Cost Structure Improvement and TCO Reduction
KGNN on IBM Power 11 delivers a more predictable, lower TCO by:
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Utilizing existing Power 11 assets and Matrix Math Accelerators to eliminate the need for external GPUs, cutting compute hardware and licensing/cooling costs.image.jpg
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Enabling automated, schema-less graph ingestion with Auto-ETL, reducing labor by up to 80%, and converting expensive manual data engineering into efficient automated processes.image.jpg
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Maximizing Power core utilization for optimized per-core licensing, far better than generic cloud-optimized solutions that underuse hardware, inflating costs.image.jpg
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Integrating built-in knowledge graph context for automated data lineage/auditing, removing the need for costly third-party governance tools.image.jpg
Performance and Capabilities Comparison
Equitus KGNN natively built on Power11 stands out in several key areas:
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Delivers automatic, zero-ETL data ingest for both structured and unstructured data, compared to complex, manual modeling required by competitors.image.jpg
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Executes AI workloads directly on MMA hardware, avoiding cloud GPU sprawl and keeping inference local and very fast.image.jpg
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Ensures regulatory compliance and data sovereignty with on-prem/hybrid architecture and no cloud dependencies, which is critical for finance, health, and government clients.image.jpg
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Offers real-time, optimized relationship query performance for tasks like fraud and risk analysis, outperforming data warehouses and generic graph platforms on connected data workloads.image.jpg
Value Proposition for Regulated Industries
For regulated industries, KGNN’s IBM Power11 platform delivers:
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Data Control: Secure, reliable, auditable environment with no cloud data residency risks.image.jpg
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Accelerated AI: Drastically reduces manual prep time, enabling rapid operationalization of models like RAG for internal knowledge.image.jpg
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Compute Optimization: Leverages licensed Power11 cores for AI, avoiding unpredictable cloud/GPU expenses and reducing overall compute costs.image.jpg
This combined TCO and performance advantage is uniquely suited for banks, government, and healthcare organizations seeking to de-risk AI deployment and maintain complete data sovereignty.image.jpg
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