The State of Kubernetes 2023: Cloud Cost Remediation Is Top Priority

Pepperdata has released The State of Kubernetes 2023, the results of a survey conducted with 800 C-level execs and senior ITOps and DevOps professionals in industries including financial services, healthcare, technology, and advertising. The survey aimed to gain insights into the types and sizes of containerized applications and other workloads on Kubernetes clusters, as well as the FinOps tools and practices used to optimize Kubernetes deployments and control costs.


Key findings from the survey show:

1. The Kubernetes market is maturing.

Evidence of this includes:

■ The number of clusters that are being deployed has grown to six to ten clusters per organization.

■ The variety and types of workloads that are now being launched by Kubernetes extend beyond microservices to data ingestion, cleansing, and analytics, databases, and artificial intelligence (AI) and machine learning (ML).

■ An emerging challenge has been unexpected infrastructure spend and the growing focus on cost control and optimization.

2. Companies deploying Kubernetes clusters are doing so to stay competitive or ahead of the competition, and need the agility Kubernetes affords them to deploy services rapidly.

3. Cost savings was identified as the top ROI metric for a Kubernetes deployment.

4. Companies are turning to a wide variety of approaches to attempt to remediate infrastructure spend overruns.

"The survey confirms that Kubernetes has become the preferred choice for deploying workloads among agile enterprises. However, the speed and ease of deployment can result in unexpected infrastructure cost overruns. Respondents are increasingly turning to FinOps and cloud cost optimization tools to help them remediate the cost of operating in the cloud and optimize their Kubernetes clusters," said Ash Munshi, CEO, Pepperdata. "Regardless, the survey shows that cost savings is the top metric people are using to measure the ROI of their Kubernetes investments in 2023."

Other findings included:

■ How many? The average number of Kubernetes clusters among those surveyed ranged from three to ten. 32% reported having three to five deployments, while 38% reported six to ten clusters. Almost 15% said they had between 11 and 25 clusters, and four% reported more than 25.

■ What for? When asked what types of applications were being deployed, respondents chose from a list of workloads, with the most popular being data ingestion, cleansing, and analytics, including Apache Spark as the choice for 61% of those polled. Other popular deployments included databases or NoSQL databases such as PostgreSQL, MongoDB, and Redis; and web servers like NGINX.

■ What challenges? The benefits of Kubernetes are many, but companies also find that getting a Kubernetes project off the ground comes with many challenges. These include significant or unexpected spend on compute, storage, networking infrastructure, and/or cloud-based IaaS; a steep learning curve; and a lack of visibility into Kubernetes spend, leading to cost overruns. Over 57% cited the "significant or unexpected spend on compute, storage, networking infrastructure, and/or cloud-based IaaS" as their biggest challenge.

■ Big upsides. There are many ways to measure the return on Kubernetes investments. Those polled found that cost savings was the primary metric they used to measure success. Almost 44% of the organizations are implementing cloud cost reduction and FinOps initiatives to reduce cost overruns.

The Rise of FinOps for Kubernetes Workloads

FinOps is a budding approach to managing and optimizing cloud spending across different teams within an organization. Given the increased interest in managing Kubernetes costs in the cloud, those surveyed were asked about their experience with FinOps.

The FinOps Foundation defines levels of familiarity with FinOps, from crawl to walk to run. Similar to the results identified in The State of FinOps 2022 survey, most respondents self-identified at the walk stage, meaning they can implement cloud cost reduction recommendations. The second largest group self-identified as the crawl stage, with the ability to visualize cloud costs even if they haven't yet begun the process of implementing recommendations for remediation. Interestingly, more than 98% of respondents indicated familiarity with FinOps and saw themselves somewhere on the continuum of implementing best practices for cloud cost remediation. In addition, more than 17% of respondents identified themselves in the run stage, with the ability to remediate cloud costs autonomously.

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The State of Kubernetes 2023: Cloud Cost Remediation Is Top Priority

Pepperdata has released The State of Kubernetes 2023, the results of a survey conducted with 800 C-level execs and senior ITOps and DevOps professionals in industries including financial services, healthcare, technology, and advertising. The survey aimed to gain insights into the types and sizes of containerized applications and other workloads on Kubernetes clusters, as well as the FinOps tools and practices used to optimize Kubernetes deployments and control costs.


Key findings from the survey show:

1. The Kubernetes market is maturing.

Evidence of this includes:

■ The number of clusters that are being deployed has grown to six to ten clusters per organization.

■ The variety and types of workloads that are now being launched by Kubernetes extend beyond microservices to data ingestion, cleansing, and analytics, databases, and artificial intelligence (AI) and machine learning (ML).

■ An emerging challenge has been unexpected infrastructure spend and the growing focus on cost control and optimization.

2. Companies deploying Kubernetes clusters are doing so to stay competitive or ahead of the competition, and need the agility Kubernetes affords them to deploy services rapidly.

3. Cost savings was identified as the top ROI metric for a Kubernetes deployment.

4. Companies are turning to a wide variety of approaches to attempt to remediate infrastructure spend overruns.

"The survey confirms that Kubernetes has become the preferred choice for deploying workloads among agile enterprises. However, the speed and ease of deployment can result in unexpected infrastructure cost overruns. Respondents are increasingly turning to FinOps and cloud cost optimization tools to help them remediate the cost of operating in the cloud and optimize their Kubernetes clusters," said Ash Munshi, CEO, Pepperdata. "Regardless, the survey shows that cost savings is the top metric people are using to measure the ROI of their Kubernetes investments in 2023."

Other findings included:

■ How many? The average number of Kubernetes clusters among those surveyed ranged from three to ten. 32% reported having three to five deployments, while 38% reported six to ten clusters. Almost 15% said they had between 11 and 25 clusters, and four% reported more than 25.

■ What for? When asked what types of applications were being deployed, respondents chose from a list of workloads, with the most popular being data ingestion, cleansing, and analytics, including Apache Spark as the choice for 61% of those polled. Other popular deployments included databases or NoSQL databases such as PostgreSQL, MongoDB, and Redis; and web servers like NGINX.

■ What challenges? The benefits of Kubernetes are many, but companies also find that getting a Kubernetes project off the ground comes with many challenges. These include significant or unexpected spend on compute, storage, networking infrastructure, and/or cloud-based IaaS; a steep learning curve; and a lack of visibility into Kubernetes spend, leading to cost overruns. Over 57% cited the "significant or unexpected spend on compute, storage, networking infrastructure, and/or cloud-based IaaS" as their biggest challenge.

■ Big upsides. There are many ways to measure the return on Kubernetes investments. Those polled found that cost savings was the primary metric they used to measure success. Almost 44% of the organizations are implementing cloud cost reduction and FinOps initiatives to reduce cost overruns.

The Rise of FinOps for Kubernetes Workloads

FinOps is a budding approach to managing and optimizing cloud spending across different teams within an organization. Given the increased interest in managing Kubernetes costs in the cloud, those surveyed were asked about their experience with FinOps.

The FinOps Foundation defines levels of familiarity with FinOps, from crawl to walk to run. Similar to the results identified in The State of FinOps 2022 survey, most respondents self-identified at the walk stage, meaning they can implement cloud cost reduction recommendations. The second largest group self-identified as the crawl stage, with the ability to visualize cloud costs even if they haven't yet begun the process of implementing recommendations for remediation. Interestingly, more than 98% of respondents indicated familiarity with FinOps and saw themselves somewhere on the continuum of implementing best practices for cloud cost remediation. In addition, more than 17% of respondents identified themselves in the run stage, with the ability to remediate cloud costs autonomously.

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Manufacturing Organizations Doubled AI Investment Yet Only 37% Fully Prepared to Operationalize AI

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Optimizing Decisions with Edge-First Cognitive Intelligence

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

The New Perimeter Is the User: Why Identity Is the Real Network Edge

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

When AI Infrastructure Stops Behaving Like Infrastructure

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Enterprise Resilience: Understanding the Shift from Static to Dynamic

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile Users Expect Perfection in 2026

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

AI Confidence Is High. Readiness Is Not. What the Data Tells Us - and Why It Matters

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI, Realistic Expectations and the Future of IT Operations in 2026

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

Trust, But Verify: Building Confidence in AI Through Human Oversight

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

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