It is a service for running small units of code in the cloud, triggered by events, without managing servers.

Normally, to run a program in the cloud, you create a server, install software, keep the server running day and night and look after its updates. The program waits for work, and you pay for the server whether work arrives or not. This is wasteful when the work comes only now and then.

Azure Functions offers a different way. You write a small function, which is a piece of code that does one job, such as resizing a picture, processing an order or sending a message. You tell Azure what should start it: a web request, a timer, a new file, a message in a queue or many other events. When the event happens, Azure runs your function. When the work is done, the resources are released. You do not manage servers, and you pay only for the time and resources that your code uses.

This style is called serverless computing. The word does not mean that there are no servers. It means that you do not see or manage them. Azure decides where and how to run your code, and adds or removes capacity automatically as the number of events changes.

Words you will meet again and again

Because a lot of useful work is small, occasional and driven by events.

Think about the tasks that make up a typical business system. When a customer uploads a photo, a thumbnail must be made. When an order is placed, a confirmation email must be sent. Every night, old records must be cleaned up. When a sensor reports a high temperature, an alert must be raised. None of these tasks needs a whole server running all day. Each needs a small piece of code at the right moment.

Building these tasks on traditional servers means paying for idle time, planning capacity for the busiest moment and handling updates and failures yourself. Azure Functions removes this burden so that developers can focus on the logic, and the platform handles the scaling and infrastructure.

Four simple ideas explain almost everything about how Azure Functions is organised.

flowchart LR
  T["Trigger: new message in queue"] --> F["Function: process order"]
  IB["Input binding: read customer record"] --> F
  F --> OB1["Output binding: write to database"]
  F --> OB2["Output binding: send email message"]
        

Why bindings matter

Without bindings, your code would need to open connections, handle authentication and manage details for every service it touches. With bindings, you declare what you need, and the runtime does the plumbing. This keeps functions short and focused. Bindings are optional, and you can always use the standard software libraries of a service directly when you need more control.

A look behind the curtain explains scaling, cold starts and many surprises.

Each function app runs on one or more instances. An instance is a virtual machine or container that hosts the Functions runtime, which is called the host, together with your code. The host listens for trigger events, calls your function with the event data, handles bindings and reports results and logs.

A separate component, the scale controller, watches the event sources of your function app. For example, it looks at the length of a queue or the lag of an event stream. When it sees more work than the current instances can handle, it adds instances. When work decreases, it removes them. On plans that scale to zero, it can stop all instances when there is nothing to do and start one when an event appears.

sequenceDiagram
  participant S as Event Source
  participant C as Scale Controller
  participant H as Functions Host Instance
  participant F as Your Function Code
  participant O as Output Service
  S->>C: New events waiting
  C->>H: Start or add instance
  H->>S: Read event
  H->>F: Invoke with event data
  F->>O: Write result
  F-->>H: Finished
  H->>S: Mark event as processed
        

The list of triggers is the best way to see what Functions can do for you.

Examples of triggers with bindings

The hosting plan is the most important choice you make for a function app.

flowchart TD
  Q["Choose a hosting plan"] --> A{"Need private networking or fast scale with no idle cost?"}
  A -->|"Yes"| FX["Flex Consumption"]
  A -->|"Need constant warm instances and long runs"| PR["Premium"]
  A -->|"Already have spare App Service capacity"| DD["Dedicated"]
  A -->|"Standardised on containers"| CA["Container Apps hosting"]
        

Understanding cold starts

When no instance is running and an event arrives, a new instance must start, load the runtime and your code, and then process the event. This extra delay is the cold start. It may be a fraction of a second or several seconds depending on the language, the size of your code and its dependencies, and the plan.

Scaling happens at two levels: more instances, and more work at once on each instance.

Scaling out means adding instances. Concurrency means how many executions run at the same time on one instance. Both matter. If each function call is quick and light, one instance can run many in parallel. If each call is heavy, more instances are needed sooner.

Ordinary functions are short and stateless. Durable Functions add long-running, stateful workflows.

Some jobs take many steps over minutes, hours or days. An order might need payment, then stock reservation, then shipping, with waiting between steps and the possibility of failure at any point. Writing this with plain functions means building your own state tracking, retry logic and timers. Durable Functions is an extension that does this for you.

In Durable Functions, you write an orchestrator function that describes the workflow as ordinary code: do this, then wait for that, then do these three in parallel. The orchestrator calls activity functions, which perform the real work. The framework saves progress to storage after each step. If the instance stops, the workflow resumes from where it left off, by replaying the orchestrator’s history.

flowchart TD
  S["Start orchestration"] --> A1["Activity: validate order"]
  A1 --> A2["Activity: charge payment"]
  A2 --> P["Fan out"]
  P --> B1["Activity: reserve item 1"]
  P --> B2["Activity: reserve item 2"]
  P --> B3["Activity: reserve item 3"]
  B1 --> J["Fan in: combine results"]
  B2 --> J
  B3 --> J
  J --> A3["Activity: create shipment"]
        

In a world of failures, good functions assume that things will be tried more than once.

Most event sources in Azure Functions deliver messages at least once. This means that a message is never lost, but it can occasionally be delivered more than once, for example after a crash or a timeout. If your function is not prepared for repeats, you may charge a customer twice or send duplicate emails.

Where you put settings decides how safe and flexible your function app is.

A good workflow lets you build on your own machine and release safely to the cloud.

Functions are small, but they are often public entry points to your systems, so protect them well.

Functions often need to reach private systems, and sometimes they must be reachable only privately.

Because functions run unattended and in parallel, good telemetry is essential.

Azure Functions integrates with Application Insights, a monitoring service that collects information about every invocation, including duration, success or failure, dependency calls and exceptions. It also draws maps of how your functions and the services they use are connected, and traces a request across several functions.

Functions can be extremely cheap, but a few details can surprise you.

Several Azure services run your logic, and each fits a different style of work.

flowchart TD
  Q1{"Is the work event-driven and small?"} -->|"Yes"| Q2{"Prefer code or visual designer?"}
  Q2 -->|"Code"| FN["Azure Functions"]
  Q2 -->|"Visual and connectors"| LA["Logic Apps"]
  Q1 -->|"No, long-running or containerised services"| Q3{"Need Kubernetes API?"}
  Q3 -->|"No"| CA["Container Apps"]
  Q3 -->|"Yes"| AKS["Kubernetes Service"]
        

These approaches appear again and again in successful serverless systems.

flowchart LR
  U["Client"] --> API["HTTP Function: accept request"]
  API --> Q["Queue"]
  Q --> W["Worker Function: process with limits"]
  W --> DB["Database"]
  W --> PQ["Poison Queue for failures"]
  W --> N["Notification Function"]
        

Azure Functions quietly powers a wide range of everyday systems.

These habits regularly cause duplicate work, outages and surprise bills.

Walk through these steps whenever you plan a new function app.

It means that you do not manage the servers. You provide code, and Azure provisions, scales and maintains the machines that run it. There are still servers underneath, but you do not see or operate them.

A trigger is the event that starts a function, and every function has exactly one. A binding is an optional, declared connection to data, such as reading a record or writing a message, either as input or output.

A cold start is the extra delay when a new instance must start before running your function, usually after a quiet period. You can reduce it by keeping packages small, avoiding heavy start-up work and using plans or settings that keep instances warm, such as always-ready or pre-warmed instances.

For most new serverless workloads, Flex Consumption is a strong default because it scales quickly and supports private networking. Choose Premium for steady, latency-sensitive or long-running work, Dedicated if you already have spare App Service capacity, and check the current documentation for plan status and limits.

They are used for reliable, long-running workflows with many steps, waiting, parallel work and human approvals. The framework saves progress, so that a workflow can continue after failures or restarts.

Most triggers use at-least-once delivery, so duplicates can happen after retries, timeouts or crashes. Make your function idempotent, so that repeating the same message does no harm.

Do not leave it anonymous if it is exposed to the internet. Use platform authentication with an identity provider where possible, use function keys only for simple service-to-service cases, and restrict the network, use managed identities and keep secrets in Key Vault.

Cap the maximum number of instances, limit concurrency per instance and put a queue between the caller and the worker functions, so that work is processed at a controlled pace. Add retries with delays in your code.

Make functions efficient, batch work, avoid loops and unnecessary polling, control telemetry volume with sampling, choose the plan that matches your traffic pattern and set budgets and alerts for unexpected spikes.

Use Functions for code-first, event-driven tasks. Use Logic Apps when a visual workflow with many connectors fits better. Use Container Apps for containerised services, long-running workers or custom runtimes. Many solutions combine them.

Azure Functions runs small pieces of code in response to events, without any servers for you to manage. A function has one trigger and optional bindings, and functions are grouped in function apps that scale together according to a hosting plan. The scale controller adds and removes instances as events arrive, so you pay for use rather than idle time. Durable Functions add reliable long-running workflows, while good design for retries, idempotency, poison messages and downstream limits keeps systems safe. With managed identities, Key Vault, careful networking, pipeline-based deployment, strong monitoring and attention to cost, Azure Functions lets small teams build responsive, scalable systems quickly.