AI COST REDUCTION
Optimize data for AI inference processing
"Inference will exceed training as dominant AI computer demand."
McKenzie & Company, Dec. 2025
By 2030, AI inference is expected to make up 40% of data center demand. Optimizing data for inference processing is fast becoming a key differentiator in healthcare and life sciences.
THE CHALLENGE
Reducing AI inference costs is not just about lowering model runtime fees. The greatest savings come from reducing the number, complexity, and redundancy of AI decisions by improving data quality, resolving identities across systems, and integrating AI into operational workflows.
The Gaine HDMP Health Data Management Platform reduces inference costs by providing AI models with mastered, normalized, and aggregated data. By eliminating fragmented records and consolidating information from multiple operational systems into trusted profiles, AI models can run fewer, faster, and more accurate inference cycles − dramatically reducing compute requirements and processing times.
THE SOLUTION
A data foundation for healthcare
By deploying a healthcare HDMP foundation, your organization can expect significant savings from eliminating duplicate inference, improving feature reliability, and enabling targeted decisioning rather than brute-force model execution.
20–60% reduction in AI inference costs
30–70% reduction in redundant AI calls & reprocessing
Simplified AI calculations improve reliability and trust
Controlling the hidden costs of AI inference
AI inference costs are driven less by model size and more by how often decisions run, how complex the data is integrated into operational workflows. By providing mastered, governed, and unified data, Gaine HDMP enables AI to run fewer, faster inference cycles − dramatically reducing compute demand and cost.
Fragmented data dramatically increases inference costs. When identities are unresolved and data is scattered across systems, AI must process more information and rerun decisions repeatedly.
Strong data governance identity resolution, and an operational data control plane change this dynamic − allowing AI to operate on trusted, unified data and dramatically reducing compute demand.
INSIGHTS
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