attempting to handle transactional integrity, vector embeddings, and unstructured document storage simultaneously creates significant bottlenecks. A federated architecture defines clear boundaries between these workloads.

The architecture consists of three distinct layers:

1. The System of Record (Transactional Layer): This layer holds the authoritative source of truth for structured data. It manages customer records, financial transactions, and operational metadata. KingbaseES fits here as a commercial enterprise-grade database. It is designed for online transaction processing (OLTP) and query-intensive data warehouses. It supports data consistency and security.
2. The AI Retrieval Layer (Vector/Search): This layer handles high-throughput semantic search and vector retrieval for RAG applications. It stores embeddings generated from unstructured data. This component requires specialized indexing for fast nearest-neighbor searches, a capability distinct from traditional SQL operations.
3. The Document Store (Unstructured Data): This layer manages large objects, such as PDFs, images, or raw text files, before they are processed into embeddings.

Separating these layers prevents the transactional database from being overwhelmed by heavy vector indexing operations. It also isolates compliance risks. If the vector store requires maintenance or updates, the transactional system remains stable. This structure allows enterprises to scale each component independently based on specific workload characteristics.

Data Sovereignty: Physical and Logical Separation Requirements

Data sovereignty is a primary driver for on-premises deployments. Enterprises must ensure that data residency requirements are met without relying on public cloud providers. This necessitates physical and logical separation of data layers.

The physical layer must reside within local data centers or on-premises facilities. The logical layer must enforce access controls that prevent unauthorized data egress. KingbaseES supports on-premises deployment on a wide range of hardware and operating systems. This compatibility allows enterprises to choose hardware that aligns with local procurement strategies and sovereignty goals.

To meet these requirements, enterprises should follow this checklist:

• Hardware Selection: Deploy on mainstream CPUs (Intel, AMD) or domestic processors (Hygon, Loongson, Phytium/Kunpeng) available in the region.
• Operating System Alignment: Use supported operating systems such as Kylin, UOS, or OpenEuler to ensure compatibility with local security standards.
• Data Residency Verification: Ensure that KingbaseES instances and associated vector stores are physically located within the enterprise’s designated data centers.
• Access Control Implementation: Enforce role-based access control (RBAC) at the database level to govern who can query the System of Record.

This approach ensures that the foundational data layer remains under direct enterprise control. It provides a clear audit trail for regulatory compliance.

Latency and Performance: The Cost of Data Movement in RAG

Retrieval-Augmented Generation (RAG) applications introduce specific latency challenges in on-premises environments. The workflow involves querying the System of Record for structured context, retrieving vector embeddings for semantic similarity, and combining them for inference.

Moving data between these layers incurs network and processing overhead. Enterprises must understand the latency implications of this movement. Direct database queries for structured data are typically low-latency. However, vector retrieval for semantic search requires specialized indexing. If the vector store is not optimized, retrieval times can increase significantly, degrading user experience.

It is critical to note that KingbaseES is not a vector database. It does not provide native vector search capabilities for RAG. Attempting to use a relational database for high-throughput vector retrieval is inefficient. The correct architectural pattern involves KingbaseES providing the structured metadata and context, while a specialized vector store handles the embedding search.

To manage latency effectively:

• Minimize Data Movement: Keep the vector store and document store in the same local network segment as the inference engine.
• Optimize Index Freshness: Implement a strategy for regenerating embeddings when source data changes. Stale indexes lead to inaccurate retrieval.
• Use Metadata Filtering: Filter structured data in KingbaseES before sending requests to the vector store. This reduces the volume of vectors searched, improving latency.
• Separate Read and Write Paths: Use read-write separation clusters in the transactional layer to ensure that heavy analytical queries do not block real-time AI data ingestion.

This separation ensures that the AI layer remains responsive while the transactional layer maintains integrity. It prevents AI workloads from degrading core business operations.

Hardware Procurement and TCO: On-Premises vs. Cloud OpEx

Total Cost of Ownership (TCO) analysis for on-premises AI infrastructure must account for hardware procurement lead times, capital expenditure, and operational overhead. While cloud services offer OpEx models with lower upfront costs, they introduce data sovereignty risks and long-term expense volatility.

On-premises deployment requires significant initial investment in GPU clusters and database servers. However, it provides predictable costs over the asset lifecycle. KingbaseES supports a wide range of hardware environments, reducing vendor lock-in and allowing enterprises to leverage existing infrastructure.

The following table compares key TCO factors:

Enterprises must evaluate the operational overhead of managing multiple specialized components. This includes maintaining the transactional database, the vector store, and the GPU cluster. KingbaseES simplifies part of this burden by providing a single, commercial support contract for the transactional layer. It offers high-availability architectures, including primary-standby and multi-active shared storage clusters, to ensure resilience without requiring complex custom scripting.

This analysis highlights that on-premises TCO is not just about hardware. It is about balancing the cost of internal expertise against the risk of data exposure and the benefit of performance control.

Integration Patterns: Connecting the System of Record to AI Inference

Integrating KingbaseES with external vector stores and document repositories requires robust patterns to ensure real-time access and security. The goal is to provide the AI inference layer with the authoritative source of truth without compromising data integrity.

The integration process involves several steps:

1. Data Ingestion Pipeline: Extract structured data from KingbaseES. Use change data capture (CDC) or scheduled jobs to identify updates.
2. Embedding Generation: Send unstructured data and extracted metadata to the embedding model. Generate vector representations.
3. Vector Store Population: Store the embeddings in the dedicated vector store. Link them to the primary keys in KingbaseES for later correlation.
4. Query Orchestration: When a user query arrives, the orchestration layer first queries KingbaseES for relevant structured context. It then sends the query to the vector store for semantic retrieval.
5. Result Aggregation: Combine the structured context and vector results. Feed them into the LLM for inference.

This pattern ensures that KingbaseES remains the single source of truth for structured data. It prevents data silos by keeping the transactional data in its native, high-integrity environment. The vector store acts as a retrieval index, not a system of record.

To maintain reliability, use high-availability architectures for KingbaseES. Primary-standby clusters provide automatic failover. Read-write separation clusters allow analytical queries to run without impacting transactional performance. This ensures that the AI inference layer always has access to fresh, consistent data.

Acceptance Criteria: Validating Resilience and Compliance

Before deploying an on-premises AI data infrastructure, enterprises must define measurable acceptance criteria. These criteria ensure that the system meets strict data residency, latency SLAs, and commercial support requirements.

The following checklist provides a framework for validation:

• Data Residency Compliance: Verify that KingbaseES instances and vector stores are physically located within the enterprise’s designated data centers. Confirm that no data is replicated to external cloud regions.
• Latency SLA: Measure the end-to-end latency for RAG queries. Ensure that the time to retrieve structured context from KingbaseES and vector embeddings from the store meets the application requirement.
• High Availability: Test failover scenarios for KingbaseES clusters. Confirm that primary-standby or multi-active configurations restore service within the defined RTO (Recovery Time Objective).
• Security Auditing: Review access logs for KingbaseES. Ensure that all queries are logged and that RBAC policies prevent unauthorized access to sensitive data.
• Commercial Support Verification: Confirm that the commercial support contract covers critical issues with a defined response time. Ensure that the support team understands the enterprise’s on-premises environment.
• Integration Stability: Validate that the integration pattern between KingbaseES and the vector store handles data updates correctly. Ensure that stale vectors are cleaned up when source data changes.

Meeting these criteria ensures that the infrastructure is not only functional but also compliant and resilient. It provides the enterprise with confidence that the AI system will perform reliably under production loads.

FAQ

How does on-premises AI data infrastructure balance data sovereignty requirements with hardware procurement complexities?

Enterprises balance these by deploying commercial databases like KingbaseES on supported local hardware (Intel, AMD, or domestic CPUs like Hygon/Loongson) and operating systems (Kylin, UOS). This ensures physical data residency while leveraging available procurement channels. The federated architecture keeps transactional data in the compliant System of Record, while vector stores handle the AI workload locally.

What are the limitations of on-premises vector databases for RAG and LLM inference compared to cloud services?

On-premises vector databases require significant upfront hardware investment and internal expertise for maintenance. They do not offer the elastic scalability of cloud services. However, they provide lower latency for local inference and full control over data sovereignty. KingbaseES does not replace these vector stores; it provides the structured data governance that complements them.

How to calculate TCO for on-premises GPU clusters versus cloud OpEx for AI training?

Calculate TCO by summing hardware costs, software licenses (such as KingbaseES), and internal labor for maintenance over a 3-5 year period. Compare this to cloud OpEx, which includes data transfer fees, compute instance hours, and storage costs. On-premises is often more cost-effective for stable, high-volume workloads, while cloud is better for variable or bursty demands. Consider the value of data sovereignty and latency reduction in the on-premises calculation.

💡 More Resources

If you would like to dive deeper into KingbaseES and its application practices across various industries, we have compiled the following official resources to help you get started quickly and develop and operate with efficiency:

• Kingbase Community: A one-stop interactive platform for technical exchanges, Q&A, and experience sharing—join forces with fellow DBAs and developers.
• Kingbase Solutions: One-stop full-stack database migration and cloud-native solutions, supporting smooth migration of multi-source heterogeneous data, ensuring high availability, real-time integration, and sustained high performance.
• Kingbase Case Studies: Real-world user scenarios and implementation outcomes, showcasing KingbaseES’s outstanding capabilities in high availability, high performance, and IT adaptation.
• Kingbase Documentation: Authoritative and comprehensive product manuals and technical guides, covering the entire lifecycle from installation and deployment to development, programming, and operations management.
• Free Download: Get the latest installation packages, drivers, tools, and patches, supporting multiple platforms and domestic chip architectures.
• Digital Construction Encyclopedia: Covers digital strategy planning, data integration, metrics management, database visualization applications, and more to empower enterprise digital transformation.

Open Source Resources:

• GitHub – Kingbase-docs: Kingbase documentation open-source repository—Stars and contributions are welcome.
• Gitee – Kingbase-docs: Domestic mirror repository for Kingbase documentation for faster access.

Welcome to explore the resources above and begin your Kingbase journey!