Azure Container Apps Serverless GPUs

The Problem#

GPU workloads are expensive when they sit idle. Many AI services need GPU capacity only during bursts, experiments, or batch windows.

Why It Matters#

Azure Container Apps serverless GPUs give teams a managed container path with per-second billing and scale to zero. That is useful when you want to bring your own model without operating a full Kubernetes GPU platform.

Core Concepts#

The app runs as a container in a consumption workload profile with an attached GPU. Quota is required. Autoscaling controls replica count. Cold start depends on image size, model loading, storage placement, and artifact streaming.

Implementation#

Keep deployment settings explicit:

gpu type: T4 or A100
replicas: min 0 for batch, min 1 for interactive
image: small runtime layer
model storage: mounted close to the app
autoscale metric: queue depth or request latency

Use an async API for long-running inference so clients can poll job status.

Real Project Scenario#

A media platform needs bursty thumbnail classification after uploads. Running GPU nodes all day wastes money, but CPU inference is too slow. A serverless GPU container can process spikes after uploads and scale down when the queue is empty.

Production Setup#

Put inference requests behind a queue. Store model artifacts in controlled storage, load them close to the runtime, and track queue age, cold start, model load time, GPU memory, and job failure rate. Those signals show whether the serverless GPU shape is actually cheaper than a small always-on pool.

Common Mistakes#

• Requesting quota late in the project.
• Assuming every region has the same GPU options.
• Packing model weights into slow-moving image layers.
• Scaling on HTTP request count when GPU queue time is the real bottleneck.

Production Considerations#

Validate your container against upcoming driver and CUDA runtime changes. Keep a rollback image and run a small scheduled probe to catch cold-start regressions.

Security#

Use managed identity for storage access and avoid embedding credentials in model server configs or images.

Performance#

Artifact streaming and storage mounts can reduce startup time. Benchmark with real model files, not tiny test containers.

Summary#

Azure Container Apps serverless GPUs are a practical middle ground between managed model APIs and a dedicated GPU cluster, especially for bursty custom inference.