[updated 11 Nov 2025]
By late 2025, containerization has moved decisively beyond basic adoption to become ubiquitous across operational environments. Indeed, it is no longer the preserve of the experimental or fast-moving startup but is now fundamental to the well-established enterprise. This widespread infiltration is fueled by multiple drivers, including the rise of serverless containers, the demand for resilient multi-cloud architectures, and the accelerating impact of edge computing.
In this post, we will examine the key containerization trends of 2025, survey the maturing technology landscape, and conclude by analyzing the profound impact of AI and Machine Learning (AI/ML) on this domain.
Key Trends in 2025
Serverless Container Model
During 2025, many organizations have moved towards a Serverless Container Model. In this model, organizations access diverse cloud capabilities without the overhead of provisioning, deploying, or managing underlying hardware or software infrastructure. The focus shifts entirely to the application code and data, packaged within a container. Customers simply configure the container as required and deploy it directly onto a serverless platform.
This abstraction means organizations no longer manage the actual servers or compute infrastructure, allowing them to focus entirely on container creation and deployment into the cloud provider's managed offering. For example, AWS offers Fargate, enabling companies to run containers without managing EC2 servers; Google offers Cloud Run; and Microsoft Azure provides Container Instances. A further attraction of this model is that billing often shifts to a consumption-based model, focusing on container execution time rather than persistent compute usage.
The rise of Docker
Docker (launched back in 2013) is still the leading containerization platform in use today, well known for its simplicity and stability. In many ways, Docker is the face of containerization; for many, when they think of a container solution, they think of Docker. One analysis suggests that 47% of companies with at least 1,000 hosts have Docker in full production, indicating that larger enterprises are taking adoption extremely seriously. However, Docker does not have the market entirely to itself; alternatives like Podman are starting to gain traction, particularly in security-sensitive environments.
Dominance of Kubernetes
Whilst Docker may be the face of containerization in 2025, Kubernetes is the conductor, ensuring that the orchestra of containers plays the tune the customer wants to hear. Indeed, in 2025, the Cloud Native Computing Foundation (CNCF) reported that 96% of organizations either use Kubernetes or are actively evaluating it, cementing Kubernetes' position as the undisputed leader in container orchestration. It has evolved into a stable, well-established, and vital platform for running enterprise-wide, container-based systems.
Alternatives to Kubernetes
While Kubernetes is currently the dominant orchestration system, another key trend in 2025 is the rise of viable alternatives. Driven by Kubernetes’ acknowledged complexity, many organizations are seeking simpler, easier-to-use, and lighter-weight solutions such as Fly.io, HashiCorp's Nomad, and AWS’s App Runner. Docker Swarm is also experiencing renewed interest - though not a full revival - due in part to its simplicity, ease of use, and native integration with Docker.
Cloud Native Containerisation is the New Normal
The 'Kubernetes in the Wild' report by Dynatrace illustrates this, finding that two-thirds of all Kubernetes clusters are now hosted in the cloud. This trend strongly indicates that cloud-native computing, powered by containerized applications, has become the de facto standard for modern systems. Cloud-native containerization truly is the new normal.
Multi-Cloud
Building on the previous trend, the question becomes: which clouds? Organizations often utilize different cloud offerings - ranging from internal systems to public infrastructure and edge computing environments - making the ability to deploy applications consistently across multiple providers paramount. The use of standardized containers and powerful orchestration systems like Docker and Kubernetes effectively insulates an organization from the idiosyncratic differences between one cloud system and another.
Edge Computing
As the world moves ever closer to a fully interconnected reality, Edge Computing - the practice of placing data processing functions closer to the data source (e.g., smart vehicles, IoT devices) - will only become more important.
While the current trend for edge computing favors lighter-weight orchestration solutions such as K3S and MicroK8s, containerization remains fundamental to the growth and viability of this area.
Security
The growth of the container security market in 2025 is driven by both widespread container adoption and the complex security challenges they present. This is demonstrated in several ways. For example, with compromised open-source images posing a growing risk, there is now an increased emphasis on ensuring that these building blocks are safe and secure by only using trusted images. Bloated or non-minimal (but often easy-to-access) container images also create a larger attack surface for security breaches, an issue made worse by the rapid proliferation of available container images.
Security misconfigurations within containerized solutions are widespread; for example, one 2022 report found that 44% of organizations were running 71% or more of their workloads with root access privileges.
However, identifying trusted sources is non-trivial, and numerous tools are becoming available to scan images and generate reports on their trustworthiness. Open-source container-security tools like Trivy and Clair automate the scanning process and generate detailed reports with severity ratings. Security is thus 'shifting left,' ensuring that vulnerability scanning and threat modeling are integrated early into the development process. Finally, threat detection is becoming more sophisticated with the use of AI and ML tools to analyze and identify anomalies.
Technology Landscape
A Maturing Market
Docker and Kubernetes have matured over the last decade into well-established and solid suites of tools. Their usage across the IT industry has achieved near-ubiquity. While adoption in other sectors, such as manufacturing and finance, is currently lower, it is growing rapidly, suggesting these industries will soon catch up to the IT sector.
The global software containers market is estimated at $4.5 billion in 2025 by Future Market Insights and is projected to reach over $13 billion by 2035, indicating significant growth over the next 10 years.
Docker and Kubernetes reign
As stated above, the container world is dominated by Docker and Kubernetes in 2025. However, managing and operating Kubernetes remains a complex challenge for many organizations. This complexity likely serves as a barrier to adoption in non-IT markets and remains a considerable challenge even for experienced IT professionals. How this will affect Kubernetes' dominance in the long run remains to be seen.
Logging and Monitoring
Logging and monitoring are critical within a dynamic containerized system, yet consistent visibility of these systems remains difficult and requires constant attention, even in 2025. Tools such as Cloudwatch, Prometheus & Grafana, and ELK Stack are popular options to assist DevOps teams.
Containers and AI/ML
AI is the defining trend of our times, and containerized systems are no exception. The role of AI and Machine Learning (AI/ML) in containerization is more nuanced than simple integration, impacting three primary areas: Container Management, Container Security, and serving as an application area for Containerization itself.
AI/ML for Container Management
AI/ML can be used to enhance the performance and reliability of the container infrastructure itself. For example, AI/ML can be used to predict workload resource requirements, which in turn optimizes container scheduling and placement. This ensures high-priority workloads receive necessary resources while improving overall cluster efficiency, consequently reducing cloud costs. This emerging technology is receiving heavy investment from most cloud infrastructure providers.
Additionally, the term AIOps (AI for IT Operations) is being used to transform how operations activities are performed. AIOps aims to automate and improve IT operations using machine learning and other AI techniques by analyzing IT data in real-time. These models provide intelligent automation and predictive analytics within Kubernetes operations, identifying potential issues before they occur and automating incident response to reduce the Mean Time to Resolution (MTTR). AI/ML is even being integrated into developer-oriented tools such as Docker Desktop to provide intelligent guidance for building applications.
AI/ML for Container Security
Instead of relying on static rules, AI/ML is also being used to help with security within a containerized system. It can be used to perform proactive threat detection, automated vulnerability management, and incident response.
To do this, AI/ML systems analyze container behavior to create a profile of 'normal' activity. Any deviation, such as unusual network traffic or suspicious system calls, is flagged as a potential threat. The system can then automatically take action, such as quarantining the affected container or blocking malicious traffic. For example, Falco is an AI-powered threat detection engine that can be used to monitor system calls and detect unusual network connections.
Containers for AI / ML workloads
Not only can AI/ML help the container world, but in return, containerized solutions are highly beneficial to the AI/ML world. Containers offer a significant benefit by creating reproducible environments, effectively solving the classic 'it works on my machine' problem. They allow teams to package complex dependencies - such as Python, and specific versions of key libraries like pandas, numpy, PyTorch, and SciPy - into a single, shareable Docker image.
Using containers and orchestration tools, containerized AI/ML solutions can be scaled out across many nodes, allowing for large-scale model training and high-volume data processing. In this area, a rich ecosystem of containerized tools has emerged, including Jupyter Docker Stacks, MLFlow containers, and Large Language Model (LLM) containers such as Ollama.
Summary
The world of container-based systems has matured over the last 10 years to become one of stability and reliability. However, this world is still only starting to feel the effects of AI and Machine Learning on what can be done to help manage the complexity of these systems and ensure their security. The application of AI within this sphere is only likely to increase, so watch this space. What is clear is that the impact and growth of containerized systems will only accelerate over the next few years.