Cloud infrastructure
Cloud infrastructure is the servers, databases, networking and release pipeline your website, applications and AI workloads run on. Corrplus designs and builds it, and puts migration off legacy servers, scaling for traffic spikes, automated deployments and the choice of where your data is stored into one written plan.
Book a free callTiming
Assessment usually takes 1–2 weeks; migration time depends on the size of the system
Pricing
Fixed-scope project for assessment and migration; the cloud bill goes straight to your own account.
Tools we use
Amazon Web Services (AWS), Microsoft Azure, Google Cloud, Cloudflare, Vercel, Supabase
Who it is for
- Companies whose site slows down or fails when a campaign, sale or press mention brings a traffic spike
- Small and mid-sized businesses running critical work on shared hosting or a single ageing server
- Teams that want n8n, a vector database or language model infrastructure running in their own cloud account
- Companies operating in Türkiye that must be clear about where personal data is stored under GDPR and KVKK (Türkiye's data protection law)
- Development teams still deploying by hand and nervous about every release
When it is not a fit
- Low-traffic brochure sites that rarely change. Good managed hosting does that job for less.
- Buyers who only want to rent servers or purchase hardware. We design and build infrastructure; we do not resell hardware.
What you get
- Current-state assessmentA written record of your servers, databases, domains and DNS, backup status and bottlenecks.
- Target architectureA diagram and decisions showing which component runs on which cloud service, in which region, and how it grows under load.
- Infrastructure as codeDevelopment, staging and production environments defined in Terraform or similar, so they can be rebuilt on demand.
- Automated release pipelineCode that is tested, approved and deployed without downtime through GitHub Actions or GitLab CI.
- Backup and recovery planScheduled backups, a tested restore and a step-by-step runbook for when something breaks.
- Monitoring and alertsA dashboard for availability, response time and resource use, with alerts routed to the right person.
How we work
Discover
Together we map your current infrastructure, traffic patterns, costs and the incidents that hurt most.
Design
We write down the target architecture, cloud provider, data residency and any cross-border transfer questions, then quote against that scope.
Build
We stand up staging first, migrate in short cycles and switch production over in a low-traffic window with a rollback plan.
Run
We watch load and cost closely in the first weeks and tune resources. If you prefer, the infrastructure stays with us as a managed service.
When does moving to cloud infrastructure make sense?
A site that slows down on campaign days, releases that need a late-night shift, or nobody knowing when the last backup ran: these are signs your infrastructure is behind your business. For e-commerce companies in Türkiye this usually shows up during sale periods and marketplace campaigns.
The cloud is not a fix on its own. The real work is deciding which component runs where, what grows when traffic rises and how you recover when something fails. We write those decisions down and build them as code, so any environment can be recreated on demand.
Which hosting model fits you?
| Aspect | Managed hosting | Single virtual server | Cloud architecture |
|---|---|---|---|
| Best for | Brochure sites that rarely change | Steady, predictable workloads | Fluctuating traffic, several applications |
| Under a traffic spike | Hits the provider’s limits | Scaled by hand | Scales automatically |
| Releases | Control panel or FTP | Manual or simple scripts | Automated pipeline |
| Backup and recovery | Up to the provider | Your responsibility | Defined and tested |
| Data protection | Ask the provider where servers are | You choose the location | Region choice and transfer assessment needed |
Not every company needs a cloud architecture. If the assessment shows a simpler model is enough, we say so in writing.
Infrastructure for AI workloads
AI projects add new demands: a vector database, a layer that manages model calls, self-hosted n8n and monitoring for all of it. We build these components to serve LLM integration and RAG knowledge assistant projects. For teams in Europe, this is also where GDPR and EU AI Act obligations meet concrete choices about regions and providers.
We run the same kind of setup in our own products, such as Okey 101 Çetele and Villa Yönet. The architecture we recommend to clients is one we already operate in production.
What happens after the build?
Infrastructure needs updates, security patches and cost tracking once it is live. Your team can own that; the documentation and handover are written for it. If you prefer, we keep monitoring and maintenance under managed services. For the website that runs on top, see WordPress and web development.
We start with a free first call. We talk through your current infrastructure and the problem that costs you most, and agree the scope of the assessment together.
How would this work in your business?
Tell us where you are in a free first call; we will say plainly where it makes sense to start.
Related services and use cases
Further reading
How to choose an LLM for your businessCriteria, access routes, data retention, open models and a test method for choosing a language model for business use.
KVKK and generative AI: a checklistFrom staff chat tools to API integrations: a step-by-step KVKK checklist for generative AI, with the differences from GDPR.
Microsoft 365 Copilot readiness checklistA step-by-step checklist for permissions, oversharing, labelling, the pilot and training before Copilot goes live.Our own products
We use the tools and checklists from client projects on our own products too.
All products →Key terms
- Vector databaseA vector database is a database that stores numeric vectors representing content such as text, images or audio and quickly finds the records closest in meaning to a query.
- KVKK (Turkish Personal Data Protection Law)KVKK is Türkiye's Personal Data Protection Law, Law No. 6698, which sets the rules for processing personal data and the rights of individuals in Türkiye.
- LLM (Large Language Model)An LLM (large language model) is an AI model trained on very large amounts of text that answers questions, summarises, translates and writes by predicting what text should come next.
Frequently asked questions
Which cloud provider should we choose?
It depends on the tools your team already uses, the region your data must stay in and the services you need. Azure is often natural for a Microsoft 365 company; Vercel and Cloudflare suit fast static websites. We give the recommendation in writing, with reasons.
Is the cloud always cheaper?
No. A steady, low-traffic workload can be cheaper on a single server. The cloud pays off where traffic fluctuates, downtime is expensive and releases need to be fast. The assessment includes an open cost comparison.
Will our data leave the country?
That depends on the region chosen. For systems with personal data we settle data location and cross-border transfer conditions during design, together with your legal counsel, under GDPR and KVKK.
Will the site go down during migration?
We aim for zero downtime. We build and test the new environment in advance, switch DNS in a low-traffic window and keep the old environment available for rollback for a while.
Whose name are the cloud accounts in?
Yours. Accounts, invoices and all access rights stay with your company. We work only with the permissions you grant.
Can we run AI models on our own infrastructure?
Yes. We can host n8n, a vector database and open-weight models in your cloud account, or use services such as Azure OpenAI so data is processed in the region you choose. The right route depends on data sensitivity and cost.
Cloud infrastructure: let’s take the first step together
Tell us where you are in one call; we will share a scope and roadmap in writing.
How we start
- A free first callWe pin down the problem and the goal together.
- A 2-week discovery sprintWe look at the data, the process and the tools, and decide where to start.
- A written proposalScope, architecture and timeline, in writing.