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.

Hot Topics

The Latest

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

Explainability Is the New Battleground in AI-Powered Observability

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

Empowering the Human Side of the Autonomous IT Age

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

No Escalations ≠ No Work: Why Visibility in DevOps Matters More Now That AI Is Accelerating Everything

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Observability Costs Rising Faster Than Value

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

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.

Hot Topics

The Latest

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

Explainability Is the New Battleground in AI-Powered Observability

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

Empowering the Human Side of the Autonomous IT Age

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

No Escalations ≠ No Work: Why Visibility in DevOps Matters More Now That AI Is Accelerating Everything

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Observability Costs Rising Faster Than Value

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...