TL;DR
DevOps Trends to Watch in 2019 without the fluff: more automation, more cloud‑native, more security, more observability—and more hype. The winners will be the teams that translate these trends into better lead time, reliability, and cost, not just prettier diagrams.
“A ‘trend’ that doesn’t change how you ship and operate is just a conference talk.”
Introduction: Trends vs. Reality
As we step into 2019, the DevOps landscape continues to evolve. For every buzzword, there’s a real problem hiding underneath: slow releases, fragile systems, burned‑out on‑call engineers, or spiraling cloud bills. Treat trends as tools you might use to address those problems—not as checkboxes on a maturity model.
In that spirit, here are a few trends that matter this year, and what they look like when they actually improve life for teams and customers.
1. Increased Automation (Done Right)
Automation remains at the core of DevOps. In 2019, more organizations will continue to adopt tools and practices to automate repetitive tasks, from code deployment to infrastructure management. The difference between noise and value is where you automate.
Good automation:
- Turns risky, manual deployments into boring pipelines with one‑click or automatic promotion.
- Keeps environments in sync via IaC instead of “SSH plus hope.”
- Eliminates repetitive on‑call tasks (log rotations, cache flushes, simple restarts) behind safe runbooks and buttons.
Bad automation:
- Wraps broken processes in scripts and calls it progress.
- Hides complexity behind opaque tools nobody understands.
- Fires off changes without guardrails, tests, or rollbacks.
Measure automation success via lead time, change failure rate, and MTTR, not by counting how many Jenkins jobs you created.
2. Cloud-Native Technologies Become the Default
The shift to cloud‑native architectures continues to gain momentum. Kubernetes, serverless computing, and container orchestration are becoming essential components of modern DevOps workflows. The temptation is to treat them as goals; they’re not. They’re means.
What “cloud‑native” should give you:
- Easier horizontal scaling when traffic spikes.
- Better isolation and resource management across services.
- A more consistent deployment model from dev to prod.
What it shouldn’t do:
- Multiply your operational burden without clear payback.
- Force every team to become an expert in the entire CNCF landscape.
- Turn a simple monolith into 50 services nobody can reason about.
“If your ‘Kubernetes migration’ didn’t reduce lead time or improve reliability, you just swapped one set of headaches for another.”
3. AI and Machine Learning in DevOps (Carefully)
AI and machine learning are making their way into DevOps in the form of predictive analytics, anomaly detection, and smarter automation. Used well, they help teams identify and resolve issues faster; used poorly, they are just expensive alert noise generators.
Where AI can help:
- Suggesting likely root causes by correlating logs, metrics, and deployment events.
- Detecting anomalies in traffic or error rates that don’t fit simple thresholds.
- Generating test cases or configuration diffs from natural language descriptions.
Guardrails:
- Keep humans in the loop for changes that touch config, infra, or money.
- Tie AI decisions to clear evidence and traceability; no black boxes in incident response.
- Track override and rollback rates; if people constantly correct the system, retrain or roll it back.
4. DevSecOps: Security by Default
Security is becoming a top priority in DevOps. DevSecOps integrates security practices into the pipeline so you don’t bolt them on at the end or during a crisis.
This looks like:
- Static and dependency scanning in CI, with sensible thresholds and suppression flows.
- Secret management with vaults and short‑lived tokens instead of environment variables in plain text.
- Policy as code at deployment time (e.g., OPA/Kyverno) to prevent unsafe configs from ever hitting production.
The goal isn’t “no vulnerabilities ever.” It’s to make doing the secure thing the easiest path and to catch risky changes before they go live.
5. Focus on Observability (Not Just Monitoring)
Monitoring and observability are critical for understanding system performance, especially in distributed architectures. Tools like Prometheus, Grafana, and the ELK/EFK stack are becoming more sophisticated, but sophistication isn’t the goal—clarity is.
Observability in 2019 should mean:
- Traces that link user actions to backend services so you can see where time is spent.
- Metrics tied to business SLOs and error budgets, not just CPU and memory dashboards.
- Logs structured enough to be queryable in seconds when incidents hit.
“The best observability practice is still this: know which graph you open first when the pager goes off.”
6. Platform Teams and Golden Paths
A quiet but important trend is the rise of internal platform teams that provide paved roads (“golden paths”) for building, testing, and deploying services. Instead of every team rolling their own scripts and pipelines, platform teams:
- Offer opinionated templates with CI/CD, observability, and security baked in.
- Expose self‑service tools (CLI/portals) for creating services and environments.
- Own shared concerns like ingress, identity, and logging.
For developers, the experience should feel like: “I type one command, answer a few questions, and I get a repo, pipeline, and dashboards that work.” That’s DevOps maturity that actually matters.
Conclusion
2019 promises to be an interesting year for DevOps, but not because of new buzzwords. It’s interesting because more teams are moving past “we installed Kubernetes” or “we have CI” and into “we can ship, recover, and measure with confidence.”
Pick one or two of these trends and tie them to concrete goals: faster lead time, lower MTTR, clearer SLOs, reduced toil, or safer changes. If a trend can’t be traced back to those outcomes, skip it and focus on the work that will.
Stay tuned for more DevOps insights and best practices.
Stay tuned for more DevOps insights and best practices.