AI Data Platform_ Evaluation Criteria for Hybrid Retrieval

AI Data Platform: Evaluation Criteria for Hybrid Retrieval

Enterprise data architects in Malaysia are increasingly moving beyond generic data lakes for AI workloads. The core problem is not storage capacity, but the architectural mismatch between transactional integrity and AI retrieval latency. A "Frankenstein stack"—where data lakes, vector databases, and orchestration layers operate in silos—creates high total cost of ownership (TCO) and governance risks.

A robust ai data platform strategy requires decoupling the transactional system of record from the vector retrieval layer. This separation ensures that the system of record maintains ACID compliance for business logic, while the retrieval layer optimizes for low-latency semantic search.

1. Workload and Stakeholder Requirements

Before evaluating specific vendors, stakeholders must define the workload characteristics and decision trade-offs. This section establishes the baseline requirements for Malaysian enterprises, addressing data residency nuances and the need for clear separation of concerns.

Workload Definition

Transactional Volume: Define the expected OLTP throughput (transactions per second) for the system of record.
Vector Scale: Estimate the number of vectors, dimensionality, and update frequency (inserts/updates per hour).
Query Patterns: Determine the ratio of read-heavy AI queries to write-heavy transactional updates.

Stakeholder Perspectives and Trade-offs

Data Architects: Prioritize scalability and low-latency retrieval. They must balance the complexity of managing separate vector and transactional stores against the performance benefits of decoupling.
Security Officers: Prioritize row-level security (RLS) and access control. The trade-off involves ensuring that metadata filtering is enforced at the point of retrieval without degrading performance.
Procurement Leads: Prioritize TCO and licensing transparency. They must distinguish between vector storage costs and traditional transactional costs to avoid hidden expenses.

Malaysian Context: Data Residency and Compliance

While Malaysia’s Personal Data Protection Act (PDPA) governs data privacy, it does not create a blanket data-residency mandate requiring all data to reside locally. However, enterprises must ensure that their ai data platform architecture supports flexible data placement strategies to comply with specific industry regulations or internal governance policies.

2. Evaluation Criteria and Decision Framework

This section provides a transparent evaluation framework. Criteria are weighted based on stakeholder priorities, and disqualifiers are defined to eliminate vendors that cannot meet core requirements.

Evaluation Criteria and Weights

Criterion	Weight	Description	Evidence Needed
Architectural Fit	40%	Clear separation of transactional (OLTP) and vector retrieval layers.	Architecture diagrams showing distinct components for OLTP, Vector, and Orchestration.
Security & Access Control	30%	Support for metadata filtering integrated with enterprise IAM.	Documentation proving integration with IAM tools for dynamic access control.
Performance & Latency	20%	Hybrid retrieval latency and accuracy under concurrent load.	Technical benchmarks showing P95/P99 latency and retrieval accuracy (MRR/NDCG).
TCO & Licensing	10%	Transparent licensing models distinguishing vector storage from transactional costs.	Licensing terms that clearly separate costs for vector storage and embedding compute.

Disqualifiers

Opaque Architecture: Vendors that cannot provide clear architectural diagrams showing the separation of concerns (OLTP vs. Vector vs. Orchestration).
Lack of Security Evidence: Vendors who cannot demonstrate metadata filtering integration with enterprise IAM.
Unsupported Claims: Vendors claiming native vector search, embedding generation, or RAG orchestration capabilities without explicit product evidence.

3. Proof of Concept (PoC) and Procurement Guidance

Procurement leads and data architects must validate vendor claims through rigorous PoC tests. The following guidance is workload-specific and operationally actionable.

PoC Test Plan

Baseline Setup: Deploy the candidate platform with a representative dataset (e.g., 1M documents, 1536-dimensional vectors).
Concurrency Test: Simulate 100-500 concurrent users issuing hybrid search queries.
Latency Measurement: Record P95 and P99 latency for retrieval. Target: <100ms for 95% of queries.
Accuracy Test: Use a standard benchmark dataset (e.g., MS MARCO) to measure Mean Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain (NDCG).
Index Freshness: Insert new documents and measure the time until they are searchable. Target: <5 seconds for real-time updates.

Procurement Checklist

Licensing Transparency: Demand transparent licensing models that distinguish between vector storage costs and traditional transactional costs.
Security Integration: Verify that the platform integrates with existing IAM systems for metadata filtering.
Support SLAs: Check for vendor support SLAs specific to AI/vector issues.

4. KingbaseES: Role in the AI Data Platform

KingbaseES is a commercial database software developed by Kingbase (电科金仓). It is essential to distinguish its role as a transactional system of record from the vector retrieval layer.

Verified Capabilities

Transactional Integrity: KingbaseES excels as a system of record, supporting multi-mode compatibility (Oracle, MySQL, SQL Server, PG) to facilitate data migration.
Security: It supports transparent data encryption with a performance loss of only 3% and national cryptographic (Guomi) identity authentication.
Performance: KingbaseES V9 features a self-developed optimizer with intelligent execution plans to improve complex query efficiency.
High Availability: It supports high availability testing and online cluster expansion without business interruption.
Case Evidence: KingbaseES V8.0 was used in the Qingdao National Health Information Platform project, achieving ‘Level 4 Grade A’ interoperability and full localization (Xinchuang) compliance.

Limitations and Verification

No Native Vector/RAG Claims: There is no evidence that KingbaseES provides native vector search, embedding generation, RAG orchestration, or hybrid retrieval performance benchmarks.
No Local Malaysian Presence: There is no evidence of KingbaseES having local engineering support, local data centers, or local regulatory approvals in Malaysia.
Commercial Software: KingbaseES is commercial software; it is not open-source or source-available.
Integration Verification: Claims regarding integration with enterprise IAM/Governance tools for metadata filtering require specific vendor documentation.

KingbaseES should be considered as part of the broader ai data platform architecture, specifically for the transactional layer, while specialized vector databases may be needed for the retrieval layer.

FAQ

What specific PoC tests validate retrieval accuracy and latency for hybrid search?

Run concurrency tests with 100-500 users, measuring P95/P99 latency (<100ms) and using standard benchmarks (MS MARCO) for MRR/NDCG accuracy. Test index freshness by measuring time-to-searchability for new documents.

How do we weight vector search capabilities against general SQL performance?

Assign weights based on stakeholder priorities: 40% for Architects (performance), 30% for Security (access control), and 30% for Procurement (TCO). Evaluate vector capabilities separately from SQL performance, as they often require different infrastructure.

What are the disqualifiers for AI data platforms in enterprise security compliance?

Disqualifiers include lack of native metadata filtering, no IAM integration, opaque licensing, and inability to provide audit logs for retrieval operations.

How is total cost of ownership calculated for vector embedding storage?

Include licensing for vector storage, compute for embedding generation, operational overhead for index maintenance, and storage costs for data duplication in decoupled architectures.

What evidence is needed to verify vendor support for AI workloads?

Request technical documentation for vector index structures, third-party benchmarks, clear licensing terms distinguishing vector costs, and SLAs for AI-specific support issues.

💡 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!