Inflection AI, in collaboration with leading AI analyst firm GAI Insights, has released a new report that details the costs vs. benefits of different AI deployment strategies for enterprises. The study reveals that relying solely on cloud-based AI inference can lead to higher costs, IP security risks, and vendor lock-in; while companies that bring inference in-house instead of buying from a provider gain significant financial and strategic advantages that compound as AI initiatives scale.
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“Rising compute demands and token consumption are reshaping the economics of AI”
The report forecasts that GenAI workloads will drive up compute costs by 89% from 2023 to 2025, and each AI agent deployed will require 300,000 to 500,000 tokens or more. These statistics showcase that choice of AI infrastructure is a critical decision for enterprises of all sizes.
Key findings of the report include:
- The cost of AI inference is easily offset by the productivity gains it enables – A 45,000-person call center could save $30 million annually with just a 1% productivity gain. Critically, these cost-savings lower the cost of innovation and enable organizations to rapidly scale their services while maintaining a high quality. In one case study, a car insurance company used GenAI to automate 89% of customer inquiries, boosting satisfaction while generating $4 million in annual ROI—allowing the company to triple in size without hiring additional customer service staff.
- On-premise beats cloud, immediately and at scale – In a head-to-head comparison, inference costs of self-hosted models are consistently lower than those hosted in third-party clouds (GPT4o on Azure, and Llama 3.1 405B on AWS). For large call centers, cost savings range from $200K in the first year of deployment to nearly $1.2M in year three. Similarly, for a banker use case, on-premise enterprise deployments save about $500K in year one and up to $2.3M in savings by year three.
- Legal & IP Risks Are Growing – Lesser-known challenges with cloud-hosted models include that data must be unencrypted to be used in the model itself, risking the exposure of proprietary data and cognitive capital. Additionally, the current wave of copyright and IP lawsuits against major LLMs could open up firms that are adopting these models to unintended legal consequences. By bringing AI inference in-house, organizations can better control their proprietary data, mitigate IP risks, and ensure compliance with evolving regulations; protecting their long-term AI investments.
“As businesses increasingly rely on generative AI to drive innovation, the ability to manage infrastructure in-house becomes a strategic imperative,” said Ted Shelton, COO of Inflection AI. “On-premise AI capabilities empower organizations to control costs, safeguard intellectual property, and innovate at their own pace, ensuring they stay ahead in a rapidly evolving landscape.”
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The report’s findings are particularly important for enterprises whose future growth and operations will be deeply tied to generative AI—what it categorizes as “innovators” and “strategic users”—as the advantages of on-premise AI deployments accumulate over time.
“Rising compute demands and token consumption are reshaping the economics of AI,” said John Sviokla, co-founder of GAI Insights. “For sophisticated users of generative AI, owning the infrastructure offers financial predictability and operational resilience, providing a clear competitive edge in managing the growing complexity of AI workloads.”
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