Optimize data for AI inference processing
"Inference will exceed training as dominant AI computer demand."
McKenzie, 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
In healthcare, AI inference cost savings are less about reducing model runtime fees alone and more about reducing the volume, complexity, and redundancy of inference calls through better data quality, identity resolution, governance, and workflow integration.
The Gaine Healthcare HDM reduces inference costs by ensuring AI models operate on trusted, normalized, aggregated data instead of fragmented operational feeds.
THE SOLUTION
A healthcare HDMP foundation
Organizations deploying a healthcare HDMP foundation 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 compute consumption
30–70% reduction in redundant AI calls & reprocessing
25–50% lower operational AI cost per decision
2–5× improvement in decision efficiency (same compute → more value)
Controlling the Hidden Costs of AI Inference
The biggest inference costs will not simply correlate with model size — they will correlate with:
- decision frequency
- latency sensitivity
- data complexity & multimodality
- auditability & compliance requirements
- edge vs centralized deployment
- continuous operational integration
For healthcare data ecosystems in particular, inference costs can be dramatically reduced by strong data governance, identity resolution, and operational data control planes — because poor data multiplies compute demand.
INSIGHTS
GET STARTED
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