Azure FinOps Essentials

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Hi there, and welcome to this week's edition of Azure FinOps Essentials! 🎉

This week’s topic comes straight from the trenches. I was recently faced with a simple request: accept mutation events from an external company whenever their data changed. A classic integration scenario, right?

The obvious answer would have been to spin up a web application, expose an HTTP endpoint, and process the incoming payloads in real time. But I didn’t want to manage uptime. I didn’t want to scale for bursts. And I definitely didn’t want to pay for an idle service sitting around waiting for requests.

So I took a different path. And that decision turned into a perfect example of how event-driven architecture isn’t just elegant, but cost efficient too.

Cheers,
Michiel

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When Always-On Doesn’t Make Sense

In many cloud projects, especially integrations between systems, the default approach is to build something that’s always available. A classic pattern: spin up a web app, expose an HTTP endpoint, and wire it into your deployment pipeline.

It works, but it comes at a cost. Literally.

Services that sit idle for most of the day still generate compute and hosting costs. They require uptime guarantees. Monitoring. Maintenance. And if they happen to be offline during a deployment or a failure, they cause backpressure on the sender — or worse, dropped data.

I recently ran into exactly this scenario.

Another company needed to notify us whenever data was mutated on their end. The obvious route would have been to create a web service that listens for their payloads and processes them directly. But that came with all the usual problems:

• What if the service is redeploying?

• What if they send hundreds of requests at once?

• What if we need to throttle or delay processing?

This wasn’t a high-throughput integration. The events were important, but not urgent. And we didn’t need instant processing — just eventual consistency. Yet the default solutions felt like overkill.

I needed a design that wouldn’t tie cost to uptime. That wouldn’t penalize me for being temporarily unavailable. And that wouldn’t require a fleet of always-on services.

Idle by Design: Turning the Pattern Inside Out

The integration I mentioned didn’t require low-latency processing. It needed reliability, simplicity, and resilience to bursts or downtime.

Instead of building a web app and keeping it warm 24/7, I inverted the model.

Here’s what I did:

• API Management acted as the front door. It exposed a stable HTTP endpoint to the other party.

• That endpoint didn’t route to a backend service. Instead, it pushed the incoming payload straight into a queue.

• From there, a queue-triggered Azure Function picked up the message and processed it — on its own terms, whenever resources were available.

The result?

• Zero idle compute cost. The Azure Function only runs when needed.

• No coupling between sender and receiver. If I’m redeploying or down, nothing breaks — messages accumulate safely.

• No need to overprovision. A sudden spike in requests doesn’t overwhelm anything. Azure Functions scale with demand (within defined bounds).

And since API Management was already in place and shared across services, there was no additional hosting cost. The queue is nearly free. The function execution is charged by invocation.

This is what Idle by Design means to me: intentionally building systems that don’t need to run unless there’s something to do.

It’s an architectural decision — but it’s also a FinOps one.

Applying Idle-by-Design in Azure: Patterns That Scale Down to Zero

When we think about modern systems, we often focus on scalability under load. But FinOps-minded teams should also ask: “How does my system behave when there’s no traffic?”

That’s the core of Idle by Design — building systems that cost nothing (or close to it) when idle, without compromising on responsiveness or reliability.

Azure offers several services and patterns to help you achieve that:

1. Event-Driven Ingestion with Minimal Overhead

Instead of building and hosting always-on APIs, use Azure API Management to receive data and route it immediately to:

• Azure Service Bus – reliable queuing with dead-lettering and retries.

• Event Grid – for high-fanout scenarios or integrations across services.

• Storage Queues – for simple, ultra-low-cost message delivery.

These services absorb bursts and decouple systems, eliminating the need for persistent frontend infrastructure.

2. On-Demand Processing with Azure Functions

Once your data is queued or published, process it with Azure Functions. Key benefits:

• Triggered only when work arrives.

• Scales automatically, down to zero.

• Integrates directly with queues, blobs, HTTP, Event Grid, and more.

Combine with Durable Functions for orchestration or long-running workflows without background compute.

3. Zero-Idle Hosting

If you do need to host an API or background worker, choose services that scale to zero:

• Azure Container Apps: Handles HTTP or background jobs, scales to zero by default.

• Azure App Service with Pre-warmed Instances: Optional if you need predictable startup, but idle cost is higher.

• Azure Kubernetes Service (AKS) with KEDA: For advanced use cases, scale pods based on queue length, HTTP traffic, or even custom metrics.

4. Storage That Sleeps

Data at rest shouldn’t break the bank:

• Use Azure Blob Storage with lifecycle policies to transition unused data to Cool or Archive tiers.

• Choose serverless Cosmos DB for event-based, rarely accessed documents.

By combining serverless, event-driven, and elastic Azure services, you create systems that:

• Handle scale without overprovisioning.

• Pause compute when not needed.

• Minimize waste while maximizing responsiveness.

This pattern isn’t limited to one niche case. It’s an architectural mindset that can be applied across APIs, data ingestion, background jobs, and even databases.

Cost-Efficient by Design, Not by Accident

FinOps isn’t just about slashing cloud bills. It’s about making conscious, data-informed decisions that align engineering with business value.

You may decide that cost isn’t your top priority — maybe performance, resilience, or speed-to-market matter more. That’s valid. But the key is to know the trade-offs.

What’s powerful about Idle-by-Design patterns is that they often deliver benefits beyond cost:

• Greater reliability through decoupled services and retry mechanisms.

• Improved scalability by reacting to demand instead of provisioning for peak.

• Smoother operations thanks to minimal infrastructure to maintain.

In my own project, I didn’t choose this pattern just to save a few euros. I wanted to avoid running a full web app, reduce operational complexity, and stay responsive even when my system wasn’t available. The fact that it’s nearly free to run when idle? A welcome bonus.

So whether you’re driven by budget constraints or engineering elegance, embracing event-driven, idle-aware architectures can move you closer to both.

And that’s the essence of FinOps: maximizing business value, not minimizing spend.

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Want more FinOps news, then have a look at FinOps Weekly by Victor Garcia