The shift to cloud-native environments away from traditional data center infrastructures continues unabated, but security and complexity challenges remain a struggle for DevOps teams.
These were two of the main takeaways in a survey and analyst report published by the IT consultancy firm Flexera.
The survey, based on the responses of 750 IT professionals, revealed that 92% of the organizations represented in the survey have a multi-cloud strategy, 80% have a hybrid cloud strategy and only a minority (45%) have integrated data between cloud environments.
The top challenges, according to the survey results, are:
The solution for these challenges largely involves — in addition to implementing the right DevOps culture and practices — adoption of the right tools and platforms, especially for security. However, first-generation cloud-cost management tools “simply are not cutting it” for modern architectures, said Asim Razzaq, CEO of Yotascale, which offers a cloud resource optimization platform.
“Today’s modern architectures are dynamic, and ownership is transient and not always well-defined. The greatest strength of containers and Kubernetes is the ability to scale on-demand and share cloud resources among multiple applications,” said Razzaq. “Tracking cloud costs in modern architecture requires a strong ability to automate cost attribution — or tag management — for those dynamic resources, and the ability to understand usage at the application level, even for containers that are sharing resource instances.”
First-generation cloud management tools are, in this way, unable to provide the information DevOps teams require to understand how to automate cost attribution for modern architectures, said Razzaq.
“[DevOps teams] can’t break down costs into any level of meaningful detail for engineering and finance teams to digest and act upon,” said Razzaq. “Conversely, newer approaches that are focused on Kubernetes cost management lack the ability to understand and break down the costs of non-cloud-native workloads.”
Indeed, tools are often frustrating to engineering teams because of their static custom reporting, broad and generalized recommendations and no effective workflow for effective collaboration between finance and engineering, Razzaq said.
“The biggest source of frustration is the inability to drill down and manage costs in a way that matches the way that these teams do business. There may be 10 teams, each with five production applications running in the cloud, for a particular account,” Razzaq said. “Engineers need to see, at the application level, what their resource utilization, costs and cost anomalies are in order to make the best cost/performance decisions for their applications. Without that, they’re flying blind; having to defend their cloud usage without any supporting data.”
As an example, a misconfigured Kubernetes cluster can “cause a massive spike in costs,” Razzaq explained. “Without detailed cost and utilization reporting at the team and application level, an engineer can spend weeks digging through spreadsheets and logs trying to track down the source of the anomaly. That’s weeks of their time that they aren’t [using to] be productive on new features.”
Razzaq said there are a number of solutions DevOps teams can adopt to address these issues, including automated cost attribution and tag management, actionable recommendations and comprehensive observability capabilities.
Automated cost attribution and tag management “is the foundation of accurate and detailed cloud cost management,” said Razzaq. “Engineers need tools to help them set tag management policies, identify untagged instances and update tags quickly and easily in their cloud environment,” he said. “With this in place, cloud-native workloads can be automatically tagged as they are dynamically created, ensuring that cloud costs are always clearly attributed to the right teams and applications.”
Actionable recommendations at the cloud-instance level with closed-loop feedback also are an important process “so that engineers can see exactly why the recommendation is being made, and what the cost impact will be of taking that action,” Razzaq said. “The ability to provide feedback on the recommendation and dismiss recommendations that don’t align with business needs ensures that new recommendations are relevant,” Razzaq added.
Finally, tools that provide a unified view of both cloud-native and traditional workloads that have been shifted to the cloud help to ensure “that engineering and finance have a single source of truth for their cloud costs that enables them to make smarter decisions for their business,” Razzaq said.
The data used to train AI models needs to reflect the production environments where applications are deployed.
Looking for a DevOps job? Look at these openings at NBC Universal, BAE, UBS, and other companies with three-letter abbreviations.
Tricentis is adding AI assistants to make it simpler for DevOps teams to create tests.
Redis is taking it in the chops, as both maintainers and customers move to the Valkey Redis fork.
GitLab Duo Chat is a natural language interface which helps generate code, create tests and access code summarizations.