The Hidden Costs of Provisioned SageMaker Endpoints

The Sticker Shock

When AWS announced Provisioned Throughput for SageMaker, it sounded perfect: guaranteed capacity, predictable latency, and a simple pricing model. What they didn't mention was all the ways costs can spiral.

After deploying a Hebrew voice AI model last year, I learned these lessons the hard way.

Hidden Cost #1: Idle Billing

Provisioned endpoints bill 24/7, even when no inference requests are running. If your traffic is bursty (e.g., business hours only), you're paying for 16+ hours of idle compute per day.

Solution: Use AWS Application Auto Scaling with scheduled actions to scale to zero during off-hours. Yes, you'll have cold starts, but 10-second startup beats paying for nothing.

Hidden Cost #2: Multi-Model vs. Single-Model Endpoints

If you're serving multiple models, you might think Multi-Model Endpoints (MME) save money. The gotcha: model loading time. If your model is 5GB, the first request after a "cold" model incurs 30-60 seconds of latency while the model loads from S3.

# The fix: Pre-warm your models
import boto3
client = boto3.client("sagemaker-runtime")
client.invoke_endpoint(
    EndpointName="my-mme-endpoint",
    TargetModel="model-v1.tar.gz",
    Body=b"warmup"
)

Hidden Cost #3: Data Transfer

Audio and video inference can generate massive payloads. AWS charges $0.09/GB for data transfer out. For a voice AI processing 100,000 audio files per month, that's potentially$500+/month just in data transfer—on top of compute.

Solution: Deploy endpoints in the same region as your application. Better yet, use VPC endpoints to keep traffic internal.

The Cost Optimization Checklist

• ✅ Use Spot Instances for dev/test (70% savings)
• ✅ Implement auto-scaling with scale-to-zero
• ✅ Use model caching for MME workloads
• ✅ Monitor with CloudWatch Billing Alarms
• ✅ Consider Inference Recommender for right-sizing

For more on SageMaker BYOC deployment, see my complete AI Engineer Roadmap 2026.