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

Keys to Building a Partner Ecosystem That Scales

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Why ITOps Need Right-Sized AI, Not Bigger Models

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

5 Takeaways from the Observability Forecast for Media and Entertainment

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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

Keys to Building a Partner Ecosystem That Scales

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Why ITOps Need Right-Sized AI, Not Bigger Models

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

5 Takeaways from the Observability Forecast for Media and Entertainment

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...