ml
13 articles on ml — what drives the cost, how it is priced, and where the savings actually are.
ML feature store cost: online and offline storage plus serving
A feature store's cost is the offline store (bulk feature data for training), the online store (low-latency serving for inference), and the compute that materializes features. The online store and materialization drive the bill. Here is the model.
Data labeling cost: budgeting the least glamorous ML expense
Labeling training data, human annotation, managed labeling services, or model-assisted labeling, is often a large and underestimated ML cost. Active learning, pre-labeling, and scoping the labeled set cut it. Here is how.
Model registry cost: versioned models are mostly storage
A model registry stores versioned model artifacts and metadata; its cost is mostly the storage of those artifacts, which can be large for many versions of big models. Lifecycle policies and pruning old versions cut it. Here is the model.
SageMaker cost optimization: training, endpoints, and notebooks
SageMaker cost spans training jobs (GPU-hours), inference endpoints (always-on serving), notebooks and Studio (idle compute), and processing. Endpoints and idle notebooks are the biggest waste. Here is how to cut it.
Vertex AI cost optimization: training, prediction, and pipelines
Vertex AI cost spans custom training (compute-hours), prediction endpoints (always-on nodes), pipelines, and notebooks. Idle endpoints and workbench instances are the biggest waste. Here is how to cut it.
Azure Machine Learning cost optimization: compute is the bill
Azure ML cost is the compute it orchestrates, training clusters, inference endpoints, and compute instances (notebooks), plus storage. Idle compute instances and always-on endpoints are the biggest waste. Here is how to cut it.
MLOps pipeline cost: budgeting the whole ML lifecycle
MLOps cost spans data prep, training, serving, feature stores, registries, monitoring, and orchestration, each a piece of the bill. Serving and training compute usually dominate, but the hidden pieces add up. Here is how to budget the whole lifecycle.
GPU instance cost compared: choosing the right accelerator
GPU instances vary enormously in cost, from older inference GPUs to top training accelerators costing many dollars per hour. Matching GPU class to the workload, and using Spot, drives the bill. This compares the tradeoffs.
ML training cost optimization: getting models trained for less
Training cost is GPU-hours: accelerator class times count times training time. Spot with checkpointing, right-sized GPUs, efficient data pipelines, and stopping idle instances cut it sharply. Here is how.
ML inference cost optimization: serving models efficiently
Inference cost is serving capacity times uptime: the accelerators or CPUs kept ready to respond. Autoscaling, right-sized hardware, batching, and scaling to zero when idle cut it. Here is how.
GPU vs CPU for inference: which is cheaper to serve on
GPUs accelerate inference but cost far more per hour; CPUs are cheaper but slower per request. For small models, low throughput, or latency-tolerant workloads, CPU is often cheaper per inference. Here is how to decide.
Model serving cost: managed endpoints vs self-hosted
Serving a model can use a managed endpoint (SageMaker, Vertex, Azure ML) that bundles ops at a premium, or self-hosted serving on your own compute that is cheaper per hour but adds operational burden. Here is the tradeoff.
Batch vs real-time inference: the cost of immediacy
Real-time inference keeps serving capacity always ready to respond instantly; batch inference processes predictions in bulk on transient compute. When predictions can wait, batch is far cheaper. Here is the tradeoff.