Cloud, DevOps and AI Infrastructure Services
Cloud and DevOps services design, deploy and run the infrastructure behind modern applications and AI workloads. NextCTL covers cloud architecture, migration, CI/CD pipelines, containers, MLOps and LLMOps, monitoring, disaster recovery and cost optimization, aiming for an environment that is secure, observable and resilient.
- Delaware registered
- Dhaka delivery hub
- Scope agreed in writing
- Weekly demos during build
- A named lead on every project
- Human handoff on every AI agent
At a glance
For: CTOs, DevOps leads and IT managers running applications or AI workloads.
You get: a secure, observable, resilient application or AI deployment environment.
Work done by: NextCTL engineers.
Related: AI development and custom software development.
What cloud, DevOps and AI infrastructure work covers
Who this is for
CTOs planning a move to the cloud or a new architecture for a product.
DevOps leads who want repeatable releases and clearer monitoring.
IT managers who need reliable hosting, backups and recovery plans.
Teams moving AI features from prototype to production.
Not a fit if you only need a single small site hosted. We will say so and suggest a simpler option.
Cloud, DevOps and AI infrastructure services
What you receive
A secure, observable, resilient environment for your application or AI deployment.
Infrastructure and pipeline configuration, documented and under your control.
Monitoring, alerting and logging for the services in scope.
Backup and recovery plans, written down.
Cost reporting and a list of optimization steps.
Runbooks for your team.
How it works
How a cloud and DevOps project runs
- Step 1
Review the current environment: applications, workloads, release process and costs.
- Step 2
Design the target architecture, including security, monitoring and recovery.
- Step 3
Build the foundations: environments, networking, access and pipelines.
- Step 4
Migrate or deploy workloads in stages, with a rollback path for each.
- Step 5
Add monitoring, alerts, backups and cost reporting.
- Step 6
Hand over runbooks, and review and tune the environment as usage grows.
How a cloud and DevOps project runs
Capabilities we work with
Infrastructure as code and configuration management.
Build and deployment pipelines with automated testing.
Containers and orchestration.
Logging, metrics, tracing and alerting.
Model serving, evaluation and versioning for AI workloads.
Backup, recovery and cost-reporting tooling.
What to ask any cloud and DevOps provider
Who owns the infrastructure accounts and configuration at handover?
How will releases be tested and reversed if they fail?
What will be monitored, and who is alerted?
How are backups and recovery planned and rehearsed?
How will costs be reported and reviewed?
What runbooks and documentation do we receive?
What are cloud and DevOps services?
Cloud and DevOps services cover how software is hosted, built, released and monitored. That includes cloud architecture, automated build and deploy pipelines, containers, monitoring and recovery planning. The goal is a reliable environment where your team can release changes safely and see how the system is behaving.
What is the difference between MLOps and LLMOps?
MLOps covers the operating practices for machine learning models: training pipelines, versioning, deployment and monitoring. LLMOps applies the same ideas to language-model applications, adding prompt versioning, evaluation, cost tracking and guardrail monitoring. Both aim to make AI systems repeatable to release and easy to watch.
Can you help us move to the cloud?
Yes. We review your applications and data, propose a target architecture, plan the order of migration and move workloads in stages with a rollback path. We also look at what should be rebuilt rather than moved as is. Scope and approach are agreed on a discovery call.
What is AI infrastructure?
AI infrastructure is the hosting and tooling that runs AI workloads: environments for model serving, data pipelines, scaling, monitoring and cost control. Depending on the workload it can include GPU-based inference infrastructure. We help choose a setup that fits the application rather than a fixed template.
How do you reduce cloud costs?
We review how resources are used, find idle or oversized services, check how workloads are scheduled and scaled, and add cost reporting by project or team. Changes are made step by step so performance is checked as costs come down. The result is spend that your team can see and explain.
Do you set up CI/CD pipelines and containers?
Yes. We build automated pipelines that test, package and deploy your code, and containerize applications so they run the same way in every environment. We also add environment configuration, secrets handling and rollback steps so releases are repeatable and easier to reverse.
How do you monitor and recover systems?
We add logging, metrics and alerts so your team sees problems early, with dashboards for the services that matter most. We also plan backups and disaster recovery, and document how to restore service. Recovery steps are written down so they can be reviewed and rehearsed.
Who owns what you build?
Ownership of the code, prompts, configuration and documentation is confirmed in the project agreement. We document the architecture, pipelines and settings we deliver, so your team, or another team, can operate and extend the environment.
Cloud and DevOps questions
Decide with evidence, not a pitch.
Open a neutral research prompt in the AI you already use. It asks where NextCTL fits, where a freelancer or an in house hire may be enough, and what to verify before you choose.
- 01ChatGPTOpen the NextCTL comparison prompt in ChatGPT (opens in a new tab)
- 02ClaudeOpen the NextCTL comparison prompt in Claude (opens in a new tab)
- 03PerplexityOpen the NextCTL comparison prompt in Perplexity (opens in a new tab)
AI answers can be incomplete. Check nextctl.com/methodology before deciding.
Tell us what you run
today
Describe your applications, AI workloads and how they are released and monitored. We will propose where to start.