DevOps in 2026: What Has Changed and What Really Matters

The state of DevOps in 2026. Real trends, tools we use, and what's hype vs what works. From the perspective of someone who does it every day.

DevOps has gone through several phases in the last ten years. First it was "ops who write scripts", then "devs who deploy", then it became a profession in itself. Now in 2026 we're seeing another evolution, with platform engineering emerging as a separate discipline and AI starting to enter workflows.

I've been working in this field long enough to have seen trends emerge, explode, and sometimes die. Here's what I think is really happening, without hype.

Platform Engineering: DevOps Evolved

The buzzword of the last two years is "platform engineering". It's not a rebrand of DevOps, it's an evolution.

The idea: instead of expecting every developer to learn Kubernetes, Terraform, and twenty other tools, you build an internal platform that abstracts the complexity. The developer pushes code, the platform handles everything else.

In practice, this means:

Internal Developer Platforms (IDP): Self-service interfaces where developers can create environments, deploy, see logs, without writing YAML or understanding the underlying infrastructure.

Golden Paths: Pre-built and validated workflows. "Want to deploy an API? Use this template." Instead of a thousand ways to do the same thing, you offer one that works well.

Developer Experience (DX): Focus on the experience of those using the platform. DevOps is no longer "do it yourself", it's "we give you the right tools".

Tools like Backstage (Spotify), Port, and Humanitec are emerging to build these platforms. They're not mainstream yet, but adoption is growing, especially in medium-large companies.

My opinion: platform engineering makes sense for organizations with 50+ developers. Below that threshold, the cost of building and maintaining an internal platform exceeds the benefits. Better to invest in documentation and training.

GitOps: Now Standard

GitOps is no longer a trend, it's the de facto standard for Kubernetes deployment.

ArgoCD and Flux are the dominant tools. Most teams I know use one of the two. The choice between them is more preference than technical — both do the job well.

What has changed:

Multi-cluster management: ArgoCD ApplicationSets, Flux with fleet management. Managing 10, 50, 100 clusters from the same Git repo has become feasible.

Progressive delivery: Argo Rollouts for canary deployments, Flagger for automated progressive delivery. Deploying without risks is much easier than a few years ago.

Image automation: Flux Image Automation and ArgoCD Image Updater. CI builds the image, the GitOps controller automatically updates the manifest and commits it. Fully automated cycle.

If you're not doing GitOps for Kubernetes yet, you should probably consider it. The initial learning curve is steep, but the benefit in reliability and auditability is significant.

Infrastructure as Code: Terraform and Beyond

Terraform remains the king, but the landscape is evolving.

OpenTofu: The open source fork after the Terraform license controversy. It's gaining traction, especially among those concerned about HashiCorp vendor lock-in. 99% compatible with Terraform.

Pulumi: Infrastructure as Code with real languages (Python, TypeScript, Go) instead of HCL. Has a solid niche among those who prefer programming to configuring.

Crossplane: IaC inside Kubernetes. Define cloud infrastructure as Kubernetes resources. Interesting for teams that want a single control plane for everything.

CDK for Terraform: AWS CDK but for Terraform. Write in TypeScript or Python, generate HCL.

My take: Terraform/OpenTofu remains the safe choice for most cases. Pulumi is interesting if you have dev-heavy teams that hate HCL. Crossplane makes sense if you're already all-in on Kubernetes and want to unify.

Observability: From "Nice to Have" to "Must Have"

The observability stack has matured enormously.

OpenTelemetry is the standard. Adoption has exploded. If you need to instrument an application, OTel is the answer. Vendor-neutral, supported everywhere.

The classic trio remains: Metrics (Prometheus), Logs (Loki or similar), Traces (Jaeger/Tempo). But convergence toward unified platforms is accelerating.

Grafana dominates visualization. Grafana Labs has built a complete ecosystem (Mimir, Loki, Tempo, Grafana) that covers all of observability. It's almost a monopoly in the open source world.

eBPF changes the game. Tools like Cilium, Pixie, and Tetragon allow deep observability without modifying code. You see all network traffic, system calls, without instrumentation. It's powerful and a bit scary.

AI for analysis: Vendors like Dynatrace and Datadog are integrating AI for anomaly detection and root cause analysis. Still in early phase, but promising.

If your observability stack is still "I look at logs when something breaks", you're behind. Investing in metrics, traces, and intelligent alerting pays enormously in saved debug time.

Security Shift Left (Finally For Real)

"Shift left" has been a slogan for years. In 2026 it's becoming common practice.

Supply chain security: After SolarWinds and other incidents, SBOM (Software Bill of Materials) and image signing have become almost mandatory in enterprise contexts. Tools like Sigstore/Cosign for signing, Syft for SBOM.

Policy as Code: Open Policy Agent (OPA) and Kyverno for Kubernetes policies. Define rules (no root containers, no unsigned images, etc.) and they're enforced automatically.

Integrated scanning: Trivy, Grype, Snyk integrated into CI/CD pipelines. Vulnerabilities are found before deploy, not after.

Secret management: HashiCorp Vault remains dominant, but External Secrets Operator for Kubernetes has greatly simplified integration.

Security is no longer an afterthought. It's part of the process from day zero. If it's not on your team, it's a serious risk.

AI in DevOps: Hype vs Reality

Let's talk about the elephant in the room. Everyone says "AI for DevOps". What actually works?

What works:

Code/config generation. ChatGPT, Copilot, and similar are useful for generating boilerplate Terraform, Kubernetes YAML, automation scripts. Not perfect, but they accelerate.

Log analysis. AI tools that analyze logs and suggest problem causes. Still early phase but promising.

Documentation. Generate documentation from code, from runbooks to wiki. Works fairly well.

What's hype:

"AI that manages infrastructure autonomously." No. We're not even close. AI can suggest, but critical decisions still require humans.

"DevOps replaced by AI." AI is a tool that increases productivity, not a replacement. Infrastructure problems are too context-dependent to be solved by generic models.

"AIOps solves everything." AIOps (AI for IT operations) has existed for years and results are mixed. Useful for anomaly detection, less for solving complex problems.

My approach: I use AI as an assistant for repetitive tasks. For architectural decisions or complex debugging, the human brain remains necessary. It will probably be this way for a while.

The Tooling I Use in 2026

For context, here's my current stack:

IaC: Terraform with reusable modules, Terragrunt for managing multiple environments.

CI/CD: GitHub Actions for most projects, GitLab CI for some clients. ArgoCD for deployment on Kubernetes.

Kubernetes: K3s for edge and development, EKS/GKE for enterprise production. Helm for packaging, Kustomize for environment customizations.

Observability: Prometheus + Grafana + Loki. OpenTelemetry for traces where needed.

Security: Trivy in CI, OPA/Gatekeeper for policies, Vault for secrets.

Containers: Podman on Fedora, Docker where needed. Buildah for CI builds.

It's not the perfect stack. It's what works for my use cases. Yours might be different, and that's fine.

What I Recommend to Beginners

If you're entering DevOps in 2026, here's where to invest time:

Learn Kubernetes well. Not superficially. Understanding how it really works (networking, storage, RBAC) distinguishes you from those who only know how to do kubectl apply.

Master one IaC tool. Terraform is the safe choice. You don't need to know them all, but you need to know one deeply.

Understand networking. DNS, load balancing, firewalls, VPN. Many DevOps problems are network problems in disguise.

Learn to debug. Logs, traces, metrics. Knowing where to look when something breaks is an undervalued skill.

Program at least a little. Python or Go. You don't have to be a senior dev, but writing scripts and automations is essential.

Don't chase every trend. FOMO is strong in this field. Choose a few tools, learn them well, change only when you have concrete reasons.

Predictions (With Caution)

The future is hard to predict, but some trends seem clear to me:

Platform engineering will grow. More companies will build internal platforms. The "platform engineer" role will become as common as "DevOps engineer".

Kubernetes will simplify. Not in underlying complexity, but in user experience. More abstraction, less YAML.

Edge computing will explode. IoT, retail, manufacturing. Managing distributed deployments will become more important.

Security will be non-negotiable. Compliance, audit, zero trust. It will no longer be optional for anyone.

AI will be a daily assistant. It won't replace work, but those who don't use it will be less productive than those who do.

Conclusion

DevOps in 2026 is different from five years ago. More mature, more specialized, with better tools and consolidated practices.

The basic principles remain: automation, collaboration, continuous feedback, iterative improvement. Tools change, principles don't.

If you work in this field, the best advice I can give you is: stay curious but pragmatic. Try new things, but don't change stack every six months. Build deep competencies, not superficial ones. And remember that the ultimate goal isn't using the coolest tools, but delivering value to users reliably.

Technology changes quickly. Solid engineering principles last.