cost optimization
798 articles on cost optimization — what drives the cost, how it is priced, and where the savings actually are.
Fine-tuning cost in the cloud: adapting models for less
Fine-tuning a model costs training compute (GPU-hours) plus data preparation, and serving a fine-tuned model may cost more than a base model. Parameter-efficient methods and right-sized runs cut it. Here is the model.
AI agent infrastructure cost: the price of autonomous loops
AI agents make many LLM calls per task as they reason, use tools, and iterate, so their cost is LLM tokens times the loop length, plus the tools and infrastructure they invoke. Long loops and large context drive the bill. Here is how.
Prompt caching cost savings: paying once for repeated context
Prompt caching lets an LLM API bill repeated input context at a large discount after the first call, so a big shared system prompt or document sent many times costs far less. For repeated-context workloads, savings are substantial. Here is how.
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.
AWS ParallelCluster cost: the HPC cluster is the compute
ParallelCluster is a free tool that provisions HPC clusters; the cost is the underlying EC2 compute, storage, and networking it launches. Right-sizing nodes, using Spot, and scaling to zero when idle drive the bill. Here is the model.
Azure Batch cost: the service is free, the VMs are not
Azure Batch is free to use; you pay for the underlying VM compute in the pools it manages, plus storage and networking. Low-priority (Spot) VMs, right-sized pools, and autoscaling to zero drive the bill. Here is the model.
HPC in the cloud: what high-performance computing really costs
Cloud HPC trades a capital cluster purchase for pay-as-you-go compute, so cost is driven by node type, job runtime, and utilization. Spot, scale-to-zero, and efficient jobs make it economical. Here is how the cost works.
Spot instance cost strategy: big discounts for interruptible work
Spot instances (spare capacity at up to 90% off) can be reclaimed with little notice, so they suit fault-tolerant, interruptible, or checkpointed work. Diversifying pools and handling interruption make them safe. Here is the strategy.
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.
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.
LLM API cost optimization: paying less per token
LLM API cost is tokens: input plus output tokens times the per-token rate, which varies by model tier. Choosing the right model per task, trimming prompts, caching, and capping output cut the bill. Here is how.
RAG infrastructure cost: the pieces behind retrieval-augmented generation
A RAG system's cost spans a vector database, embedding generation, the LLM API for generation, and the retrieval compute. Each scales differently, and the LLM and vector store usually dominate. Here is how to budget it.
AWS Verified Access cost: per app connection per hour plus data
Verified Access bills per application connected per hour plus data processed, giving zero-trust access to internal apps without a VPN. Many connected apps and high traffic drive the bill. Here is the model.
AWS Clean Rooms cost: per compute-unit of collaboration queries
AWS Clean Rooms bills for the compute (CRPU) that runs privacy-preserving queries across partners' data without sharing raw data. Query volume and complexity drive the bill. Here is the model.
Azure Firewall Manager cost: policy management plus the firewalls
Firewall Manager centrally manages Azure Firewall policies and security across many networks; the meaningful cost is the Azure Firewalls themselves plus per-policy charges beyond the first. Here is the model.
Azure DDoS Protection cost: a flat monthly plan plus overage
Azure DDoS Protection (Network or IP Protection) bills a flat monthly fee covering a set of protected resources, with per-resource charges beyond the included count. It protects against volumetric attacks. Here is the model.