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Azure cloud consulting that leaves a setup your team understands. Azure cloud consulting for growing teams: App Service and VMs, Azure DevOps pipelines, Azure OpenAI deployments, monitoring and cost control in your tenant.
We design, build and run applications on Microsoft Azure. That means choosing between App Service, Container Apps and virtual machines for each workload, wiring up Azure DevOps or GitHub Actions pipelines, hosting Azure OpenAI features with private networking and sensible quotas, and setting up monitoring and budgets from the first week. Everything lives in your own subscription, is written as code where it makes sense, and comes with documentation your team can follow.
Why teams call us about Azure. Resources created by hand in the portal Nobody can say which settings matter, and rebuilding an environment or adding a second region means clicking through it again from memory.
Deploys cause downtime Code is pushed straight to the production App Service or copied onto a VM, so every release restarts the site in front of users.
An Azure OpenAI prototype that is not ready for real users Keys sit in config files, the endpoint is public, quota errors appear under load and nobody is sure what the monthly token spend will be.
A bill that is hard to explain Oversized plans, idle VMs, unattached disks and premium tiers chosen during a trial keep running because nobody owns the cost view.
What our Azure work covers. Landing zone and access Subscriptions, resource groups, naming and tagging, least-privilege Azure RBAC roles assigned to Microsoft Entra ID groups, and budgets with alerts before workloads arrive.
App Service hosting Web apps and APIs on the right plan tier (Standard or above when you need deployment slots), with staging slots, custom domains, managed certificates and autoscale rules where the tier supports them.
Virtual machines Linux VMs for workloads that need full control: hardened images, NSG rules, Azure Bastion or IP-restricted SSH, patching and Azure Backup policies.
Azure DevOps pipelines YAML pipelines in Azure DevOps or GitHub Actions that build, test and deploy to slots, with workload identity federation instead of stored secrets.
Azure OpenAI hosting Model deployments with quotas planned per environment, managed identity access, private endpoints, content filtering settings and usage logging.
Data and storage Azure Database for PostgreSQL flexible server, Blob Storage and Azure AI Search, with backups, retention and network rules set deliberately.
Monitoring Application Insights and Azure Monitor with a Log Analytics workspace, availability tests and alert rules that reach the person who can act.
Infrastructure as code Bicep or Terraform for the environment, kept in your repository so staging and production are built from the same definitions.
Engagement options Azure build A fixed-scope project to set up a new Azure environment or move an application onto App Service, VMs or Container Apps.
Architecture and service selection in writing Environment built as code in your subscription Pipelines with staging slots and rollback Documentation and a hand-over session Managed Azure A monthly retainer where we run your Azure workloads next to your developers.
Patching, scaling and plan reviews Monitoring, alerts and incident response Monthly cost and Advisor review Coverage hours and response times agreed in the retainer Azure review A short assessment for teams already on Azure who want an outside view before changing anything.
Review of resources, access and networking Security and reliability findings Cost observations by resource group A written, prioritised action plan How it works Scoping call We go through your applications, current hosting, Microsoft licensing and what the Azure environment needs to do.
Review and design With read access to your subscription we map what exists, then send a design with service choices, estimated running costs from the Azure pricing calculator and the order of work.
Build in two-week increments Environments, pipelines and data stores are built as code, tested on staging and switched over once they are proven.
Hand over or run You receive the code, runbooks and access in your tenant. On a retainer we keep it patched, monitored and costed each month.
Why Infigini We have shipped Azure OpenAI to production We built medical and enterprise knowledge assistants on Azure OpenAI, Azure AI Search and App Service, so we know where quotas, retrieval and networking catch teams out.
Developers who also run infrastructure The same engineers write the FastAPI, Django and Angular code, so hosting and pipelines fit how the application actually works.
Your tenant, your ownership Subscriptions, code, pipelines and documentation sit in your accounts from day one. You can take it in-house at any point.
Not tied to one cloud We also run AWS and self-hosted servers, so if a workload is cheaper or simpler elsewhere we will say so in writing.
Technologies: Azure App Service, Azure Virtual Machines, Azure Container Apps, Azure DevOps, GitHub Actions, Azure OpenAI, Azure AI Search, Azure Database for PostgreSQL, Application Insights, Microsoft Entra ID, Bicep, Terraform
Related projects Medical Knowledge Assistant — A conversational assistant that answers from custom knowledge bases hosted on Azure.
Enterprise Knowledge Assistant — A second RAG assistant for conversations over private knowledge bases on Azure Cloud.
Frequently asked questions What does an Azure cloud consulting engagement include? It usually starts with a review of what you have, or a design if you are starting fresh. We then build the environment as code in your subscription: hosting on App Service, Container Apps or VMs, databases and storage, Azure DevOps or GitHub Actions pipelines, monitoring and budgets. The exact scope, deliverables and order of work are written down before we start.
Should we use Azure App Service or virtual machines? For most web apps and APIs, App Service is the better default. Microsoft patches the OS and runtime, and TLS certificates, scaling and staging slots are built in. We choose VMs when a workload needs OS-level control, software App Service cannot run, or specific licensing. Containerised services with several parts often fit Azure Container Apps. We explain the choice for each workload in the design.
Can you host our Azure OpenAI application securely? Yes. We put the application on App Service or Container Apps, call Azure OpenAI with a managed identity rather than API keys, place the OpenAI and AI Search resources behind private endpoints where the design needs it, and store any remaining secrets in Key Vault. We also plan model quotas per environment and log usage so token spend can be tracked.
Do we have to use Azure DevOps if we are on Azure? No. Azure DevOps Pipelines and GitHub Actions both deploy to Azure well, and both support workload identity federation so no long-lived secrets are stored. If your code is already in GitHub we usually keep it there. If you use Azure Boards and Repos, we build in Azure DevOps. We do not move you between tools without a clear reason.
How is Azure consulting priced? Build projects are quoted at a fixed price once the review or scoping is done, based on the number of applications, environments and integrations. Managed Azure retainers are a fixed monthly fee based on the size of the environment and the support hours you need. Azure usage itself is billed by Microsoft to your subscription, and we give you an estimate of it in the design.
Can you reduce our existing Azure bill? Often, yes. We look at App Service plan tiers and instance counts, idle or oversized VMs, unattached disks, old snapshots, log retention and database sizing, then check where reservations or savings plans would make sense for steady workloads. You receive a list of changes with the expected effect of each, and nothing is removed without your approval.
Can you migrate our application from on-premises or another cloud to Azure? Yes. We move applications in stages: build the target environment as code, copy and sync the data, run the new setup alongside the old one on staging, then switch DNS once it is proven. Rollback steps are written before cut-over. If parts of your system would be simpler or cheaper on AWS or your own servers, we will tell you.
Do you work with companies outside India? Yes. We are based in Dehradun and work remotely with clients in India and abroad. Your Azure resources can sit in whichever region suits your users and data rules. Coverage hours and response times for monitoring and incidents are agreed in the retainer.
Further reading Azure App Service or containers on a VM? How to choose in 2026
Azure DevOps pipelines for web apps: a practical YAML setup
AWS vs Azure for a growing product team: how to choose in 2026
RAG on Azure OpenAI: notes from two knowledge assistants
Qdrant or Azure AI Search for retrieval?
Infigini IT Services · 11 Kanwali Road, Dehradun 248001, India · info@infigini.com · +91 95684 87789