Cloud-Native Azure Application Development Services

Built for the team that has to run it, at a monthly cost you agreed to in advance.

Two things sink Azure application projects, and neither is the code. The first is an integration nobody surveyed, which turns a six month plan into a twelve month one somewhere around month four. The second is a monthly run cost that nobody modeled while the architecture was still a choice, and which arrives as a surprise long after the project budget closed.

Cabot's Azure application development services settle both before design starts. We survey the integration surface and name an owner for each system the application has to speak to, and we model the monthly run rate of each architecture option at the point of choosing rather than after. Every managed service we recommend comes with its exit cost stated, because each one buys speed and sells a piece of portability, and that trade should be made deliberately. AI runs through delivery in defined stages, under review rules set out further down.

Most engagements open with a discovery increment that produces a target architecture, the integration risks with owners against them, and a run cost per option. Tell us what you are building or modernizing and what it has to connect to, and we will come back with those three things.

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See how we build on Azure See where AI sits in the build

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90%

of surveyed technology professionals now use AI at work, and more than 80 percent report a productivity gain, self reported.

59%

of technology professionals say AI has improved their code quality, while AI adoption still shows a negative relationship with delivery stability. The release controls around it decide which you keep.

49%

of organizations have governance guardrails in place for how AI is used in engineering work, which is the gap that decides the outcome above.

What Azure application development services actually cover

Azure application development is the design and engineering of software that runs on Microsoft Azure, covering new cloud native builds, the incremental modernization of applications that already run, and the integration and data work that connects them to the systems around them.

It is a different engagement from Azure consulting, and the distinction is worth holding onto because buyers are frequently sold the wrong one. Consulting decides what the environment should be: what moves, what it costs, who can do what inside it. Application development builds the software. If the platform questions are still open, advice comes first. If they are settled, more advice is an expensive way to delay a build.

Within application work the decision that carries the most weight is which Azure managed services to adopt. App Service, AKS and Functions each buy you speed and each take something back in portability and operational weight. There is no universally right answer, and the right one depends less on the workload than on the team who will be carrying a pager for it at two in the morning.

Why Azure application projects slip

These are the six patterns behind most of the overruns we are asked to rescue. Only one of them is really about code.

The integration was never surveyed. The application was estimated on its own features, then met a system with no API, an owner with a queue and a governance process nobody had timed. The build was never the long pole.

Run cost arrived after the decision. The architecture was chosen on build effort and elegance, and the monthly bill turned up later as somebody else's problem. That figure outlives the project budget by years.

Cloud native was assumed rather than chosen. Containers and microservices were adopted because they are the default answer, adding operational weight a small team could not carry once the delivery partner left.

The rewrite was chosen over the increment. A working application was replaced wholesale because the new architecture was cleaner, and two years later the organization had newer software that did the same thing.

Lock-in was accepted by default. Managed services were adopted for speed with nobody asking what leaving would cost. A fine trade if made knowingly, and an unpleasant discovery if not.

The data boundary moved late. Residency, retention and access were settled after the data model rather than before, and shifting a boundary in production costs several times what drawing it early would have.

Placeholder illustration for cloud native Azure application development services

The Azure application work we do

Three service lines. Most engagements are a mix, because a new application almost always has to live beside something that already exists and talk to it.

Two adjacent capabilities sit outside this page rather than inside it. If the platform decisions are still open, that is Azure consulting and it should come first. If the users are clinicians or patients, the compliance and adoption constraints shape the build from day one and that work runs through healthcare application development. We would rather point you there than quote for the wrong engagement.

Cloud native application builds

New applications built for Azure rather than moved onto it, using App Service, AKS or Functions according to what the workload actually needs. The decision that matters is which managed services to adopt, because each one buys speed and sells a piece of portability.

Application modernization on Azure

Taking a .NET estate or a monolith that already runs and making it something you can change safely. Usually incremental: strangle the parts that hurt, leave the parts that work, and resist the rewrite that sounds cleaner and takes two years.

Integration and data engineering

APIs, event driven messaging and the data pipelines behind them. On most Azure programmes the integration surface is the long pole rather than the application, so we survey it before committing to a delivery date.

What does an Azure application cost to build, and what does it cost to run?

Two numbers, and most quotes only give you the first. Build cost tracks with the integration surface and the number of user groups. Run cost tracks with the architecture, and we model it per option during discovery so the recurring figure is visible while the design is still a choice rather than after it has hardened.

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See how we build on Azure See where AI sits in the build

What we settle before designing anything

Four questions in a fixed order. Each one changes the architecture rather than the backlog, which is why they come before design rather than during it.

Whether it should be cloud native at all

Containers and microservices add operational weight that a small team may not be able to carry. We size the architecture to the team that will run it, not to the diagram that presents well.

What the integration surface really is

Which systems this has to speak to, who owns each one, and how long their approval takes. That answer sets the timeline far more reliably than the size of the application itself.

Where the data actually lives

Residency, retention and who can read it, settled before the data model rather than after. Moving a boundary once an application is in production is expensive in a way that is easy to underestimate.

What it costs to run, not just to build

Architecture decisions are recurring cost decisions. We model the monthly run rate of each option at the point of choosing, because that number outlives the build budget.

What does AI actually change inside an Azure build?

The evidence is more useful than the marketing. DORA's 2025 research found that AI adoption correlates with higher delivery throughput where strong engineering practice already exists, and with worse delivery stability where it does not. Both move at once. Which one you are left holding is decided by the release controls around the work rather than by the model doing the typing.

That finding sets where we use it. AI drafts where a wrong answer is cheap to catch: test cases from acceptance criteria, boilerplate and data access code, infrastructure definitions for an agreed environment, and a first pass of review comments a person would rather not spend attention on. Each of those meets a test suite or a named engineer within minutes, which is what makes the speed safe rather than merely fast.

It does not choose the architecture, decide which managed services to adopt, judge what finished means, or approve a release. Those are the decisions with long tails, and the ones you are engaging an engineering team for. Digital.ai's 18th State of Agile Report, published in October 2025, found that only 49 percent of organizations have governance guardrails for AI in place, which is why ours sit in the contract.

Three rules govern every engagement. We are model neutral, choosing tools per task rather than committing your delivery to one vendor. A named engineer reviews every AI assisted change before it merges and is accountable for it as if they had written it. And we do not train models on your code or your data, under any arrangement.

The Azure stack we build on

We work in your environment with your constraints rather than importing a stack of our own. Where your organization already standardizes, we use what you have. The table further down sets out where AI sits at each stage of delivery, what stays with a person, and which tools are involved.

What we work in:

Application runtime

.NET

App Service

AKS

Functions

Container Apps

React

Data and integration

Azure SQL

Cosmos DB

Service Bus

Event Grid

API Management

Data Factory

Delivery and quality

Azure DevOps

GitHub Actions

Bicep

Playwright

SonarQube

Application Insights

Where AI sits at each stage of an Azure build, and where it does not

Each stage below has an output you can inspect and a person who approves it. Nothing an AI drafts reaches your repository or your environment without a named engineer accepting it first.

Build stage	What AI does	What stays human	Tools used
Discovery and architecture	Summarizes requirement threads into decisions, drafts acceptance criteria, and compares run cost across architecture options from published rates.	Choosing the architecture, deciding which managed services to adopt, and accepting the portability trade each one carries.	Claude, Azure Pricing
Build	Drafts boilerplate, data access layers and repetitive transformations, completing code against patterns already in the repository.	The data model, interface contracts, and any decision that will be expensive to reverse once live.	GitHub Copilot, Claude Code
Code review	Raises a first pass of issues: unhandled errors, missing null checks, obvious security patterns, style drift.	The merge decision. A named engineer signs off every change and owns it afterward.	GitHub Copilot, SonarQube
Environments and test	Drafts Bicep for an agreed environment, and generates unit and integration cases from acceptance criteria.	Deciding what must be tested, what risk is acceptable, and approving any change to a shared environment.	Bicep, Playwright
Release and operate	Drafts release notes from merged changes and groups Application Insights errors into probable root causes.	Go or no-go, the rollout plan, and reading what live usage and run cost mean for the next increment.	Azure DevOps, Application Insights

How a Cabot build differs from a typical Azure development partner

Four dimensions on which Azure application projects succeed or fail, chosen because they are where they go wrong rather than where they are easy to describe. Each row is a mechanism you can hold us to, not a promise.

Dimension	Typical Azure partner	Cabot
When run cost is discussed	After go live, when the first full month's bill arrives and the architecture is no longer a choice.	Modeled per option during discovery, so the recurring figure is on the table while the design is still open.
How the integration is estimated	From the feature list, with interface availability and approval queues discovered during the build.	Surveyed first, with an owner named for every system the application must speak to, because that sets the date.
How the architecture is chosen	Cloud native by default, sized to what presents well rather than to who will operate it.	Sized to the team carrying the pager, with the portability cost of each managed service stated at the point of choosing.
Modernization versus rewrite	A rewrite, because it is cleaner to build and larger to sell.	Incremental by default. We will argue against a rewrite that replaces working software with newer software that does the same thing.

The teams we build Azure applications for

We work with three kinds of organization. Each entry says what we understand about that market rather than asserting that we serve it. Regression, performance and release coverage on every build comes from our QA and testing practice, and where the platform underneath still needs work that sits with our cloud enablement team.

Healthcare and regulated products

The application has to produce its own evidence as it is built, not reconstruct it before a review. Requirements traced to tests and access to sensitive data logged from the first working version cost very little early and a great deal late.

Enterprises with a .NET estate

The question is rarely whether it can move. It is which parts are worth moving, in what order, and what stays on the existing platform indefinitely because moving it buys nothing.

Product companies scaling on Azure

Tenancy decisions made in the first month determine whether cost per customer can ever be calculated and whether an enterprise buyer's isolation requirement can be met without a rebuild.

Industries we already understand

volunteer_activism

Healthcare

shopping_cart

Ecommerce

paid

Fintech

apartment

Travel and Tourism

How security and evidence stay inside the build

Security is engineering work with a place in the definition of done rather than a review at the end. On Azure that means managed identities instead of credentials sitting in application settings, private networking as the default rather than the hardening pass, secrets in Key Vault, dependencies and container images scanned on every build, and environments defined as code so a control is a file rather than something somebody remembers configuring.

Where the product is regulated, the evidence is produced by the build rather than assembled before a review. Requirements traced to tests, test results retained per release, and access to sensitive data logged from the first working version. That discipline costs very little when it starts in the first increment and a great deal when it starts in the last one. Our compliance practice covers the wider programme, and where the application is clinical the additional obligations are scoped at healthcare application development.

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Managed identities, no stored secrets

Private networking by default

Dependency and container scanning

Environments as code

Requirements traced to tests

HIPAA scoped where applicable

How an Azure build actually runs

Three stages, each ending in a decision you make rather than a document you receive. The first stands on its own and is worth having even if you build with someone else.

Discovery and architecture

We settle the integration surface, the data boundary and the run cost of each architecture option before designing anything. You end this stage with a target architecture, a named set of integration risks with owners against them, and a monthly run rate for each option. You decide which one is worth building.

Build in reviewable increments

Environments as code first, then the application in slices you can open and use. Testing runs inside the increment, a named engineer reviews every change before merge, and scope changes are priced against what they displace when they are raised.

Release, operate and evolve

Release with monitoring and rollback rehearsed rather than assumed, then a backlog fed by what usage shows. The run cost is reported against the model from stage one, so architecture assumptions get checked against reality.

Who is on the team and what each person owns

Accountability is explicit from the first day. A tech lead owns the architecture and the merge decisions and answers for every change that ships. Engineers own their slices end to end rather than handing work to a separate integration group. A quality lead owns the automated suite and what it proves. A delivery lead owns the cadence and keeps your stakeholders current between demos. Every decision has a named owner you can reach directly.

Our depth shows in four specific places. We are strong at .NET modernization where the question is which parts are worth moving rather than whether they can be. We are strong at the integration surface, which is where Azure programme dates are actually set. We are strong at building products that have to produce audit evidence as they go. And we are strong at sizing an architecture to the team that will operate it, which sometimes means recommending something less fashionable than the alternative. Outside those four we will point you at a specialist rather than take the work.

On working arrangements, a distributed team commits to a written overlap window with yours and batches the decisions that need you into it. Architecture decisions are recorded in your repository as they are made, so the reasoning survives a rotation. When the team needs to grow, you can extend the team already running rather than opening a second one from scratch.

Why technology leaders choose Cabot for cloud-native Azure application development

Shipping fast is only worth it if the result is operable and affordable. These are the six reasons buyers give when they explain why they moved their Azure build work to Cabot.

Portability is a decision, not an accident

Every managed service adopted buys speed and sells portability. We state that trade at the point of choosing so it is made deliberately rather than discovered by whoever inherits the system.

Run cost modeled before the build

The monthly figure outlives the project budget and is rarely discussed until it arrives. We put it on the table while the architecture is still a choice.

Integration risk surfaced first

Interfaces, owners and approval queues are established during discovery rather than found in month four, because that is where Azure programme timelines actually fail.

We resist the rewrite

Modernization is usually incremental. A full rewrite is occasionally right and far more often a two year project that replaces working software with newer software that does the same thing.

AI in delivery with review before merge

AI drafts tests, boilerplate and first pass review comments. A named engineer owns every change before it merges, and we do not train models on your code or your data.

Sized to the team that will run it

An architecture your team cannot operate is a liability whatever it looks like on a diagram. We design for the people who will be on call, and say so when that means something less fashionable.

Where to go next, depending on what is in your way

Azure application development is the build layer. Take whichever of the three below matches the question you have not answered yet.

The platform decision is not settled yet

If what should move and what it should cost are still open questions, start with an assessment. Go to Azure consulting and readiness assessment.

The application is clinical or patient facing

If clinicians or patients are the users, adoption and compliance shape the build from day one. Start at healthcare application development.

Shipping is the problem, not the architecture

If the design is sound and releases are what hurt, the constraint is the pipeline. Start at DevOps in healthcare.

Our Clients

Questions technology leaders ask before starting

What are Azure application development services?
They are the design and engineering of software that runs on Microsoft Azure, covering new cloud native builds, incremental modernization of applications that already run, and the integration and data work connecting them to surrounding systems. It is distinct from Azure consulting, which decides what the environment should be rather than building software inside it.
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How much does an Azure application cost to build?
Build cost tracks with the integration surface and the number of distinct user groups far more than with feature count. There is a second number most quotes leave out, which is what the architecture costs to run every month. We model that per option during discovery. You can get a starting range from our cost calculator.
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Should we modernize our .NET application or rewrite it?
Almost always modernize incrementally. Strangle the parts that hurt, leave the parts that work, and ship continuously while doing it. A full rewrite is occasionally right, usually when the platform is genuinely at end of life, and far more often a two year project that replaces working software with newer software that does the same thing. We will argue that case rather than quote for the larger option by default.
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Do we need AKS, or is App Service enough?
App Service is enough for most applications, and it is cheaper to operate. AKS earns its operational weight when you genuinely need fine grained scaling, complex networking or workload portability, and it costs you a platform capability you must then staff. The right answer depends less on the workload than on who will be carrying the pager for it.
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How long does an Azure application build take?
The variable is almost never the application. It is the integration surface, and specifically how many systems it touches, who owns each one, and how long their approval takes. We survey that during discovery so the date reflects it. Any estimate given before those three answers exist is decorative.
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What happens to our data residency and retention requirements?
They are settled before the data model rather than after. Where data lives, how long it stays and who can read it are architecture constraints, and moving a boundary once an application is in production is expensive in a way that is easy to underestimate from the outside.
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How locked into Azure will we be?
More than you would be on plain containers, less than the word lock-in suggests. Every managed service you adopt buys development speed and sells portability, and we state the exit cost of each one at the point of choosing so the trade is deliberate. If portability matters more than speed on your product, we will design for that instead.
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Do you use AI in the build, and does our code train a model?
AI drafts tests, boilerplate, infrastructure definitions and first pass review comments. It does not choose the architecture or approve a release. A named engineer reviews every AI assisted change before it merges and is accountable for it. We do not train models on your code or your data under any arrangement.