Trends in DevOps 2025: Technology, Challenges and Transformation

Analysing key changes in the IT landscape based on global, economic and governance factors.

1. Global Trends: Regionalization, Hybrid Infrastructures and Ecology.

1.1 Localization of IT services

The main drivers of growth in localization of IT services are regulatory requirements (GDPR and similar laws), national interests, and sanctions risks: 

• Data Residency Laws (such as GDPR in the EU) require data to be stored in the user's country.
• Sanctions risks: companies avoid dependence on infrastructure in “unfriendly” countries. For example, European companies are transitioning from AWS to on-premises clouds, such as OVHcloud and Deutsche Telekom.
• Brazil and India are developing their own clouds to develop their digital sovereignty.

The dominant technology in this area is Kubernetes solutions with regional clusters based on Rancher, OpenShift, and tools like Crossplane for multi-cloud management. There is also a strong emphasis on compliance pipelines, such as automatic GDPR compliance verification using Open Policy Agent.

1.2 Hybrid Infrastructures

The primary trend in IT infrastructure for 2025 revolves around multi-cloud solutions (AWS + Azure + GCP + local providers) and Sovereign Cloud platforms, such as Switzerland’s Exoscale, Norway’s SAPA, and France’s now pan-European OVHcloud.

The main tools that help with this are:

1. The HashiCorp stack, particularly HashiCorp Consul, for service management in distributed infrastructures and service discovery.
2. Infrastructure as Code (IaC) tools such as Terraform/OpenTofu, Pulumi, and Ansible for idempotent configurations and orchestration. Puppet/Chef remain relevant for compliance and enterprise environments, though their popularity is declining.
3. Kubernetes is the industry standard for container orchestration.

Key Trends:

• Growth of Pulumi: Increasing popularity due to flexibility in programming language usage.
• Decline of Chef/Puppet: Being replaced by Ansible and Terraform.
• OpenTofu: Gaining momentum as an open-source alternative to Terraform.  

4. Multi-cloud control panels such as Azure Arc, Google Anthos, AWS Outposts, Rancher, Red Hat OpenShift + Advanced Cluster Management (ACM), Open Cluster Management (OCM), and VMware Tanzu for hybrid management.

The most popular multi-cloud tools in the table:

Neutral countries (Switzerland, UAE) do not always save from extraterritorial laws. For example, Swiss banks still comply with US sanctions. The real trend is hybrid infrastructure, not relocation.

1.3 Edge Computing

To the growth of IoT, DevOps is adapting via lightweight Kubernetes distributions (K3s, MicroK8s). Infrastructure as code (IaC) for edge devices (Pulumi, Ansible) and GitOps approach.

2. Changes in the IT Macroeconomy: Regionalization vs. Globalization

2.1 Search Engine Transformation

The “collapse” thesis is exaggerated, but transformation is inevitable.

• The rise of AI assistants: ChatGPT, Perplexity and Claude intercept some traffic by offering direct answers instead of links. However, Google is adapting by implementing SGE (Search Generative Experience).
• Monetization: The search engine advertising model (90% of Google's revenue) will continue, as AI assistants cannot yet fully replace contextual advertising.
• Vertical search: Niche platforms (e.g., Semantic Scholar for scholarly articles) will take share away from universal search engines.
• Meta-search crisis: Aggregators are losing relevance due to direct store integrations with AI assistants.

Rather, search engines are evolving into hybrid platforms combining traditional search and generative AI.

2.2 Reducing the share of public clouds is unlikely to happen.

Public clouds are growing (the market will reach $1.3 trillion by 2025), but hybrid models are gaining momentum: Large companies (Dropbox, 37signals) are partially reverting to self-hosted because of the long-term savings, but it requires expertise and CapEx. And while Kubernetes as a standard, OpenShift and Rancher make hybrid environments easier to manage, Self-hosted solutions are not a panacea. For 80% of SMEs, public clouds remain more profitable due to their ability to scale and the absence of upfront costs (no CapEx).

2.3 FinOps and CapEx/OpEx balance

The trend is the Growth of FinOps practices:

• 60% of companies implement cost monitoring tools (CloudHealth, Kubecost).
• Startups prefer OpEx (SaaS subscription), corporations prefer CapEx (in-house data centers for AI/ML).
• AI for cost forecasting - public cloud providers are actively deploying AI, both to optimize customers' virtual infrastructure and to perfect their own compute capacity.
• GitHub Copilot reduces the need for juniors, but increases the load on seniors.

The CapEx/OpEX balance depends not only on economics, but also on regulation. For example, GDPR is forcing even startups to invest in local infrastructure. Excessive savings also leads to risks: Facebook incident in 2021 (shutting down servers to save money caused a global outage)

2.4 Democratizing AI: Open Source as an equalizer

A breakthrough is the emergence of free LLMs: Llama (Meta), Mistral (France), and Falcon (UAE) allow SMEs to build AI-powered products without huge budgets, while Hugging Face and Weights & Biases lower the entry threshold for ML. Likewise, the last year has also seen several breakthroughs in optimizing the training of LLM models.

Impact on DevOps:

• MLOps tools (MLflow, Kubeflow, AWS Sagemaker, Azure ML, etc.) are becoming part of standardized pipelines.
• There is a growing demand for specialists able to customize open-source models.

3. Сhanges in team management

3.1 Full return to offices is cancelled

Although remote work in complex projects (e.g., software development) reduces task-solving speed by 15-20% due to communication lags, 58% of employees report an increase in efficiency through remote work.

The reason is the adaptation approach - productivity drops only in teams without remote work experience. This is not due to remote work per se, but rather to the lack of established processes (e.g., asynchronous communication, clear OKRs). Positive example of GitLab - originally a distributed company, retains efficiency.

Therefore, a full-on hybrid work model is the trend at the moment:

• Apple and Google require 3 days in the office, but face resistance from employees.
• 70% of IT startups keep full remote.
• Some companies prefer to use offices as hubs, providing space for collaboration (e.g., Spotify, with flexible hours).
• IBM returned 80% of employees and kept flexible options for developers.
• Microsoft is introducing “flexible offices” with reservations through Teams.
• Data-driven approach: Occupancy Analytics (EcoSync) sensors optimize space utilization.

Hybrid models are becoming the standard. Here are the major trends:

• Async-first approach: Companies like Doist use tools (Slack, Notion, Linear) to minimize synchronous meetings.
• Geographic flexibility: 40% of FAANG employees work from locations outside their home cities or countries.
• Timezone-agnostic workflows: Automating standup rallies via bots (Geekbot), recording video reports, zanation and mitap databases (MS Teams, Loom).
• Global Onboarding: Video courses on Udemy, local solutions + interactive simulations (A Cloud Guru).
• Frozen windows practice: Zapier, with 500+ employees in 40 countries, uses frozen windows for synchronous work (4 hours a day)

Statistic: 65% of FAANG employees prefer hybrid (2 days in the office / 3 at home).

3.2. Reducing the share of management

Management is shrinking in agile environments, but is retained in regulated industries.

Where management is being minimized:

• Startups and Open Source: GitHub, Elastic, and HashiCorp are minimizing management through self-organization and OKRs. Valve and GitHub are minimizing management by implementing flat structures.

Where management stays:

• Regulated industries: Banks (e.g. JPMorgan) and medtech (Philips) retain PMO for compliance.
• Crisis projects: DevOps in infrastructure migrations requires coordination.

AI instead of managers:

• AI automates sprint progress assessment.
• ChatGPT (OpenAI) analyzes retrospectives and suggests improvements.

3.3 Staffing Failure: The Reality

This is a consequence of large companies cutting their investment in training; almost 60% of companies have reduced their internship budgets.

The results were not long in coming: the market is flooded with self-taught people with gaps in basic skills (lack of understanding of OS, scripting).

Uncontrolled use of LLM without a systematic approach:

• Junior developers use ChatGPT to write Terraform configs but don't understand IaC principles (why and how do we use it).
• According to research, more than 45% of AI-generated code contains vulnerabilities and logical errors, and this code ends up back on GitHub and StackOverflow, where it is used for learning upcoming versions of models.

The next problem is the generation gap:

• 30% of senior engineers leave IT due to burnout.
• 70% of juniors can't solve problems without an LLM.

The problem is the lack of mentoring - companies like Red Hat, Microsoft, Google (Google Cloud Skills Boost) maintain balance through mentoring programs and internal courses. But the remaining majority will have to live with this in 2025.

In general, despite 70% of companies implementing FinOps and Platform Engineering to manage complexity, there is a crisis of discipline, as 40% of engineers copy solutions from Stack Overflow without adaptation.

3.4 Mental wellbeing as a new KPI

Mental health is a competitive advantage for recruitment and retention.

Challenges:

• Burnout: 42% of IT professionals experience chronic stress.
• Loneliness: 30% of remote employees feel isolated.

That is, on the one hand, the lack of relief from work tasks, and on the other hand, the lack of social connections typical of an office environment. I added this paragraph for DevOps team leaders who care about their people.

What tools can be used to address this issue?

• Headspace and Calm for meditation, platforms like Mindler for psychological support.
• Engagement metrics: Using Pulse surveys (Culture Amp) and analyzing chat tone (AWA).
• Salesforce and Spotify are introducing “Wellbeing Days” — extra weekends for mental wellness.

DevOps practices:

• “No Meeting Fridays” to reduce cognitive load.
• Automation of routine (e.g. ChatOps via Slack bots).

In fairness, reducing stress does not have a positive effect on everyone. If senior employees become more productive, then for juniors, it hinders the formation and activation of the prefrontal cortex, reducing their ability to learn, so stress is also a tool.

4. Technological health of the IT sphere:

4.1 LowCode/NoCode as a trend and anti-pattern

Low-Code/No-Code is a development approach that allows you to create applications with minimal or no coding. It is a tool for specific business tasks.
Low-Code/No-Code platforms are a stable trend, as they simplify development — a tool for MVP, but not a replacement for development.

• Who is it for: Corporations (wealthy), startups (very poor), business users (perplexed).
• Where it's effective: Prototyping, internal tools, simple applications.
• Where it doesn’t work: Complex systems, high-load projects, tasks with unique logic.
• Where he works: Corporate tools, startup MVPs, and automation of routine tasks.

Advantages:

• Accelerating digital transformation.
• Reducing the burden on IT departments.
• Rapid prototyping capability.

Risks:

• Technical debt: 30% of LowCode solutions require rewriting when scaling.
• Limited flexibility: Difficulty integrating with legacy systems and custom APIs.
• Security: 40% of LowCode applications are vulnerable to attacks due to standard templates.

LowCode/NoCode tools:

Successful examples:

• Lufthansa automated 80% of business processes through OutSystems, reducing development time by 70%.
• Unilever utilizes Power Apps for its internal systems, resulting in annual savings of $2 million.
• Adidas created a mobile app MVP in 2 weeks using the Bubble platform.

There are many more unsuccessful examples, but they won't be mentioned here because these companies have since sunk into oblivion, and their marketing departments no longer exist.

For DevOps/System engineers it is categorically harmful because it increases technical debt, makes the work of the first lines of support easier, but is a time thief for the third and fourth lines (the most expensive part) of the support team and SRE/DevOps teams in the field of automation and standardization, because repeatability and stability are our everything.

From personal experience, 100% of projects written using LowCode/NoCode faced technical limitations of platforms when scaling and customizing. In business, the closest analogue is a loan, use it now, pay later, and much more.

4.2 NoOps as a logical continuation of DevOps

NoOps is not a simplification, but a high-level abstraction where routine tasks are automated, allowing engineers to focus on strategy.

True NoOps (full automation of infrastructure) is a utopia for most companies, but partial implementation is more than possible. It is an organic extension of the DevOps methodology. It requires highly qualified engineers and a deep understanding of the essence of development processes, as well as a zero-tolerance approach to technical debt.

Successful cases:

• Netflix Automates 95% of Operational Tasks with Spinnaker and Chaos Monkey, reducing downtime by 40%.
• Oxagile and T4itech, with the help of a high level of automation, achieved 100% on projects where they professed NoOps and no longer carried out active development.

Failures:

• 60% of startups that implemented NoOps reverted to DevOps due to complex incidents.

NoOps only works in mature companies with perfect automation or a high level of System/DevOps expertise and a non-engineering team.

In general, for projects in development, I would be lying if I said that a DevOps team is not needed; however, their focus shifts to platform engineering, writing Infrastructure as Code (IaC), and project architecture. For the rest, hybrid models (classic CI/CD DevOps + automated tests) will be more effective.

4.3 Pollution of the LLM Noosphere

Experiments with early large language models (LLMs) polluted the noosphere and lowered the quality of data for more advanced models (I confess, I was guilty myself). Innovative new models learn from content generated by weaker versions and become "dumber" themselves.

Arguments:

• Research: Models trained on synthetic data lose approximately 20% of their accuracy.
• GPT-4 exhibits "degeneracy" in its responses when trained on data from GPT-3.5.

Solutions:

• Use tools like GPTZero to filter AI content.
• Focus on clean datasets from reputable sources, such as arXiv and PubMed, and verify their accuracy.

5. Technology Trends: Vector Databases, Open Source, and Platforms

5.1 LLM data versioning, vector, and graph database

The growth of graph databases is closely tied to the tasks of recommender systems and fraud detection. The leaders in this area are Memgraph, Nebula, and Neo4j. eBay uses the latter for recommendations, Amazon Neptune has integration with AWS AI services, and is provided as a service.

Vector DBs: In parallel, demand is growing for Pinecone and Milvus, which are optimized for semantic search and RAG. They have become a critical component for working with LLM and RAG (Retrieval-Augmented Generation), bypassing graph DBs in importance in the context of AI:

Useful in semantic search:

• Pinecone and Milvus enable you to search data by meaning rather than keywords, which accelerates the development of chatbots and recommender systems by 50%.
• ChatGPT utilizes vector indices to access relevant information beyond the training data.
• Memgraph is the most optimized graph and free DB. It clusters well, and I hope that someday I will write an article comparing it with its analogues.

Good integration with LLM:

• Platforms like Weaviate combine vector search with generative AI to create end-to-end pipelines for processing unstructured data.

Benefits for DevOps practitioners:

• Automatically update vector database embeddings using Airflow or Prefect.
• Monitoring search quality using metrics like NDCG (Normalized Discounted Cumulative Gain).

5.2 Open Source Monetization Attempts and Forks : 

Cases:

• Elasticsearch → OpenSearch: After the Elastic license changed, AWS forked the project, preserving the community. Of course, they rolled it back later, but the aftertaste remained.
• Terraform → OpenTofu: Reacting to HashiCorp's BSL License, Creation of the OpenTF Foundation. Only companies providing PaaS and SaaS services based on Terraform were concerned, but the alarm was serious.

As a consequence, a split in communities (for example, Redis with the RSALv2 license).

5.3 Crisis of overproduction of tools  

Death of tools: 80% of new DevOps tools disappear after 2 years (e.g. Pulumi survived, while CDK8s fell out of favor). The average project utilizes 15 or more tools, which exponentially increases the complexity of support.

Solution:

5.4 DevOps arsenal 

Trending tools:

• Platform Engineering — Chief
• AWS Honeycode and Retool — for No-Code automation.
• Trivy and SBOM (Software Bill of Materials) for security, in-build analysis projects.
• WebAssembly (Wasm) is used in Fermyon for Cloud Functions.
• eBPF is, to some extent, a revolution in monitoring (Cilium, Pixie).

Stagnating technologies:

• Terragrunt is still alive and kicking — 45% of Terraform users use it.
• GitOps, despite all its shortcomings, is implemented by 60% of enterprise companies.

Technologies that are losing popularity:

• Blockchain - the hype has died down, but niches (for example, supply chain tracking ) are saved.
• Meta has reduced its investment in the Metaverse by 50%, but industrial VR solutions continue to grow (NVIDIA Omniverse).
• Service meshes are fading — Istio and Linkerd are losing hype, but 35% of companies are using them for security due to regulatory requirements.

Conclusion 

We are at a bifurcation point, and DevOps is on the verge of radical change:

1. Regionalization of infrastructures.
2. Hybrid models of work and management are becoming the norm.
3. Vector DBs and RAGs redefine the approach to data in the AI era.
4. Open Source survives through forks and new business models.
5. Security and ethics become critical elements of DevOps pipelines.

What to do?

• Adapt, not resist: Hybrid infrastructures, AI assistants, and regionalization — inevitability.
• Implement FinOps to balance capital expenditures (CapEx) and operating expenditures (OpEx).
• Invest in people: mentoring, retraining, psychological support.
• Invest in Platform Engineering to manage complexity.
• Choose technologies consciously: keep the balance between innovation and stability, and control over technical debt.

DevOps was a driver of speed in 2019; by 2025, it will be a driver of sustainability. Success awaits those who combine agility with responsibility, and technology with humanity. And stay flexible: