The Hidden Cost of AI Inference in Healthcare
AI costs are driven by your data quality. Learn how health plans can lower inference spend and improve AI accuracy. Download the free white paper.
AI COST REDUCTION
Optimize data for AI inference processing
As AI inference becomes a primary driver of enterprise cloud spend, the quality of your data foundation directly determines what you pay.
Gaine HDMP reduces AI inference costs by giving models clean, unified and governed data − so they spend less time reasoning and more time delivering quality answers.
THE CHALLENGE
By year-end 2027, more than 60% of enterprise AI-related cloud send will be attributable to AI inference workloads, up from less than 20% in 2025."
Gartner, Inc., "AI Inference Creates New IT Cost Exposure," Allison Adams, Nov. 2025.
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Percentage of AI costs attributable to inference by 2027
How do you control AI expenses when they scale with every user request?
By 2030, AI inference will account for 40% of data center demand. Unlike traditional IT investments, inference costs scale with real-time user behavior, creating unpredictable financial exposure for health plans.
You achieve the greatest savings by minimizing the reasoning effort your models must perform. Fragmented data drives up these expenses across three compounding factors:
- The volume of tokens the model must process
- The number of possible paths it must explore
- The depth of reasoning it must apply to reach a confident answer
When your systems hold duplicated member records and unresolved provider identities, you end up paying the AI to perform the organizational work your data architecture should have handled.
THE SOLUTION
A data foundation for healthcare
Gaine HDMP sits between your enterprise source systems and your AI models, providing structured, governed context rather than raw, fragmented data. By eliminating fragmented records and consolidating information from multiple systems into trusted profiles, AI models can run fewer, faster, and more accurate inference cycles to reduce compute requirements and processing costs.
It consolidates provider, member, and claims data into unified profiles, preserving lineage, relationship history, and governance rules.
AI models gain a shorter, clearer path to a trusted answer.
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
Most AI cost conversations focus on model selection or licensing fees. But the costs that accumulate fastest are the ones that are hardest to see: repeated inference cycles triggered by unresolved identities, redundant processing caused by inconsistent data across systems, and compute waste from models that must reason through ambiguity before reaching a usable answer.
According to Gartner, by year-end 2027, 70% of organizations will experience unplanned increases in cloud costs due to the rapid and decentralized adoption of AI inference services.1 Without a governed data foundation in place, every new AI tool your teams adopt adds to that exposure.
Strong data governance, identity resolution, and an operational data control plane change this dynamic — allowing AI to operate on trusted, unified data, reducing the hidden per-decision costs that compound across your enterprise.
WHY THIS MATTERS ACROSS HEALTHCARE
Healthcare and life sciences data environments are among the most complex in any industry. A single member may exist in claims, eligibility, care management, and provider systems under different identifiers. A single provider may be represented inconsistently across dozens of source systems.
When AI models operate across that fragmentation, the inference cost becomes a structural tax on every AI use case your organization pursues. Gaine HDMP was built specifically for this environment. With a cross-domain data model covering over 3,500 attributes across provider, member, claims, and related domains, it provides the kind of governed, connected context that allows AI to reason efficiently and return trustworthy results — at scale.
INSIGHTS
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