AI 및 데이터 집약적 시스템을 위한 클라우드 비용 최적화: 확장하면서 절감

Source: Dev.to

Modern AI systems, LLM‑powered applications, and data‑intensive platforms generate enormous value — but they also generate enormous cloud bills. As organizations scale their machine learning pipelines, vector databases, real‑time analytics, and GPU‑heavy inference workloads, cloud costs can quickly spiral out of control. The result is familiar: impressive AI results paired with a CFO asking why the monthly cloud invoice suddenly doubled.

This is where cloud cost optimization becomes essential. Companies that strategically design, architect, and operate AI workloads in the cloud can reduce costs by 30–70 % without sacrificing performance. Effective cloud optimization isn’t just about cutting expenses — it’s about enabling sustainable scaling, predictable operations, and better resource management across the entire AI lifecycle.

In this article we break down the causes of high AI cloud spend, the most effective cloud cost‑optimization strategies, and actionable approaches to achieving meaningful cloud‑infrastructure cost optimization while still supporting rapid AI growth.

Why AI & Data Workloads Become Expensive

AI workloads are fundamentally different from traditional applications. They require:

• GPU‑intensive compute for training and inference
• High‑performance storage for large datasets
• Massive data movement across networks
• Always‑on services for real‑time applications
• Distributed infrastructure for scalability

Because of these factors, poor cloud planning can lead to unnecessary overspending. The biggest cost drivers include:

GPU Overprovisioning

Data teams often spin up the largest GPU instances available (e.g., A100 or H100) even when workloads don’t require that power.

Idle Compute Resources

Training jobs, MLOps pipelines, and inference services often run 24/7 — even when not in use.

Inefficient Storage

Storing large datasets in high‑cost storage tiers or duplicating data across environments dramatically increases bills.

Lack of Autoscaling

Without autoscaling policies, systems remain over‑allocated during low‑traffic periods.

Poor Observability & Cost Governance

Teams don’t have enough visibility over cost centers, resulting in runaway cloud bills.

Key Cloud Cost Optimization Strategies for AI Teams

To ensure sustainable scaling, organizations must adopt a combination of engineering practices, architectural choices, and ongoing operational monitoring.

Choose the Right Hardware for the Job

AI workloads often rely heavily on GPUs — but the “biggest GPU available” is not always the optimal choice.

• Use smaller GPUs (e.g., T4, L4) for inference instead of A100/H100.
• Utilize spot GPU instances for training jobs with checkpoints.
• Consider ARM‑based processors (e.g., AWS Graviton) for preprocessing and ETL tasks.
• Mix GPU and CPU‑based inference where latency allows.

Right‑sizing compute to workload requirements can achieve 30–50 % savings immediately.

Implement Autoscaling and Right‑Sizing Policies

AI systems frequently experience unpredictable traffic spikes. Autoscaling ensures that compute resources expand during peak usage and contract during low‑demand periods.

• Use Horizontal Pod Autoscaler (HPA) on Kubernetes.
• Set up scale‑to‑zero for non‑essential services.
• Leverage serverless options for vector search, embeddings, or scheduled jobs.
• Continuously track workloads with usage‑based alerts to recommend right‑sizing.

Autoscaling alone can cut 20–40 % of unnecessary spend.

Optimize Cloud Storage for Data Pipelines

Storing AI datasets, embeddings, checkpointed training models, and log files can quickly get out of control.

• Move historical datasets to cheaper storage tiers (e.g., S3 Glacier, Azure Archive).
• Use columnar formats like Parquet to reduce storage size.
• Deduplicate datasets with data‑versioning tools like DVC or LakeFS.
• Archive ML logs and checkpoints automatically after validations.

A well‑designed data lifecycle plan can reduce storage costs by up to 80 %.

Use Efficient Vector Databases and Search Architectures

Vector search systems (Pinecone, Weaviate, Qdrant, Milvus) are essential for RAG, LLM retrieval, and semantic search, but they can be cost‑heavy.

• Use hybrid indexing to reduce vector storage.
• Offload cold embeddings to object storage.
• Employ sharding and partial scale‑out instead of overprovisioning large clusters.
• Consider open‑source solutions hosted on your own Kubernetes cluster.

Choosing the right database topology can reduce costs by 30–60 %.

Compress, Quantize, and Optimize Models

Model compression dramatically reduces inference costs by allowing smaller or cheaper compute instances to serve requests.

• Quantization (FP16, INT8, INT4)
• Pruning and distillation
• Token‑level caching for LLMs
• Serving with optimized runtimes like ONNX Runtime or TensorRT

Model optimization can cut inference costs in half with minimal accuracy loss.

Use Spot Instances for Training

Training LLMs, CV models, and deep neural networks is expensive, but spot GPU instances can slash cost if jobs are checkpointed.

• AWS EC2 Spot
• GCP Preemptible Instances
• Azure Spot VMs

Spot training can reduce costs by 70–90 %, especially for long‑running batch tasks.

Improve Observability and Cost Governance

Without proper monitoring, cost leaks remain invisible.

• AWS Cost Explorer / Azure Cost Management
• Kubecost for Kubernetes
• DataDog or Grafana for resource usage
• MLflow or Weights & Biases to track training costs

For full cloud cost optimization, every team — AI, engineering, product — must see and own their usage patterns.

Adopt a Zero‑Waste Cloud Philosophy

Advanced methods ensure minimal waste across the infrastructure:

• Delete unused snapshots, volumes, clusters, and load balancers.
• Shut down dev environments at night/weekends.
• Separate dev/stage/prod with strict quotas.
• Automate resource cleanup with cron jobs or Lambdas.

Zero‑waste practices can save up to 20 % monthly with no additional engineering effort.

Optimization Strategies for AI Training vs. Inference

AI workloads fall into two categories — training and inference — and both require different optimization tactics.

Training Optimization

Training is GPU‑heavy, long‑running, and typically done in batches.

• Use spot GPUs.
• Enable gradient checkpointing.
• Select smaller batch sizes.
• Choose cheaper regions.
• Perform distributed training when needed.
• Use autoscaling clusters like SageMaker or Vertex AI.

Inference Optimization

Inference must be fast, scalable, and cost‑efficient.

• Use small or quantized models.
• Deploy on smaller GPUs (T4/L4) or CPU for light tasks.
• Use token streaming and caching.
• Autoscale aggressively.
• Use serverless inference (AWS Lambda + EFS, Vertex AI Serverless).

Building a Cloud Cost Optimization Culture

Technology alone can’t solve the cost challenge — teams must adopt the right mindset.

• Engineering estimates cloud impact before development.
• Architecture teams review infra decisions.
• Product managers understand budget implications.
• Finance collaborates with tech leaders.
• Automated alerts trigger when cost thresholds are reached.

Companies that embed this culture see long‑term success with cloud‑infrastructure cost optimization.

Scale AI Smartly, Not Expensively

AI‑driven systems and data‑intensive workloads are inherently resource‑hungry, but they don’t have to be financially unsustainable. By combining engineering best practices, architectural decisions, and automation, organizations can achieve sustainable scaling while keeping cloud spend under control.