The Real Cost of Running AI in Production on AWS
The GPU Bill Shock
Teams building AI products on AWS consistently underestimate costs by 3-5x. A single ml.g5.xlarge SageMaker endpoint (1 GPU, good for small to medium models) costs about $1,400/month running 24/7. Need two for redundancy? That's $2,800. Add a training instance that runs 20 hours a week and you're at $3,500 before you've paid for data storage, transfer, or any other infrastructure. We've seen seed-stage startups burn through $15K/month on AI infrastructure that could be optimized to $4K without sacrificing performance.
Where the Money Actually Goes
On a typical production AI workload, compute is 50-60% of the bill (GPU inference endpoints, training jobs, and Lambda/Fargate for orchestration). Storage is 15-20% (S3 for datasets, EBS for training volumes, OpenSearch or PostgreSQL with pgvector for vector stores). Data transfer is 10-15% (often surprisingly high due to cross-region or cross-AZ traffic between inference endpoints and application servers). The remaining 10-15% is monitoring, logging, and supporting services. Most teams focus on optimizing compute and ignore data transfer, which is a mistake.
Inference Cost Optimization
The biggest savings come from right-sizing inference. We see teams running ml.g5.2xlarge instances ($2,700/month) for models that run fine on ml.g5.xlarge ($1,400/month). Auto-scaling inference endpoints based on traffic patterns instead of keeping peak capacity 24/7 typically cuts inference costs by 30-40%. For SageMaker, multi-model endpoints let you serve multiple models from a single GPU instance, which works well when individual models have low utilization. For Bedrock, the optimization is about prompt engineering: shorter prompts and completions mean lower per-token costs.
Training Cost Traps
Training jobs are bursty by nature, which makes them ideal for spot instances. SageMaker managed spot training can save up to 70% on training costs with automatic checkpointing so you don't lose progress if a spot instance is reclaimed. The other common trap is keeping training instances running between jobs. We've seen teams leave ml.p3.2xlarge instances ($3.06/hour) running as notebooks for days between experiments. That's $73/day for an idle GPU. Automated lifecycle policies that shut down idle instances save thousands per month.
A Framework for AI Cost Planning
Before building, estimate costs across three scenarios: development (low traffic, experiments), launch (moderate traffic, initial users), and scale (high traffic, full production). For each scenario, map out: number of inference endpoints and instance types, expected request volume and average latency, training frequency and dataset size, storage requirements for models, datasets, and vectors. We provide clients with a cost model spreadsheet that projects monthly costs across these scenarios. This avoids the surprise of hitting scale and finding out your AI feature costs $20K/month instead of the $5K you budgeted.