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 (GDPR in the EU) force data to be stored in the user's country.
• Sanctions risks: companies avoid dependence on infrastructure in “unfriendly” countries. For example, European companies moving from AWS to on-premises clouds (OVHcloud, 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.

An illustrative example of digital transformation in this direction is Netflix, which deployed local CDN nodes within the EU to ensure GDPR compliance.

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.

Key tools enabling these trends:

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.

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.  

3. 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.

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.

Reducing the share of public clouds.

Public clouds are growing (the market will reach $1.3 trillion by 2025 [3]), 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 scale and lack of upfront costs.

2.2 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)

2.3 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 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

Despite the fact that in complex projects (e.g., software development) remote work reduces the speed of solving tasks by 15-20% due to communication lags, 58% of employees note an increase in efficiency on remote work.

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

So full on hybrid work model is the trend at the moment:

• Apple, 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: 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 other cities/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.
• AI instead of managers:
  • AI automates sprint progress assessment.
  • ChatGPT analyzes retrospectives and suggests improvements.

Where management stays:

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

3.3 Staffing Failure: The Reality

Reduced investment in training:

• 60% of companies have cut apprenticeship budgets.
• Only 20% of juniors enter IT through corporate programs.

The consequences have not been long in coming, the market is flooded with self-taught students with gaps in basic skills (lack of understanding of OSI, OS, scripting).

LLM without a systematic approach:

• Junior developers use ChatGPT to write Terraform configs but don't understand IaC principles, leading to security group bugs.
• Research: 45% of AI-generated code contains vulnerabilities.

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 mentorship. Companies like Red Hat, Microsoft, Google (Google Cloud Skills Boost) keep a balance through mentoring programs and internal courses. But most will have to live with this in 2025.

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.

Tools:

• 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).

3.5 The rise of cyber threats in distributed systems

Argumentation:

• Remote access vulnerabilities: 62% of companies have experienced attacks via RDP (CyberEdge).
• Zero Trust as a response: Implementation of BeyondCorp (Google), Azure AD Conditional Access.

Addendum:

DevSecOps: Integrate security into CI/CD (e.g. automatic code scanning with Snyk).

3.6 Cybersecurity of distributed systems

• Zero Trust (BeyondCorp, Azure AD Conditional Access). 
• Snyk and Checkov for code scanning in CI/CD. 

4. Technological Health: Automation, Human Resources and AI Ethics

4.1 NoOps and DevOps 2.0

NoOps as an abstraction

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

NoOps is not suitable for complex systems where fine tuning of infrastructure is required, but in defense of NoOps - I will say that in places where fine manual (not automated) tuning is required - more often than not the technical level of the team is very much sagging. 70% of startups continue to implement NoOps to save resources.

Examples: 

• AWS Lambda and Vercel for serverless solutions.
• GitHub Actions for CI/CD automation. 

4.2 Staffing Crisis

Challenges: 

• Shrinking training programs (only 20% of juniors come through corporate courses). 
• Dependency on LLM: 65% of juniors do not solve problems without ChatGPT (JetBrains).

4.3. Degradation of LLM data

Solution: 

• GPTZero for AI content detection. 
• Use of “clean” datasets (Project Gutenberg). 

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

5.1 Vector databases

Examples: 

• Pinecone and Milvus for semantic search. 
• Weaviate is a hybrid database for RAG and LLM. 

5.2 Monetization of Open Source: 

• Elasticsearch → OpenSearch (AWS). 
• Terraform → OpenTofu (Linux Foundation). 

5.3 Platform Engineering 

• Backstage (Spotify) for internal portals. 
• Humanitec for orchestration of DevOps resources. 

5.4 DevSecOps and Low-Code 

Trends: 

• AWS Honeycode for No-Code automation. 
• Trivy and SBOM (Software Bill of Materials) for security. 

5.5 Breakthrough Technologies

• WebAssembly (Wasm): Used in Fermyon for cloud functions. 
• eBPF: Revolution in monitoring (Cilium, Pixie)

Conclusion: Bifurcation point

The DevOps industry is on the cusp of radical change: 

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

Recommendations: 

• Invest in Platform Engineering to manage complexity.
• Implement FinOps and AI tools to balance CapEx/OpEx.
• Remain agile: “no perpetual technologies, there are perpetual principles”.

The future evolution of IT will depend on the ability to adapt to regionalization while maintaining global interoperability through Open Source and standards.