The author also introduces the concept of "cost-conscious coding" as a proactive approach to managing cloud costs. This involves designing applications for cost, reliability, and security from the outset, rather than trying to control costs reactively once the application is deployed. The author argues that this approach is particularly important for AI, where unoptimized algorithms can consume significant compute and storage resources.
Key takeaways:
- AI is becoming an integral part of cloud services, with companies needing to embrace these capabilities or risk missing out on valuable opportunities.
- Expensive data movement and cloud costs are the real threat, not AI, and businesses need to consider these factors when deciding which vendors and technologies to use.
- Companies are considering leveraging their on-premises infrastructure to avoid moving their data into the cloud, with factors such as compliance, data security, and training frequency and data playing a role in this decision.
- Cost-conscious coding is a more effective approach to cloud optimization, allowing companies to design for cost, reliability, and security in any cloud workload, which is particularly important with AI.