Balancing AI Innovation and Tech Debt in the Cloud
With meticulous governance, organizations can harness the power of AI while maintaining a secure, compliant and cost-effective cloud environment.
What is the impact of AI on cloud costs?
The recent Datadog 'State of Cloud Costs' 2024 report indicates a 40% increase in spending on GPU instances as organizations experiment with AI. Currently, spending on GPU instances accounts for 14% of compute costs, reflecting the significant resource demands driven by AI innovations.
How can organizations balance AI innovation with governance?
Organizations need to develop strategies that integrate AI innovations with robust governance frameworks. This includes leveraging AI-aware tools that provide visibility and control over AI resources, ensuring adherence to data privacy, compliance checks, and cost management policies to maintain a secure and efficient cloud environment.
What challenges does AI present for code quality?
Research indicates that AI-generated code quality may be below average, as much of the available code for AI modeling comes from early projects or open-source repositories that lack commercial use. This highlights the need for experienced developers to oversee AI-assisted coding to ensure high-quality outcomes.
Balancing AI Innovation and Tech Debt in the Cloud
published by iTech DMV Solutions
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