NeoTek Solutions plans, builds, migrates and runs cloud and infrastructure services on Microsoft Azure, Amazon Web Services (AWS) and Google Cloud. We work with IT and operations leaders at mid-size and enterprise organizations who need a dependable place to run applications, data and AI workloads. Our headquarters is in Nashville, we serve clients across the US, and we stay vendor-neutral, so the platform we recommend follows your contracts, skills and workloads.
Many organizations already have cloud accounts set up by different teams, unclear security settings and bills nobody can explain. A well-built foundation makes every later project, including AI, faster and safer to deliver.
Who Needs Cloud and Infrastructure Services?
Our cloud work fits IT directors, CIOs and operations leaders. It is a good match if you:
- Are moving applications off aging servers or out of a data center
- Have cloud accounts that grew without a clear structure or owner
- See cloud bills rising without a clear reason
- Need infrastructure that holds up in security audits and compliance reviews
- Want to run AI models and applications in production with proper controls
- Lack in-house cloud or DevOps engineers for a project or ongoing support
What Do Our Cloud and Infrastructure Services Include?
Cloud Strategy and Migration
We review your applications, data, contracts and skills, then recommend what to move, when and where. Each workload gets the right pattern: rehost to virtual machines, replatform to managed services or refactor into cloud-native designs. Test migrations and rollback plans keep disruption low. For aging applications, see legacy application modernization.
Landing Zones and Account Structure
A landing zone is the secure starting structure for your cloud. We organize accounts, subscriptions or projects by environment and team, with identity, policies, logging and budgets applied from day one. New projects then start from approved patterns instead of one-off setups.
Networking and Connectivity
We design virtual networks, subnets, firewalls and DNS for each environment. Site-to-site VPNs or dedicated connections link the cloud to your offices and data centers. Private endpoints, such as Azure Private Link, AWS PrivateLink or Private Service Connect, keep traffic to databases and AI services off the public internet.
Compute, Containers and Kubernetes
We choose the right compute for each workload: virtual machines, serverless functions, managed containers or Kubernetes. For container platforms, we set up Azure Kubernetes Service (AKS), Amazon EKS or Google Kubernetes Engine (GKE) with autoscaling, secure images and clear upgrade processes.
Infrastructure as Code and Monitoring
We define environments in code with tools such as Terraform, Bicep or AWS CloudFormation, so changes are reviewed, repeatable and easy to rebuild. We also set up monitoring, centralized logging and alerts for performance, errors and security events, so your team sees problems early.
Backup and Disaster Recovery Planning
We help you decide how much data you can afford to lose and how quickly each system must return. We then design backups, cross-region replication and tested recovery runbooks to match.
Ongoing Support, Security and Cost Optimization
After launch, we can maintain your infrastructure, apply updates, review security findings and tune performance. We tag resources, set budgets and right-size servers so costs stay tied to real use. For deeper security work, see our cybersecurity services.
What Is AI DevOps?
We apply proven software delivery practices to your AI applications and models. That covers how models, prompts and data move from experiment to production, how we check quality before release and how we monitor AI systems after launch.
CI/CD, Versioning and Evaluation Gates
CI/CD pipelines, the automated systems that build, test and deploy software, work for AI too. We version models, prompts, datasets and configuration together, so every release can be traced and rolled back. Evaluation gates run test sets on each change and block releases that reduce accuracy or safety.
MLOps and LLMOps Pipelines
We use MLOps to run your machine learning models reliably in production, and LLMOps to do the same for large language models (LLMs). We build pipelines for training, fine-tuning, registration and deployment, following our MLOps platform architecture. Our machine learning and data engineering team supplies the data pipelines behind them.
GPU and Inference Infrastructure
Some AI workloads need GPUs for training or for serving models your team hosts. We help you choose between managed model services, such as Azure OpenAI, Amazon Bedrock or Vertex AI, and self-hosted models. We size GPU capacity, set up autoscaling and plan for availability limits.
Cost, Quality and Drift Monitoring
We track token usage, GPU hours and cost by application and team. Dashboards also watch response quality, latency and errors. Drift monitoring flags when incoming data or model behavior changes over time. An LLM gateway with guardrails gives you one place to apply these controls.
Azure, AWS or Google Cloud?
All three are mature platforms. We are vendor-neutral and recommend the one that fits your contracts, skills and workloads.
Microsoft Azure
We often recommend Azure to organizations already running Microsoft 365, Windows Server, SQL Server and .NET applications. We use Microsoft Entra ID for identity and single sign-on. We build generative AI on Azure OpenAI inside your own environment, and run containerized applications on AKS.
Amazon Web Services (AWS)
We build on AWS where its wide range of services fits your workloads. We use AWS Identity and Access Management (IAM) for detailed permissions across accounts. We reach several model providers through Amazon Bedrock, and run containers at scale on Amazon EKS.
Google Cloud
We suggest Google Cloud for data-heavy and machine learning work, using BigQuery for large-scale analysis. We build on Vertex AI for Gemini and other models, with its tools to tune and monitor them. We run containers on Cloud Run without managing servers, and use GKE for larger Kubernetes workloads.
| Building block | Microsoft Azure | AWS | Google Cloud |
|---|---|---|---|
| Virtual machines | Azure Virtual Machines | Amazon EC2 | Compute Engine |
| Managed Kubernetes | AKS | Amazon EKS | GKE |
| Identity and access | Microsoft Entra ID | AWS IAM | Cloud IAM |
| Generative AI models | Azure OpenAI | Amazon Bedrock | Vertex AI |
How Does a Cloud and Infrastructure Engagement Work?
-
Assess
We inventory your applications, data, integrations, security requirements and current costs, and show what should change first.
-
Plan and Design
We choose the platform and migration pattern for each workload, then design the landing zone, network, security and recovery approach. Each phase delivers value on its own.
-
Build the Foundation
We build the landing zone with infrastructure as code and set up identity, networking, monitoring, backups and budgets. We test it before any production workload moves.
-
Migrate and Deploy
We migrate workloads in waves, with test runs, data checks and rollback plans. New applications and AI models ship through the same reviewed pipelines.
-
Operate and Optimize
After launch, we monitor performance, security findings and costs. We can support your infrastructure long term, or hand over documentation and train your team to run it.
Why NeoTek Solutions
- Vendor-neutral adviceWe recommend Azure, AWS, Google Cloud or a hybrid setup based on your needs, not a sales target.
- Security from the startIdentity, logging, encryption and private networking are part of the first design, not added later.
- AI-ready infrastructureWe build AI solutions ourselves, so we design for models, pipelines and monitoring.
- Flexible capacityThrough AI and IT staffing, we can add cloud, DevOps and MLOps engineers to your team.
- Local teamWe are headquartered in Nashville, with a second office in Hyderabad, India, and serve organizations across the US.
Related Services
How Can NeoTek Solutions Help With Your Cloud?
We can run the whole program, or take the parts your team has no capacity for.
- Work we deliverCloud migration, landing zones, networking, Kubernetes, infrastructure as code, backup and recovery planning, and AI DevOps pipelines for your applications, data and models.
- Across all three platformsAzure, AWS and Google Cloud, with hybrid, open-source or on-premises options where your data residency or contracts call for them.
- How we workShort cycles with AI-assisted delivery and human review, acceptance criteria agreed early and each phase delivering something you can use on its own.
- Skills on the teamCloud and security specialists, software engineers, data engineers, AI and machine learning engineers and QA, so infrastructure, applications and models are covered by one team.
- What makes us differentWe are not reselling one platform, and because we build AI systems ourselves, the infrastructure accounts for models, pipelines and monitoring from the first design.
- You stay in controlYou keep the infrastructure code, documentation and runbooks, so your team can run the environment whenever you choose, and your data never trains public models.
Bringing us in before the migration date is set usually leaves more room to plan around it. Book a free AI consultation and we will map the options against what you already run.
Frequently Asked Questions
Which cloud would you recommend for us?
We recommend based on your existing software, contracts, team skills and workloads, not on what we resell. Organizations built around Microsoft 365 and .NET often start with Azure. We suggest AWS for its breadth of services, and Google Cloud for analytics and machine learning work. We compare the options with you and explain the reasoning.
Can you help us migrate without downtime?
We plan every migration to keep disruption as low as possible. We use test migrations, data replication and staged cutovers scheduled around your business calendar. If an older system needs a short planned outage for the final cutover, we agree on the timing with you in advance and keep a rollback plan ready.
What does AI DevOps add to our pipelines?
We start from the DevOps practices that build, test, deploy and monitor your software. On top we add versioning for models, prompts and data, plus evaluation gates that test output quality before release. We also size GPU and inference infrastructure and monitor drift, token costs and response quality.
Can you build a cloud environment for our healthcare or financial data?
Yes. Azure, AWS and Google Cloud offer strong controls, and we configure them properly with encryption, least-privilege access, private networking and logging. We design for HIPAA and financial-services requirements and work within your compliance program. We claim no certification, and your compliance and legal teams make the final decisions.
Do you provide ongoing infrastructure support?
Yes. We can monitor and maintain your environment, apply updates, review security findings and optimize costs and performance. Support is scoped to your needs. If you prefer to run it yourself, we hand over infrastructure code, documentation and runbooks and train your team.