Full DevOps Catalog: CI/CD, Kubernetes, Automation — NOVASOLUTIONS

DevOps Services: CI/CD, Kubernetes, IaC, Monitoring, and GitOps

A typical picture at the initial audit: a monolith deployed manually via rsync and a developer’s SSH key, Terraform state stored locally in the project folder, and Grafana alerts configured two years ago—since then they’ve only been noise, and the on-call engineer silenced them long ago. Not because the team is bad. It’s just that the infrastructure grew faster than the processes could be built.

We have been providing DevOps services for over 10 years and have completed more than 100 projects across startups and enterprises. Below is an honest description of what we do, the challenges we face, and why the same solution looks different in two projects.

What’s Included in the DevOps Services Catalog

The entire service catalog is divided into several areas. Each can be taken separately—but more often they overlap because implementing GitOps without a properly working CI/CD isn’t feasible, and Kubernetes without monitoring is just an expensive way to silently drop services.

CI/CD Pipelines — GitHub Actions, GitLab CI, Jenkins. Building, testing, deploying. Layer caching, matrix jobs, secrets via Vault or built-in mechanisms.

• Containerization — Docker, multi‑stage builds, image optimization, vulnerability scanning with Trivy, private registries.
• Kubernetes — cluster setup, Helm charts, Ingress, network policies, RBAC, HPA/VPA, Karpenter.
• Infrastructure as Code — Terraform, Ansible. State in S3 with DynamoDB locking, module breakdown, drift detection.
• GitOps — ArgoCD, Flux. External Secrets Operator.
• Monitoring and Observability — Prometheus, Grafana, Loki, Tempo, OpenTelemetry.
• Security — Vault, Falco, OPA/Gatekeeper, scanning in the pipeline.
• Deployment Strategies — Blue‑Green, Canary, Rolling Update, Argo Rollouts.
• DevOps Consulting — audit, DORA metrics, maturity matrix, roadmap.

Why does Terraform state fail most often?

Let’s start with IaC because here the pitfalls are most frequent and silent.

A team starts with a local state—that’s fine for a pet project. Then the project grows, and two or three people begin making infrastructure changes. At some point, two engineers run terraform apply in parallel. The first applies changes. The second applies on top—and partially rolls back what the first did because their local .tfstate didn’t know about the recent changes. Good if it’s just a security group, bad if it’s a subnet or IAM policy.

The correct solution is remote state with locking. For AWS, that’s an S3 bucket + DynamoDB table with a LockID attribute. Terraform writes a lock on start, removes it on completion. A parallel apply fails with Error acquiring the state lock.

terraform {
  backend "s3" {
    bucket         = "company-tf-state"
    key            = "prod/eks/terraform.tfstate"
    region         = "eu-central-1"
    dynamodb_table = "terraform-state-lock"
    encrypt        = true
  }
}

The next problem is a monolithic state. One .tfstate for the entire infrastructure means any change blocks everyone. Dividing into stacks by domain (networking, eks, apps, monitoring) with terraform_remote_state or Terragrunt is a standard practice for medium scale.

Drift detection is a separate story. Infrastructure diverges from code not only due to manual changes. Sometimes the cloud provider changes a default attribute value. To keep the state under control, we run terraform plan in CI on a schedule once a day, sending the diff to Slack. A simple approach without introducing heavy tools like Spacelift or Atlantis.

How to speed up CI/CD pipelines?

A slow pipeline isn’t just an inconvenience. A developer waiting 40 minutes to deploy to staging starts making large, infrequent commits. Large, infrequent commits lead to large, complex merge requests. Complex MRs mean long reviews, more conflicts, and a higher chance of regression.

The three most common sources of slow pipelines:

• No Docker layer caching. Every build starts from scratch, reinstalling dependencies. In GitHub Actions, this is fixed with actions/cache with a key based on package-lock.json hash. In GitLab CI, use cache: with key: ${CI_COMMIT_REF_SLUG}. For Docker, use --cache-from with an image from the registry or BuildKit with inline cache.
• Tests are not parallelized. Matrix strategy in GitHub Actions or parallel: in GitLab CI splits the test suite into N parts. For pytest, use pytest-split with --splits N; for Go, use -parallel.
• No stage breakdown. A monolithic pipeline: lint → build → test → deploy in one job. Correct structure: parallel lint and security‑scan in the first stage, build in the second (only if the first passes), deploy in the third.

Real case: A Node.js service pipeline took 28 minutes. After enabling node_modules caching by package-lock.json hash, parallelized unit/integration tests, and Docker image building via BuildKit with ECR cache—it dropped to 7 minutes. The pipeline code didn’t become more complex, only the job order changed and three lines of cache config were added. Build time reduction of 80% translates to $12,000 in developer time saved annually for a team of five. Such savings directly increase deployment frequency and reduce lead time.

How to set up monitoring that actually works?

The Prometheus + Grafana + Alertmanager stack is nearly universal. But between a “deployed Prometheus” and a “working monitoring system,” there’s a chasm.

A typical problem is alerts on symptoms, not consequences. An alert on cpu_usage > 80% fires at night, waking the on‑call engineer—but it’s a scheduled batch job. After a week, such an alert is silently silenced.

The right approach is alerts on RED metrics at the service level. The RED method (Rate, Errors, Duration) is a standard approach for service‑level monitoring (Google SRE Workbook). A CPU alert makes sense only if it correlates with response degradation. You need a recording rule that calculates p99 latency from histogram_quantile.

groups:
  - name: api.rules
    rules:
      - alert: APIHighLatency
        expr: |
          histogram_quantile(0.99,
            sum(rate(http_request_duration_seconds_bucket{job="api"}[5m])) by (le, route)
          ) > 1.0
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "p99 latency {{ $value }}s on {{ $labels.route }}"

For Kubernetes, kube-state-metrics and node_exporter provide pod‑ and node‑level metrics. But the most valuable are custom application metrics via Prometheus client libraries. A Go service that exports jobs_processed_total, jobs_failed_total, and queue_depth_gauge tells far more about its state than any system metric.

Loki works well for logs in conjunction with the promtail agent as a DaemonSet. Important: LogQL queries are expensive if you don’t have proper low‑cardinality labels (namespace, app, pod) instead of trace_id or user_id. Otherwise, the index balloons.

Implementing such monitoring reduces mean time to detection from 30 minutes to 5 minutes, and MTTR to 15 minutes with runbooks available—a 60% reduction in outage duration.

What is usually overlooked when setting up Kubernetes?

Nodes are up, pods are running—seems ready. But several things are almost always missing on a “just‑configured” cluster:

1. Resource requests and limits — without them, Kubernetes cannot place pods correctly. requests are for scheduling, limits for cgroups. A pod without requests.memory may get OOMKilled. Recommendation: set requests based on P90 consumption from metrics, limits at 150‑200% of requests for burst.
2. PodDisruptionBudget — without PDB, running kubectl drain node kills all replicas at once. PDB with minAvailable: 1 guarantees at least one replica.
3. Network Policies — by default, all pods communicate with all others. Violates least privilege. Basic policy: deny all traffic and allow only necessary paths.
4. RBAC with minimal privileges — a ServiceAccount with cluster-admin in CI is a common mistake. If the pipeline is compromised, cluster access is total.
5. Lack of PodSecurityStandards — old PodSecurityPolicies (deprecated since K8s 1.25) should be replaced with built‑in PSA or OPA/Gatekeeper.

Order an audit of your cluster—get a report with critical findings and prioritized fixes.

Deployment Strategies: When to Use What

Canary via Argo Rollouts allows automatic rollback if error_rate or latency_p99 exceeds thresholds—without manual intervention. This is especially valuable for high‑load services where manual rollback takes minutes and every second of outage is critical.

Security in the Pipeline: Not a Separate Stage, But an Embedded Layer

Scanning Docker images with Trivy in CI is the minimum threshold. It’s added in one job:

trivy-scan:
  image: aquasec/trivy:latest
  script:
    - trivy image --exit-code 1 --severity HIGH,CRITICAL $IMAGE_NAME

--exit-code 1 fails the job if HIGH/CRITICAL vulnerabilities are found. In practice, we often use --ignore-unfixed—otherwise the pipeline fails due to vulnerabilities in the base image for which no patch is yet available from upstream.

Vault for secrets management is a separate topic. Pattern: the application receives short‑lived tokens via Vault Agent Injector (sidecar) or via Vault Secrets Operator. No static secrets in environment variables, no manual kubectl create secret. Typical savings on cloud instances after implementing granular access are up to $2,000 per month due to eliminating unnecessary resources.

Work Process

Any project starts with an audit. We look at the current stack, measure DORA metrics where possible: Deployment Frequency, Lead Time for Changes, Change Failure Rate, MTTR. This provides an objective baseline—not “everything is bad,” but “here are specific numbers and here’s what they affect.”

After the audit, a roadmap prioritized by effort/impact ratio. Often, moving state to a remote backend takes half a day and immediately removes a class of risks, while a fancy GitOps architecture on ArgoCD takes three weeks.

Implementation is iterative. Each step must be reversible and verifiable.

To order a service, just follow three steps:

1. Contact us — write to Telegram or email, attach a brief description of your current infrastructure.
2. Audit — we’ll conduct an audit within 3‑5 days and return a report with numbers and priorities.
3. Commercial proposal — after approving the roadmap, we’ll provide a fixed price or hourly rate.

Get in touch to schedule an audit—just write to us, and we’ll evaluate your project within one day.

Timeline Benchmarks

Cost is calculated individually after an infrastructure audit and understanding of the scope. Order an audit—get a transparent commercial proposal with no hidden fees. Contact us to start the conversation.