- Report reveals 40% increase in spending on GPU instances for AI experimentation.
- GPUs crucial for AI tasks, outperforming CPUs by over 200%.
- Significant inefficiencies noted in container resource management.
- 83% of container costs attributed to idle resources, impacting cost efficiency.
- Continued use of outdated technologies despite advancements in cloud offerings.
- Decline in organizations leveraging cloud service discounts.
- Rise in adoption of Arm-based instances for energy efficiency and cost savings.
Main AI News:
Datadog, the leading monitoring and security platform for cloud applications, has unveiled its latest report, the State of Cloud Costs 2024. Notably, the report highlights a significant 40% increase in spending on graphics processing unit (GPU) instances among organizations over the past year. This surge underscores a growing trend where companies are increasingly exploring artificial intelligence (AI) and large language models (LLMs).
The enhanced parallel processing capabilities of GPUs are pivotal for tasks such as training LLMs and executing AI workloads, delivering speeds that can surpass traditional CPUs by more than 200%. According to Yrieix Garnier, VP of Product at Datadog, “The predominant use of cost-effective GPU-based instances indicates widespread experimentation with AI, focusing on adaptive AI, machine learning inference, and initial training phases.”
Looking ahead, as organizations scale their AI initiatives from experimentation to production, there is an anticipated shift towards more advanced and consequently more expensive GPU instances. This evolution is expected to drive a larger allocation of cloud compute budgets towards GPU technologies.
In addition to AI-related expenditures, the report identifies container management as a significant area of inefficiency for many organizations. A striking 83% of container costs were attributed to idle resources, with overprovisioning of cluster infrastructure accounting for 54% of wasted spend. Meanwhile, 29% of the inefficiencies stemmed from workload idle, where resource allocation exceeded actual requirements.
Further insights from the report underscore ongoing trends in cloud adoption:
- Legacy Technology Usage: Despite advancements in cloud infrastructure by providers like AWS, a substantial 83% of organizations continue to allocate 17% of their EC2 budgets to previous-generation technologies.
- Discount Utilization: While cloud service providers offer discounts for commitment-based usage, only 67% of organizations are currently leveraging these programs, down from 72% the previous year.
- Rise of Green Technology: Organizations embracing Arm-based instances now allocate 18% of their EC2 compute budget to these technologies, double the allocation from a year ago. Arm-based instance types are noted for their energy efficiency, consuming up to 60% less energy compared to traditional EC2s while often delivering superior performance at reduced costs.
Datadog’s comprehensive analysis, based on data from numerous AWS cloud users, sheds light on the evolving landscape of cloud computing costs. It examines how organizations’ adoption of emerging technologies, alongside their management of legacy systems and participation in cost-saving initiatives, collectively shape their cloud expenditure strategies.
Conclusion:
Datadog’s 2024 report underscores a pronounced shift towards GPU instances driven by AI exploration, despite prevalent inefficiencies in container resource management and underutilization of cost-saving initiatives. This highlights a market trend towards greater investment in advanced technologies for enhanced performance and efficiency in cloud computing.
