How Much Does It Cost to Train and Serve ML Models on the Cloud?
ML infrastructure costs $40-$60/mo at 1,000 users and scales to $9,000-$10,000/mo at 1M users, with inference typically accounting for 60-80% of total spend since endpoints run 24/7 while training happens periodically.
Machine learning workloads are some of the most expensive cloud costs — GPU instances, large storage volumes, and inference endpoints add up fast. AWS SageMaker, Azure ML, and Google Vertex AI each price training and serving differently. Use our calculator to compare ML infrastructure costs across all three providers for your workload.
ML Infrastructure Components
A production ML workflow requires training compute (GPU or TPU instances for model fitting), model storage (versioned artifacts in object storage), inference endpoints (always-on or auto-scaling services for predictions), data storage for training datasets, and experiment tracking to manage runs and hyperparameters. GPU instance pricing varies 3–5x between providers for equivalent hardware, making provider choice critical for ML budgets.
Training vs Inference Costs
Training is burst compute — hours or days of intensive GPU time to fit a model. Inference is steady-state — always-on endpoints serving predictions to your application. For most production teams, inference accounts for 60–80% of total ML infrastructure cost because endpoints run 24/7 while training happens periodically. Spot and preemptible GPU instances cut training costs by 60–90% with the trade-off of potential interruptions, making checkpointing essential.
Cloud ML Cost Comparison
At 1K MAU, ML infrastructure costs $40–$60/month for basic compute and storage. At 10K MAU, expect $900–$1,000/month as Kubernetes becomes the compute layer. At 100K MAU, costs range from $2,700–$3,000/month with managed databases and load balancers. At 1M MAU, expect $9,000–$10,000/month with CDN and larger infrastructure. AWS tends to be cheapest at small scale, while Azure wins at larger scale. GCP sits in between with competitive Kubernetes pricing.
Key Cost Factors
- GPU compute: Training instances for model fitting
- Inference endpoints: Always-on model serving
- Object storage: Datasets and model artifacts
- ML platform: SageMaker/Vertex AI/Azure ML fees
- Experiment tracking: Run logging and metadata
- Data transfer: Distributed training communication
Frequently Asked Questions
How much does ML model hosting cost on AWS vs Azure vs GCP?
At 1K MAU, ML infrastructure costs $40-$60/month for basic compute and storage. At 10K MAU, expect $900-$1,000/month as Kubernetes becomes the compute layer. At 100K MAU, costs run $2,700-$3,000/month with managed databases and load balancers, and at 1M MAU expect $9,000-$10,000/month with CDN and larger infrastructure. AWS tends to be cheapest at small scale, Azure wins at larger scale, and GCP sits in between with competitive Kubernetes pricing.
What is the cheapest cloud provider for machine learning workloads?
GPU instance pricing varies 3-5x between providers for equivalent hardware, so provider choice matters more for ML than most other workloads. Spot and preemptible GPU instances cut training costs by 60-90% with the trade-off of potential interruptions, which makes checkpointing essential regardless of provider. There's no single cheapest option — run your training and inference volumes through the calculator to see the current gap.
What drives the cost of training and serving ML models?
Inference accounts for 60-80% of total ML infrastructure cost for most production teams because endpoints run 24/7 while training happens in bursts. GPU or TPU training compute, model storage for versioned artifacts, and data storage for training datasets round out the core costs, alongside experiment tracking for managing runs and hyperparameters. Data transfer for distributed training communication is a secondary but real cost at scale.
How accurate are these ML cost estimates?
Estimates use published on-demand list prices for AWS, Azure, and GCP compute and storage services in US regions, assuming 730 hours per month, last verified 2026-07-15. They don't reflect spot/preemptible discounts of 60-90% off on-demand GPU pricing, which most production ML teams use for training. Treat these as on-demand baseline figures — see /methodology.