{"id":1918,"date":"2026-10-28T16:07:36","date_gmt":"2026-10-28T16:07:36","guid":{"rendered":"https:\/\/www.kingbaseglobal.com\/blog\/tech-blog\/enterprise-ai-database-requirements-a-fair-evaluation\/"},"modified":"2026-10-28T16:07:36","modified_gmt":"2026-10-28T16:07:36","slug":"enterprise-ai-database-requirements-a-fair-evaluation","status":"publish","type":"post","link":"https:\/\/www.kingbaseglobal.com\/blog\/tech-blog\/enterprise-ai-database-requirements-a-fair-evaluation\/","title":{"rendered":"Enterprise AI Database Requirements_ A Fair Evaluation"},"content":{"rendered":"

\"Selection<\/p>\n

Enterprise AI Database Requirements: A Selection Guide for Malaysia<\/h2>\n

Enterprises in Malaysia face a critical architectural dilemma when integrating Artificial Intelligence (AI) into existing data infrastructure. The tension lies between the speed of specialized vector databases and the governance, security, and transactional integrity of established enterprise-grade relational systems. Buyers struggle to define clear evaluation criteria that distinguish between specialized vector stores and general-purpose commercial databases. This often leads to fears of vendor lock-in, operational complexity, or compromised data governance.<\/p>\n

Successful enterprise AI implementation requires a decoupled architecture. The Transactional System of Record, Vector Retrieval Layer, Document Store, and Orchestration Layer must be defined and evaluated independently. This approach prevents scope creep and ensures that the choice of database software is driven by workload-specific criteria\u2014such as hybrid search precision, Role-Based Access Control (RBAC) granularity, and Total Cost of Ownership (TCO) predictability\u2014rather than marketing claims of "all-in-one" AI solutions.<\/p>\n

Workload and Stakeholder Requirements<\/h2>\n

Before evaluating technical criteria, stakeholders must define the specific workload and organizational requirements. In the Malaysian context, this includes regulatory compliance with the Personal Data Protection Act (PDPA) and operational needs for local support.<\/p>\n

Stakeholder Matrix<\/h3>\n\n\n\n\n\n\n\n\n
Stakeholder<\/th>\nPrimary Concern<\/th>\nKey Requirement<\/th>\n<\/tr>\n<\/thead>\n
CISO \/ Security<\/strong><\/td>\nData Governance & Compliance<\/td>\nFine-grained RBAC at vector level; PDPA compliance for data residency; audit trails.<\/td>\n<\/tr>\n
CTO \/ Architecture<\/strong><\/td>\nPerformance & Scalability<\/td>\nHybrid search precision; low-latency retrieval; index freshness under load.<\/td>\n<\/tr>\n
CFO \/ Procurement<\/strong><\/td>\nTCO & Licensing<\/td>\nTransparent licensing model; local support costs; infrastructure efficiency.<\/td>\n<\/tr>\n
DevOps \/ Engineering<\/strong><\/td>\nOperations & Maintenance<\/td>\nIntegration ease (LangChain\/LlamaIndex); monitoring; migration paths.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Workload Definitions<\/h3>\n
    \n
  1. Transactional System of Record:<\/strong> Requires ACID compliance, strict consistency, and robust RBAC. This is the source of truth for structured business data.<\/li>\n
  2. Vector Retrieval Layer:<\/strong> Requires high-throughput similarity search, low latency, and hybrid search capabilities (semantic + keyword).<\/li>\n
  3. Document Store:<\/strong> Manages unstructured data (PDFs, text chunks) referenced by the vector layer.<\/li>\n
  4. Orchestration Layer:<\/strong> Application logic connecting LLMs to data layers, handling prompt engineering and context assembly.<\/li>\n<\/ol>\n

    Evaluation Criteria and Weighted Scorecard<\/h2>\n

    The following scorecard defines the evaluation criteria, weights, and evidence requirements. Scores are not predetermined; they depend on vendor-provided evidence and PoC results.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n
    Criteria<\/th>\nWeight<\/th>\nDefinition<\/th>\nEvidence Required<\/th>\nDisqualifiers<\/th>\n<\/tr>\n<\/thead>\n
    Hybrid Search<\/strong><\/td>\n20%<\/td>\nCombining semantic vector similarity with keyword-based (BM25) retrieval.<\/td>\nBenchmark data on precision\/recall for hybrid queries.<\/td>\nPure vector search only; no keyword support.<\/td>\n<\/tr>\n
    RBAC & Security<\/strong><\/td>\n20%<\/td>\nGranular access control at vector\/metadata level.<\/td>\nDocumentation of vector-level security; PDPA compliance evidence.<\/td>\nNo RBAC at vector level; no audit logging.<\/td>\n<\/tr>\n
    Performance (Latency)<\/strong><\/td>\n15%<\/td>\nTime to return top-K results under high concurrency.<\/td>\np95\/p99 latency benchmarks under production-like load.<\/td>\nLatency > 100ms at p95 under peak load.<\/td>\n<\/tr>\n
    Index Freshness<\/strong><\/td>\n10%<\/td>\nTime lag between data insertion and search availability.<\/td>\nPoC test: Insert data and measure searchability time.<\/td>\nIndex freshness > 5s under 1000 wps.<\/td>\n<\/tr>\n
    TCO (Licensing)<\/strong><\/td>\n15%<\/td>\nTotal cost including licensing, infrastructure, and support.<\/td>\nDetailed 3-year TCO model; local support SLA costs.<\/td>\nOpaque licensing; no local support option.<\/td>\n<\/tr>\n
    Integrations<\/strong><\/td>\n10%<\/td>\nEase of integration with orchestration frameworks.<\/td>\nDocumentation for LangChain, LlamaIndex, or standard connectors.<\/td>\nNo standard connectors; proprietary lock-in.<\/td>\n<\/tr>\n
    Operational Maturity<\/strong><\/td>\n10%<\/td>\nMonitoring, backup, and lifecycle management.<\/td>\nEvidence of monitoring tools; backup\/restore procedures.<\/td>\nNo automated backup; no monitoring APIs.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    Competitor Naming and Comparison<\/h2>\n

    This section names competitors only if supported by evidence, applying identical criteria to ensure fairness.<\/p>\n

    PostgreSQL with pgvector<\/h3>\n