Enterprise AI Data Infrastructure_ Evaluation Criteria

Decision Framework for Enterprise AI Data Infrastructure

Selecting an enterprise ai data infrastructure requires a structured evaluation of workload isolation, migration capabilities, and security governance. This guide provides a transparent framework to help buyers narrow candidates, define Proof of Concept (PoC) tests, and assess Total Cost of Ownership (TCO).

1. Workload and Stakeholder Requirements

Before evaluating technical criteria, define the primary workload and stakeholder priorities.

Primary Workload Definition:

• Transactional System of Record: The infrastructure must primarily support Online Transactional Processing (OLTP) with strict consistency.
• Vector Workload: The infrastructure must support vector retrieval for Retrieval-Augmented Generation (RAG) applications, specifically handling embeddings, similarity searches, and metadata filtering.
• Hybrid Access: The system must allow unified storage and mixed access to relational, document, full-text, GIS, and time-series data without requiring external network hops for basic vector retrieval.

Stakeholder Matrix:

2. Evaluation Criteria and Weights

The following criteria are weighted to reflect their impact on enterprise viability.

3. Disqualifiers

Candidates are disqualified if they fail to meet the following mandatory requirements:

• No Kernel-Level Vector Support: Reliance on external vector stores for core RAG functionality, introducing network latency and data consistency risks.
• No Automated Migration Tool: Lack of tools to assess and convert SQL/PLSQL scripts from legacy databases, leading to high refactoring costs.
• No RLS for Vector Data: Inability to enforce row-level security on vector search results, posing a security risk for PII.
• No High Availability Testing: Absence of solutions to validate architectural configurations under failure conditions.

4. Technical Deep Dive: RAG and Vector Capabilities

When evaluating an enterprise ai data infrastructure for RAG applications, verify the following specific capabilities:

• Embedding Management: The system must support the storage and retrieval of high-dimensional vector embeddings. Verify support for multimodal embedding models (e.g., text-image search) if required by use cases.
• Index Freshness: Ensure the database can handle frequent updates to vector indices as new data is ingested, without locking transactional tables.
• Metadata Filtering: Verify that metadata filtering is enforced by the database engine, not just the application layer, to ensure security and performance.
• Retrieval Latency: Measure latency under high concurrency. The system should support millisecond-level responses for vector queries while maintaining OLTP integrity.
• Integrations: Confirm compatibility with common LLM frameworks (e.g., LangChain, LlamaIndex) for seamless embedding generation and retrieval.

5. Migration and Compatibility

Migration from legacy systems is a high-risk activity. Evaluate the vendor’s migration tooling based on:

1. Assessment Accuracy: Does the tool provide a detailed gap analysis?
2. Automated Conversion: Can the tool automatically generate SQL/PLSQL scripts?
3. Compatibility Modes: Does the target database support compatibility modes (e.g., Oracle, MySQL, PG) to minimize refactoring?

KingbaseES offers the KDMS migration tool, which uses a "Cloud + Terminal + Service" architecture to provide automated assessment, syntax conversion, and script generation. It supports migration from DB2, Oracle, SQL Server, and MySQL.

6. TCO Analysis

Total Cost of Ownership (TCO) includes licensing, migration, and operational costs.

7. Proof of Concept (PoC) Tests

Define specific PoC tests to validate performance and integration.

• Concurrent Load Test: Run high-concurrency OLTP transactions alongside vector similarity searches. Measure latency spikes and throughput degradation.
• Recall Accuracy: Verify that vector search results are accurate and consistent with the underlying relational data.
• Integration Test: Test integration with common LLM frameworks (e.g., LangChain, LlamaIndex) to ensure seamless embedding generation and retrieval.
• Failover Test: Simulate node failures to verify high availability and data consistency recovery.

KingbaseES provides high availability testing solutions to help validate these scenarios. These solutions allow you to verify different architectural configurations and ensure business continuity under failure conditions.

8. Local Support Verification

For critical infrastructure, verify the vendor’s local support capabilities, including response SLAs, local engineering expertise, and data center presence. While specific local offices or certifications should be verified with the vendor, the availability of local support is crucial for rapid incident resolution and compliance with data sovereignty requirements.

FAQ

What are the disqualifying factors for an enterprise data infrastructure that cannot handle concurrent vector and transactional loads?

Disqualifying factors include significant latency spikes when vector scans occur during high OLTP traffic, data inconsistency between the vector index and the source of truth, and the inability to enforce row-level security on vector search results. If the architecture requires external vector stores for critical workloads, it may also be disqualified due to increased complexity and network latency.

How should TCO be calculated when factoring in licensing, hardware, and ongoing AI model retraining data needs?

TCO should include software licensing, hardware costs for memory-intensive vector workloads, migration costs (tools and labor), and operational staff time. Crucially, include the cost of embedding regeneration and index updates as AI models evolve. A unified platform may have higher licensing but lower operational and migration costs.

What specific PoC tests verify low-latency query response for AI agents under high concurrency?

Run a mixed workload test where 70% of queries are OLTP transactions and 30% are vector similarity searches. Measure the 99th percentile latency for both. Ensure that vector query latency does not degrade by more than 10-20% under peak OLTP load. Also, test the system’s ability to handle rapid embedding updates without locking transactional tables.

Which security certifications and data governance features are mandatory for enterprise AI deployments?

Mandatory features include Row-Level Security (RLS) for vector data, Transparent Data Encryption (TDE), and comprehensive audit logging. While specific regulatory certifications depend on the industry, the infrastructure must support role-based access control (RBAC) and data masking. Ensure that vector embeddings do not expose sensitive PII through metadata leakage.

How does the vendor’s support model in Malaysia impact the decision for critical infrastructure?

💡 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!