New Study Finds 93% of Enterprise Platform Teams Face Major Challenges With Cloud Cost Management, Kubernetes Complexity and Boosting Developer Productivity
13 Agosto 2024 - 8:34AM
Business Wire
Enterprises Focus on Environment
Standardization, Cost Control and Developer Self-service to Tackle
Kubernetes Complexity and Challenges in Operationalizing AI
Rafay Systems, the leading Platform-as-a-Service (PaaS) provider
for modern infrastructure and accelerated computing, today
announced the availability of its inaugural survey, revealing that
more than 9 in 10 (93%) of platform teams face persistent
challenges. Top challenges include managing Kubernetes complexity,
keeping Kubernetes and cloud costs low, and boosting developer
productivity. The study, titled “The Pulse of Enterprise Platform
Teams: Cloud, Kubernetes and AI,” analyzes challenges faced by
platform engineering teams across the enterprise segment. To
address these hurdles, organizations are emphasizing environment
standardization, cost control and improved developer experiences,
with a growing focus on automation and self-service solutions. The
self-service trend extends to AI adoption, where a majority of
respondents believe pre-configured AI workspaces with built-in
machine learning operations (MLOps) and large language model
operations (LLMOps) tooling could potentially unlock $1.4 million
in productivity gains for an organization with 100 developers.
Kubernetes and Infrastructure Cost and Complexity are
Pervasive Challenges
Despite widespread adoption of platform teams within IT
organizations, survey respondents across the board confirmed that
these teams are stretched to their limits managing complex
multi-cluster Kubernetes and cloud environments. Top challenges
organizations have experienced or are currently experiencing as it
relates to Kubernetes include:
- Managing cost visibility and controlling Kubernetes and cloud
infrastructure costs - 45%
- Complexity of keeping up with Kubernetes cluster lifecycle
management with multiple, disparate tools - 38%
- Establishment and upkeep of enterprise-wide standardization -
38%
As Kubernetetes and cloud environments usage grows,
organizations have also been inundated with a significant uptick in
the cost and resources required to manage Kubernetes clusters.
Nearly one-third (31%) state that the total cost of ownership
(including software/support licenses, salaries of resources) is
higher than budgeted for or anticipated. Looking ahead, 60% report
that reducing and optimizing costs associated with Kubernetes
infrastructure remains a top management initiative in the next
year.
AI and GenAI: A New Frontier Mirroring Kubernetes Adoption
Challenges
Organizations investing in AI and generative AI (GenAI)
capabilities face challenges similar to those encountered during
their Kubernetes adoption journey. The vast majority of respondents
recognize the critical importance of efficient development and
deployment methods, with 96% emphasizing this need for AI
applications and 94% for GenAI applications.
Despite this, the study reveals that less than a quarter of
organizations are at a sufficient level of implementation for both
MLOps (17%) and LLMOps (16%). This nascent stage is reflected in
the widespread difficulties faced:
- 95% of teams with MLOps implementations report challenges in
experimenting with and deploying AI apps
- 94% struggle with GenAI app experimentation and deployment
Organizations are prioritizing key capabilities for their AI
initiatives to overcome these obstacles. The top five features
include pre-configured environments for developing and testing
generative AI applications; automatic allocation of AI workloads to
appropriate GPU resources; pre-built MLOps pipelines; GPU
virtualization and sharing; and dynamic GPU matchmaking. These
capabilities aim to streamline development, optimize resource
utilization and manage costs effectively.
As with their critical function in cloud and Kubernetes
technologies, platform teams are poised to play a pivotal role in
eliminating persistent challenges to advance adoption and
implementation. Respondents identified the following top
responsibilities for platform teams to assist in the development of
AI and GenAI applications:
- Security for MLOps and LLMOps workflows - 50%
- Model deployment automation - 49%
- Data pipeline management - 45%
AI Surge and Kubernetes Expansion Drive Demand for
Self-service and Automation in Data Scientist and Developer
Workflows
Organizations are prioritizing the developer experience with a
growing emphasis on automation and self-service, spanning both AI
initiatives and Kubernetes deployments. Respondents identified the
following priorities to enhance developer productivity in the
expanding Kubernetes ecosystem:
- Automating cluster provisioning - 47%
- Standardizing and automating infrastructure - 44%
- Providing self-service experiences for developers - 44%
- Automating Kubernetes cluster lifecycle management (Day 2) -
44%
- Reducing cognitive load on developer team(s) - 37%
Substantial productivity gains are also expected through
improved developer experiences in AI projects — 83% surveyed
believe pre-configured AI workspaces with built-in MLOps and LLMOps
tooling could save teams over 10% of time monthly. For example, in
an organization with 100 developers earning an average salary of
$140,000*, adopting self-service AI workspaces could unlock nearly
20,000 hours of developer time annually. This is equivalent to $1.4
million in salary costs or the productivity gain of nine additional
full-time developers — without increasing headcount.
“The survey’s findings confirm the platform engineering trend
Team Rafay has highlighted previously: platform teams are now
decidedly in the driver’s seat when it comes to major tooling and
architectural decisions for compute consumption,” said Haseeb
Budhani, CEO and co-founder of Rafay Systems. “It’s also clear from
the survey that these teams are grappling with ever-increasing
costs and complexity. Success for these teams will hinge on them
being empowered with the right tools and strategies to optimize
resources, standardize processes and drive innovation.
Organizations that actively support their platform teams in
addressing these challenges are best positioned to thrive in an
increasingly competitive and technology-driven business
landscape.”
Research Methodology
Demographic: More than 2,000 platform engineering, platform
architecture, cloud architecture, cloud engineering, developer,
DevOps, site reliability engineering (SRE) and operations
professionals with roles ranging from C-level to team members at
U.S. organizations with over 1,000 employees.
Process: The research was conducted in two parts — more than
1,000 professionals were surveyed to understand the intricate
challenges organizations face with managing Kubernetes environments
and cloud infrastructure. A second survey was conducted with 1,035
professionals to gather insights on AI and GenAI adoption in the
enterprise.
Download a complimentary copy of Rafay’s full survey report.
Additional Resources
- Sign up for a demo of Rafay’s enterprise PaaS for modern
infrastructure here
- Follow Rafay on X and LinkedIn
- Read the Rafay Blog: The Kubernetes Current
About Rafay Systems
Rafay’s mission is to liberate enterprises from the pains and
complexities of consuming modern compute infrastructure, allowing
them to channel 100% of their developers’ focus into innovation.
Companies such as MoneyGram, Guardant Health and MassMutual entrust
Rafayto be the cornerstone of their modern infrastructure strategy
and AI architecture. With Rafay, platform teams at these companies
enable developers and data scientists to access compute and AI
resources in record time, complete with essential guardrails for
security and governance. Gartner has recognized Rafay as a Cool
Vendor in Container Management and GigaOm named Rafay as a Leader
and Outperformer in the GigaOm Radar Report for Managed Kubernetes,
acknowledging our commitment to driving innovation. To join the
ranks of industry leaders who have unlocked the true potential of
cloud-native computing with Rafay Systems, please visit
www.rafay.co.
*“Software Engineer Salary in the US,” Built In,
https://builtin.com/salaries/dev-engineer/software-engineer
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version on businesswire.com: https://www.businesswire.com/news/home/20240813978920/en/
Audrey Briers Bhava Communications for Rafay rafay@bhavacom.com
858-522-0898