ACI vs ACA vs AKS

Introduction

Containerization has become the de facto approach for deploying modern applications. Azure offers three core services to run containers, each serving different purposes based on complexity, scalability, and operational overhead:

• Azure Container Instances (ACI)
• Azure Container Apps (ACA)
• Azure Kubernetes Service (AKS)

This blog explores the differences, use cases, pros, and cons of each to help you choose the right platform for your application needs.

Azure Container Instances (ACI)

• Azure Container Instances provide the simplest and fastest way to run containers in Azure without managing servers or clusters.

• You can launch containers in seconds, making ACI ideal for burst workloads and simple task execution.

• ✅ Use Cases
  • Lightweight background jobs
  • Data processing pipelines
  • Prototyping container workloads
  • Event-driven tasks (e.g., triggered by Azure Logic Apps or Functions)
• Pros
  • No orchestration overhead
  • Pay-per-second billing
  • Simple deployment (az container create)
  • Fast startup (~10 seconds)
  • Supports Linux and Windows containers
• Cons
  • No built-in scaling or orchestration
  • No support for volumes beyond Azure Files
  • No service discovery
  • Limited networking and monitoring capabilities

2. Azure Container Apps (ACA)

• Azure Container Apps is a serverless container service for building and deploying modern microservices and APIs.

• It’s built on Kubernetes, KEDA, Envoy, and Dapr, abstracting the complexities while offering powerful features like autoscaling, traffic splitting, and service-to-service communication.

• ✅ Use Cases
  • Microservices
  • Background jobs
  • Event-driven processing
  • APIs and web apps
  • Applications with dynamic scaling needs
• Pros
  • Fully managed and serverless
  • Built-in autoscaling (KEDA)
  • Support for HTTP, event-driven apps, long-running jobs
  • Dapr integration for service-to-service communication
  • Zero to N scaling (scale to zero on idle)
  • Revision-based deployments with traffic splitting
• Cons
  • Abstracted control – you don’t manage Kubernetes directly
  • Limited customization compared to AKS
  • Not ideal for apps needing fine-grained orchestration

3. Azure Kubernetes Service (AKS)

• Azure Kubernetes Service is a fully managed Kubernetes platform offering deep control over how you orchestrate containers.

• It is suited for complex, enterprise-grade workloads that need full customization, custom networking, and integration with other Azure services.

• ✅ Use Cases
  • Enterprise microservices architecture
  • Stateful apps needing persistent volumes
  • Custom networking, security, and monitoring
  • Workloads requiring GPU, node pools, taints/tolerations
  • Applications using Helm, Kustomize, or GitOps tools (e.g., ArgoCD, Flux)
• Pros
  • Full Kubernetes API access
  • Highly customizable and extensible
  • Integration with Azure DevOps, GitHub Actions, Helm
  • Support for GPU and Windows workloads
  • Strong ecosystem of tools and community support
• Cons
  • Steep learning curve
  • Requires cluster management (upgrades, scaling, patching)
  • Higher operational overhead than ACI/ACA
  • May be overkill for small or short-lived workloads

Comparison Table

Category	Azure Container Instances (ACI)	Azure Container Apps (ACA)	Azure Kubernetes Service (AKS)
Abstraction Level	High (serverless, no orchestration)	Medium (managed K8s with Dapr support)	Low (full Kubernetes, self-managed)
Definition	Serverless containers with no need to manage infrastructure	Managed container app platform built on Kubernetes and Envoy	Fully managed Kubernetes service
Ideal Use Case	Short-lived tasks, jobs, burst workloads	Microservices, event-driven apps, background workers	Complex, large-scale distributed workloads
Startup Time(Infra)	Very fast (~10s)	Fast (~30s)	Slower (~1–2 mins)
Scaling	Manual	Automatic (via KEDA, scale to zero)	Manual or auto (HPA, Cluster Autoscaler)
Networking	Basic IP and DNS, optional VNet	Built-in ingress, VNet integration	Full Kubernetes networking (CNI, service mesh support)
Persistent Storage	Azure Files only	Azure Files, Azure Blob	Azure Disks, Azure Files, CSI drivers
CI/CD Integration	Basic pipelines, manual deployment	GitHub Actions, Azure DevOps, integrated revision management	Full GitOps (Flux, ArgoCD), custom pipelines
Platform Type	Proprietary Azure-managed	Based on open-source Kubernetes, Dapr, Envoy	CNCF-compliant Kubernetes
Billing Model	Pay-per-second for CPU & memory usage	Based on app usage (vCPU, memory), similar to serverless	Pay for VM nodes, storage, and networking
Cost Factors	Only billed while running, no VM or storage unless explicitly mounted	Serverless billing; supports scale-to-zero	Ongoing VM costs, storage, networking
Scalability	Manual scaling only	Auto-scaling with KEDA	Manual or automatic with Cluster Autoscaler, HPA
Management Overhead	Very low, no infra to manage	Minimal; platform handles upgrades, scaling, etc.	Higher; manage clusters, node pools, upgrades, monitoring
Advanced Features	Lightweight, no orchestrator, limited features	Dapr for service discovery, event binding, observability	Full K8s capabilities: Helm, RBAC, service mesh, secrets, etc.
Kubernetes Usage	Not Kubernetes	Underlying platform is Kubernetes (abstracted)	Direct use of native Kubernetes
Cost Example	Cheapest for short tasks; no idle cost	Economical for apps with bursty or irregular traffic	Suitable for always-on apps; costs scale with infrastructure

When to Use What?

Scenario	Use This Service
Just want to run a container temporarily or on demand	ACI
Need autoscaling, service discovery, traffic management	ACA
Require full control, custom orchestration, or Helm	AKS
New to containers, want minimal ops overhead	ACA or ACI
Running multi-tenant apps with advanced networking	AKS

Final Thoughts

• Azure offers containers across a spectrum of control vs. convenience:
• Start with ACI for lightweight, one-off jobs.
• Choose ACA if you want a managed serverless platform for microservices with built-in autoscaling.
• Move to AKS if you need the power and flexibility of Kubernetes, especially for production-scale enterprise workloads.
• With these tools at your disposal, you’re well equipped to choose the right container platform based on your workload and team expertise.