DEV Community: aman Singh

Azure Container Apps vs Cloud Run: Which is Right for Your Deployment?

aman Singh — Wed, 07 Oct 2026 14:30:10 +0000

Introduction

As cloud-native applications continue to gain traction, developers are increasingly looking for efficient ways to deploy their services. Among the popular options available today are Azure Container Apps and Google Cloud Run. Both platforms offer serverless container management, but they cater to different needs and use cases. In this article, we will explore the key features, advantages, and limitations of each service to help you determine which is best suited for your deployment strategy.

Overview of Azure Container Apps

Azure Container Apps is a fully managed serverless container service that allows developers to build and deploy applications without worrying about the underlying infrastructure. It is designed for microservices architectures and supports various programming languages and frameworks. Key features include:

Event-driven architecture: Azure Container Apps can automatically scale based on incoming requests or events, making it suitable for applications with variable workloads.
Integrated Dapr support: This enables developers to easily build distributed applications with state management, pub/sub messaging, and service invocation.
Custom domains and SSL: Azure Container Apps allows you to configure custom domains and automatically provision SSL certificates for secure connections.

Overview of Google Cloud Run

Google Cloud Run is another serverless container platform that allows you to deploy and manage applications in a fully managed environment. It is built on top of Knative and provides a seamless experience for deploying containerized applications. Key features include:

Automatic scaling: Cloud Run automatically scales your application up or down based on traffic, ensuring you only pay for what you use.
Support for any language or library: You can run any application that can be packaged in a container, giving you the flexibility to choose your preferred tech stack.
Integration with Google Cloud services: Cloud Run easily integrates with other Google Cloud services, making it a great choice for applications that rely on Google's ecosystem.

Feature Comparison

Feature	Azure Container Apps	Google Cloud Run
Event-driven architecture	Yes	Yes
Automatic scaling	Yes	Yes
Custom domains and SSL	Yes	Yes
Language support	Any supported by Docker	Any supported by Docker
Integration with other services	Azure ecosystem	Google Cloud ecosystem

Cost Considerations

When evaluating the costs associated with Azure Container Apps and Google Cloud Run, it's essential to separate hosting costs from LLM token spending.

Azure Container Apps: Costs are based on the resources consumed by your applications, including CPU and memory usage. You also need to consider potential costs for additional Azure services you may integrate with.
Google Cloud Run: Pricing is based on the resources allocated to your containers, including CPU, memory, and request counts. Google Cloud Run also charges based on the duration your containers are running.

Assumptions: Both platforms offer free tiers, but actual costs will vary based on usage patterns. It’s crucial to analyze your expected traffic and resource requirements to estimate costs accurately.

Honest Limits

While both Azure Container Apps and Google Cloud Run provide excellent solutions for deploying containerized applications, they may not be the best fit for every scenario. For instance, if you are developing a simple frontend application, platforms like Vercel may offer a more streamlined experience. On the other hand, if your application requires complex multi-service interactions, such as those seen in AI applications with RAG stacks, you may need a more robust solution that includes API readiness and vector storage.

Additionally, consider the potential pitfalls of false-green patterns, where health checks may indicate an application is running (e.g., returning a 200 status) while the underlying service is down, leading to poor user experiences.

Conclusion

Choosing the right platform for your deployment needs is crucial for the success of your application. If you're interested in exploring how to optimize your deployment strategy or need assistance with a specific repository, consider analyzing your options with Infriqa Managed. Our team can help you navigate the complexities of deploying applications on Azure Container Apps, Google Cloud Run, or other platforms, ensuring you make the best choice for your project.

For more insights, visit Infriqa's blog.

Why Your AI App Works on Localhost but Fails After Deployment

aman Singh — Tue, 06 Oct 2026 16:14:11 +0000

Why Your AI App Works on Localhost but Fails After Deployment

You ship with confidence. Locally everything works: chat UI loads, the API answers, models respond. After deployment, the platform says healthy — and users get 502 Bad Gateway.

That gap is one of the most common failures we see with FastAPI / RAG / agent apps. Localhost does not prove production readiness. This post breaks down why, what to check, and how to gate deploys on API readiness, not only soft health.

What “it works on localhost” Actually Proves

Local success usually means:

The app boots in your working directory with your .env
Frontend and API share one machine (CORS and proxies “just work”)
If the API crashes, you notice because the terminal shows errors

It does not prove:

The container entrypoint starts the correct module (main:app vs app.main:app)
The reverse proxy targets the real API port
The health probe checks the application, not only nginx in front of it
Cloud networking can reach Postgres, Redis, or your vector store

Four Reasons Production Fails After a “Successful” Deploy

1. Soft Health is Green; the API is Dead

Many stacks put nginx on the public port and uvicorn/Node on an internal port. A probe that only hits nginx /health returns 200 even when the API never started.

Operators see healthy. Users see 502 or an empty shell.

False-green is worse than a red deploy. A red fail stops you immediately. A green deploy with a dead API wastes hours and breaks trust in the pipeline.

2. Wrong App Module or Working Directory

FastAPI apps often live under backend/ as main:app or app.main:app. Locally you run:

cd backend && uvicorn main:app --reload

In a container, a start command that ignores --app-dir / WORKDIR never binds the app the proxy expects. Localhost hid the path problem.

3. Environment and Model Keys Differ

Local .env carries Gemini, Azure OpenAI, DB URLs, and secrets that never make it into the runtime. The UI can load while model calls 404 or time out.

4. Network Boundaries Appear Only in the Cloud

Postgres, Redis, or vector DBs reachable on your laptop may be unreachable from the orchestrator network. Local success does not validate those paths.

What We Saw on Real AI App Deploys

On Infriqa Managed deploys, a repeating pattern showed up:

Surface looked healthy — soft health responded; the container looked up
Users still hit 502 — requests that needed the API never reached a live process
Root cause — wrong uvicorn module resolution + a probe that only checked the proxy
Fix — gate on API readiness after soft health: probe the app and fail when you get connection errors or 502

Soft health ≠ API readiness. Catching that before you call the deploy “done” saves a full day of debugging.

A Practical Checklist Before You Call It Shipped

Use this on every AI/API deploy:

Health that hits the app — not only nginx. Prefer a readiness route that imports your app and touches a cheap dependency when possible.
Confirm the process — exec into the container / check process list: is uvicorn/gunicorn actually running?
Hit the API path yourself — curl the public URL for a known API route, not only /.
Verify env in runtime — required keys present? No silent missing OPENAI_* / DB URL?
Test cloud network paths — DB / Redis / vector store from inside the running task, not from your laptop.
Match start command to repo layout — same module path you use locally, with correct working directory.

Bottom Line

Localhost proves your laptop setup works. Production fails when:

The proxy is healthy but the API is not
Start paths differ from local cd habits
Secrets and networks only exist on your machine

Treat API readiness as a hard gate — not an optional afterthought.

Go Deeper

We documented this pattern with Managed deploy evidence here:

https://infriqa.ai/blogs/localhost-vs-production

Want a structured check of whether your AI app repo is deploy-ready (layout, start path, readiness) before the next ship?

Analyze your repository