DevOps Trends & Predictions for 2025

Author

Bibin Wilson
February 10, 2025

👋 Hi! I’m Bibin Wilson. In each edition, I share practical tips, guides, and the latest trends in DevOps and MLOps to make your day-to-day DevOps tasks more efficient. If someone forwarded this email to you, you can subscribe here to never miss out!

Note: You can read the web version here

In today's edition, we will explore key DevOps trends for 2025.

Understanding these trends helps engineers, team leads, and architects stay on the right path with evolving technology.

Also, in the AI era, keeping up with trends is essential, as everything is changing rapidly.

Let’s dive right in!

Disclaimer: These trends and predictions come from market research and industry data. They may not be 100% accurate, but they give a good idea of where the industry is going. Use them as a guide, not a guarantee.

1. AI and ML (AIOps)

Artificial intelligence and machine learning will play a bigger role in DevOps, automating routine tasks, analyzing large datasets, and offering smart insights for better decision-making.

For example,

AIOps platforms automate incident detection, root cause analysis, and remediation, significantly reducing Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR).

This trend is growing fast because companies need to work faster and more efficiently. AI/ML provide exactly that.

A study by MarketsandMarkets The global AIOps Platform Market size accounted for $11.7 Billion in 2023 and is estimated to achieve a market size of $32.4 Billion by 2028 growing at a CAGR of 22.7% from 2023 to 2028.

2. Platform Engineering

Platform engineering is growing as a top trend to address the complexity of modern DevOps tools and processes.

It involves building internal developer platforms (IDP) to streamline workflows for all the teams in a organizations.

The central platform teams in every organization are generally responsible for building Internal Developer Platforms (IDPs).

Gartner's research indicates that by 2026, 80% of large software engineering organizations will establish platform engineering teams to provide reusable services, components, and tools for application delivery, up from 45% in 2022. (Gartner)

3. GitOps

GitOps, the practice of using Git repositories as the source of truth for application infrastructure, is gaining common adoption.

ArgoCD is one of the most popular tools for implementing GitOps.

According to CNCF's latest microsurvey on GitOps, 31% of respondents began using GitOps in their cloud and Kubernetes environments within the last year, adding to the 60% who have been using it for over a year.

4. Security Shifts Left with AI Assistance(DevSecOps)

Security continues to be a primary concern for DevOps, leading to DevSecOps becoming a non-negotiable practice.

IBM's Cost of a Data Breach Report reveals that organizations using AI and automation for security saved an average of $2.22 million compared to those that didn’t use these technologies. (IBM)

AI-powered tools are increasingly being used to scan codebases for vulnerabilities early in the development cycle, helping "shift security left."

As per a 2024 redhat survey, 90% of respondents are already working on a DevSecOps initiative and 42% of respondents have a DevSecOps initiative in an advanced stage within their organisation.

5. Cloud Cost Optimization and FinOps

As cloud spending continues to grow, DevOps teams are focusing on financial operations (FinOps) for cloud cost management.

The integration of cloud cost estimation tools into CI/CD pipelines will help developers make cost-aware choices during deployment.

By 2025, cloud spending is expected to exceed USD 825 billion, with FinOps tools potentially saving companies USD 21 billion annually

Also, organizations are increasingly adopting multi-cloud and hybrid cloud environments, which require sophisticated cost management tools to optimize spending across platforms.

Over 58% of enterprises are using AI tools for cloud cost management, enabling real-time insights and automated resource optimization

6. Kubernetes

Kubernetes is growing into an all-purpose platform for diverse workloads.

In 2025, we will see Kubernetes playing a larger role in edge computing, AI/ML deployments, and serverless operations.

Over 60% of enterprises now use Kubernetes, and the CNCF annual survey reports that Kubernetes adoption has reached an impressive 96%.

7. MLOps: DevOps for AI/ML Workflows

MLOps is emerging as an important practice for managing AI/ML models in production.

As per Straitsresearch, Over 80% of enterprises are expected to adopt generative AI models by 2026, driving demand for MLOps platforms.

The global MLOps market was valued at USD 1.7 billion in 2024 and is projected to grow at a CAGR of 37.4% from 2025 to 2034, reaching USD 72.13 billion by 2034

Major players like Google Cloud, AWS , Microsoft Azure offer end-to-end MLOps solutions, including model training, deployment, and monitoring.

7. Edge Computing

Edge computing is growing fast as more applications need to run closer to users, reducing latency and improving performance.

Managing these distributed environments requires strong DevOps practices.

K3s, a lightweight and optimized Kubernetes distribution, is widely used for IoT and edge devices in various use cases.

The Edge computing market is projected to grow from $60 billion in 2024 to $110.6 billion by 2029, with an average annual growth rate of 13%.

Wrapping Up

We gathered a lot of data, but to keep it short, we included only the most important insights.

One key trend stands out —> AI is everywhere. There’s no denying it.

Even in conferences, AI/ML is the major topic of discussion.

What does this mean for people in DevOps/SRE?

We need to use our common sense more. Think about tasks that are repetitive or things that AI agents can easily handle. A simple example is incident management.

To stay ahead, focus on skills that put you on the other side, where these AI-driven systems are designed and developed.

Also, DevOps engineers who understand AI/ML deployment pipelines will have a competitive edge.

The bottom line?

DevOps is evolving, and AI is at the heart of it.

This means the future of DevOps will be more automated, smarter, and focused on business goals.

To succeed, you will need a mix of traditional DevOps skills, AI knowledge, and an understanding of how businesses work.

Over to you!

What do you think about these trends?

If you have any tips, insights, or comments, feel free to share them in the web version here!

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