Cloud and DevOps: Faster releases, less downtime, a lower cloud bill.
We design your cloud architecture, automate your release process and report on your cloud spending on a regular basis.
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Cloud and DevOpsIn short
DevOps consulting is engineering support that automates the path from writing code to running it in production and makes that path measurable. At CoreTechs we design your cloud architecture on AWS, Microsoft Azure or Google Cloud, migrate existing systems to the cloud in planned stages, build CI/CD pipelines and infrastructure as code, and report on your cloud spend every month. The goal is more frequent, lower-risk releases, fewer outages and a cloud bill that stays under control.
Scope
Cloud transformation
- Architecture design on AWS, Microsoft Azure and Google Cloud
- Migration of existing systems to the cloud
- Hybrid cloud setups
DevOps
- CI/CD pipelines
- Docker and Kubernetes
- Infrastructure as code (Terraform)
- Autoscaling
- Release management
Cost optimization (FinOps)
- Resource usage analysis
- Identifying unnecessary spending
- Monthly cost reports
Deliverables
- Architecture document
- Automated deployment pipeline
- Working infrastructure
- Team training and DevOps culture consulting
Who is it for?
- Teams whose releases still depend on manual steps, one person's knowledge or late-night maintenance windows
- Companies that want to move from their own data center or rented servers to the cloud, but need clarity on downtime and cost risk first
- Businesses whose cloud bill grows every month without visibility into which resources are driving it
- Teams considering containers or Kubernetes who want to check whether it actually fits their scale
- Organizations with a software team but no dedicated DevOps engineer, or that want to build this capability in-house
- Companies that need a hybrid setup because some systems must stay on-premises for regulatory or technical reasons
How we work
Current state assessment
We review your applications, servers, release process and any existing cloud accounts. Bottlenecks, single-person dependencies, security gaps and unnecessary spend are documented in a written assessment report.
Target architecture and migration plan
We define which system moves to the cloud with which approach (rehost, replatform or refactor), the target architecture, a cost estimate and a rollback plan, all in a single architecture document.
Infrastructure as code and CI/CD
We define infrastructure in Terraform, containerize your applications and build an automated release pipeline covering tests, builds, security scans and deployment. Because every environment is created from the same definition, the gap between staging and production disappears.
Phased migration and go-live
We start with lower-risk services and keep downtime to a minimum with data synchronization and parallel running. Every step has a checklist beforehand, verification tests afterwards and a rollback path ready when needed.
Cost optimization and handover
We monitor resource usage to identify idle resources, oversized servers and unsuitable pricing models, and deliver a monthly cost report. Your team is trained to run the infrastructure and pipeline on its own, and all documentation is handed over.
How long does it take?
The duration of a DevOps and cloud migration project depends on the number of systems, how cloud-ready your applications are, data volume and the acceptable downtime window. After a free 30-minute initial call, we usually start discovery within a week; at the end of discovery we provide the scope, phases and a firm quote in writing.
Phases
- Discovery and assessmentCurrent infrastructure, application dependencies and cost structure are mapped; migration approach and priorities are agreed.
- Foundation setupNetworking, identity and access management, environments and monitoring are built as code.
- CI/CD and containerizationApplications are containerized and the release pipeline is rolled out service by service in two-week sprints.
- Migration wavesSystems are grouped by risk and moved in sequence, with verification at the end of each wave.
- Optimization and handoverCost and performance are tuned, team training is completed and documentation is delivered.
What drives the cost?
The cost of DevOps consulting and cloud migration depends on scope and the condition of your current systems. We share a budget range in the initial call and a firm quote after discovery. Cloud usage fees are separate from our service fee and are billed directly to your account by the provider.
| Factor | Effect on cost |
|---|---|
| Number of systems and applications | Each application needs its own dependency analysis, testing and verification, so scope grows directly with it. |
| Cloud readiness of applications | Systems that can be rehosted move faster; those requiring code changes or refactoring take more effort. |
| Data volume and downtime tolerance | Large datasets and near-zero downtime expectations require extra steps such as parallel running and synchronization. |
| Hybrid or multi-cloud requirements | Secure connectivity and unified management between on-premises and cloud means more design work than a single-cloud setup. |
| Security and compliance requirements | Personal data under KVKK or GDPR, data residency constraints or sector regulations add architecture and audit steps. |
| Whether Kubernetes is needed | At smaller scale, managed container services may be enough; setting up and operating Kubernetes is a separate effort. |
| Post-handover support model | Whether the infrastructure is handed to your team or operated by us under an SLA determines the total cost. |
Technology options
Cloud providers
- Amazon Web Services (AWS)
- Microsoft Azure
- Google Cloud
Containers and orchestration
- Docker
- Kubernetes
- Amazon EKS
- Azure AKS
- Google GKE
- Helm
Infrastructure as code
- Terraform
- Ansible
CI/CD
- GitHub Actions
- GitLab CI
- Azure DevOps Pipelines
- Argo CD
Monitoring and logging
- Prometheus
- Grafana
- Loki
- ELK (Elasticsearch, Logstash, Kibana)
Security and secrets
- HashiCorp Vault
- Cloud provider key management services
- Container image scanning
Example solution scenario
From manual releases to automated deployment
Representative scenario, not a real client project.
- Profile
- A mid-sized software team running a web application and several backend services on its own servers.
- Need
- Releases went out once a month, after hours, through manual steps run by a single system administrator. Because staging differed from production, bugs only surfaced after release, and server capacity fell short during peak periods.
- Approach
- Servers, application dependencies and release steps were documented, and every single-person dependency was listed.
- Infrastructure was defined in Terraform, with staging and production built from the same definition.
- Applications were containerized and a CI/CD pipeline with testing, security scanning and deployment was built.
- Services were moved to the cloud first in staging, then gradually in production, with a rollback plan ready at every step.
- Autoscaling and cost reporting were enabled, and the team was trained on the new release process.
- Technologies
- AWS
- Terraform
- Docker
- GitHub Actions
- Prometheus
- Grafana
- Outcome
- Releasing no longer depends on one person; it is now a repeatable process any team member can trigger. With environment drift gone, post-release surprises became rarer, capacity adjusts itself during busy periods and cloud spend is tracked in monthly reports.
Frequently asked questions
Will there be downtime during the cloud migration?
With a planned migration, downtime can be kept to a minimum for most services. We use data synchronization, a short period of parallel running and gradual traffic shifting. Where a brief maintenance window is unavoidable, we identify it during discovery and schedule it with you.
Will moving to the cloud lower our costs?
It depends on your current infrastructure and workload. Systems with variable load can save through autoscaling and the right pricing model, while systems under constant, steady load are not always cheaper in the cloud. During discovery we present current and projected costs side by side.
How should we choose between AWS, Azure and Google Cloud?
The choice depends on your existing licenses, your team's experience, the enterprise software you use and data residency requirements. For example, Azure can be practical for organizations deeply invested in the Microsoft ecosystem. We are not tied to any provider and give you a written recommendation with the reasoning.
Do we really need Kubernetes?
Not for every project. For systems with only a few services, managed container services or virtual machines can be simpler and more economical. We recommend Kubernetes when the number of services, scaling needs and your team's operational capacity justify it.
Who will manage the infrastructure after the project?
There are two options: we hand the infrastructure over to your team with documentation and training, or we keep operating it under an SLA-based maintenance and support model. Either way, the infrastructure code and all access remain in your own accounts.
Who owns the cloud accounts and infrastructure code?
Cloud accounts are opened in your name and invoices go directly to you. Once the project fee is paid, the Terraform definitions, CI/CD configuration and all source code and intellectual property rights are transferred to you.
Can we keep some systems on-premises in a hybrid setup?
Yes. For systems that must stay on-premises for regulatory, latency or licensing reasons, we set up secure connectivity and bring cloud and on-premises environments under a single management and monitoring structure.
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Cloud, Infrastructure and Security
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