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Serverless containers continue to rise in popularity — 46% of container organizations now run serverless containers, up from 31% two years ago — as teams look to improve developer productivity, according to 10 Insights on Real-World Container Use, a report from Datadog.
Other key findings from the report include:
■ The adoption of Arm processor-based compute for containerized workloads has more than doubled over the past year.
■ Sizing workloads remains a challenge for organizations as more than 65% of Kubernetes workloads are utilizing less than half of their requested CPU and memory.
■ Usage of GPU-based compute on containerized workloads — which is used to efficiently train machine learning and large language models (LLMs), perform inferences and process large datasets — has increased 58% year-over-year.
■ Databases and web servers are leading workloads for containers — more than 41% of container organizations are hosting databases on containers
■ Organizations with larger container environments are using a service mesh — more than 40% of organizations running more than 1,000 hosts.
■ Users are upgrading to new Kubernetes releases earlie. The report states: "… we've seen a marked increase in the adoption of newer versions of Kubernetes. Forty percent of Kubernetes organizations are using versions (v1.25+) that are approximately a year old or less — a significant improvement compared to 5 percent a year ago."
"We are continuing to see organizations move to serverless containers for the benefits of improving productivity and agility while reducing operational overhead and cloud costs," said Yrieix Garnier, VP of Product at Datadog. "With the serverless approach, organizations don't have to provision or manage the infrastructure needed to run, maintain and scale the containers. This strategy also comes with cost benefits as cloud providers manage the serverless containers and therefore can optimize resource utilization and cloud spend."