DeployCue Cloud Cost Blog
Practical guides for developers and ML teams: how to choose a GPU host, cutting egress costs, LLM API pricing, spot vs on-demand, storage tiers, Kubernetes economics, and cloud billing explained.
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FinOps for AI Workloads: Building a GPU Cost Discipline
AI workloads break traditional cloud cost models. Learn a FinOps framework for GPU spend: visibility, optimization, and governance that scales.
Storage Lifecycle Policies: Automating Cheap Cold Storage Transitions
Old datasets and checkpoints pile up on costly hot storage. Lifecycle policies move data to cheaper tiers automatically as it ages, no manual cleanup.
Auto-Shutdown Scripts for Idle GPU Instances: Save Money While You Sleep
Forgotten GPU instances bill around the clock. Learn simple auto-shutdown patterns that power down idle GPUs on schedule and on inactivity, automatically.
GPU Cost Allocation: Tagging and Chargeback for ML Teams
You cannot optimize what you cannot attribute. Learn to tag GPU resources and run chargeback so every team owns its share of the cloud bill.
Blending Reserved and Spot Capacity for Maximum GPU Savings
Neither all-reserved nor all-spot is optimal. Learn to blend committed and interruptible GPU capacity to match your workload's true demand curve.
Cutting Cloud Egress Costs: CDNs, Peering, and Architecture Fixes
Egress charges can rival compute. Learn how CDNs, peering, region co-location, and smarter architecture cut the cost of moving data out of the cloud.
Rightsizing GPU Instances: Matching Hardware to Real Workload Needs
Defaulting to the biggest GPU wastes money. Learn to profile workloads and match memory, compute, and host resources to what the job actually needs.
GPU Utilization Monitoring: Stop Paying for Idle GPUs
Idle GPUs are the most expensive thing in your cloud bill. Learn which utilization metrics matter and how to monitor them to stop paying for nothing.
Using Spot Instances for Training: Checkpointing Against Preemption
Spot GPUs can slash training costs if your job survives preemption. Learn to checkpoint, resume, and design jobs that thrive on interruptible capacity.
How to Reduce GPU Cloud Costs: 15 Tactics That Actually Work
Fifteen practical tactics to cut GPU cloud spend, from spot capacity and rightsizing to egress fixes, scheduling, and committed-use discounts.
RAG Pipeline Costs: Where Retrieval-Augmented Generation Spends Money
RAG spends money in more places than the final answer. Here is a full breakdown of where retrieval-augmented generation costs add up and how to trim them.
On-Device vs Cloud Inference: When to Skip the GPU Cloud Entirely
Not every model needs a cloud GPU. Here is when running inference on the device wins on cost, latency, and privacy, and when the cloud is unavoidable.
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Inference Autoscaling: Handling Traffic Spikes Without Overpaying
Autoscaling inference well means absorbing spikes without paying for idle GPUs the rest of the time. Here is how to tune it.
Deploying Mixtral and MoE Models: Cost Quirks of Sparse Experts
Mixture-of-experts models like Mixtral are cheap to run but expensive to hold in memory. That quirk drives every cost decision.
Set Up a Fault-Tolerant Spot Training Job From Scratch
Build a training job that survives spot interruptions through checkpointing, automatic resume, and a sensible fallback.
AWS Trainium vs NVIDIA GPUs: Custom Silicon for Training Compared
AWS Trainium promises lower training costs than NVIDIA GPUs, but the tradeoff is ecosystem maturity. Here is how the two compare for real workloads.
Setting Up GPU Cloud Budget Alerts Before Bills Explode
A beginner-friendly guide to GPU cloud budget alerts: thresholds, anomaly detection, and hard stops that catch runaway spend before it hurts.
Continuous Batching: The Trick Behind High-Throughput LLM Serving
Continuous batching keeps the GPU busy by swapping finished requests for new ones mid-flight. It is why modern serving is so efficient.
GPU Cloud Billing Units: Per-Second, Per-Minute, and Per-Hour Compared
Billing granularity quietly shapes your GPU bill. Compare per-second, per-minute, and per-hour pricing and learn which fits your workload.
Cost Per Million Tokens Compared Across Top Inference APIs
How to compare cost per million tokens across inference APIs the right way, accounting for input and output splits, model tiers, and hidden fees.
Set Up GPU Monitoring With Prometheus and Grafana
Build a GPU monitoring dashboard with Prometheus and Grafana so you can spot idle GPUs, thermal throttling, and wasted spend at a glance.
GPU Cloud Marketplaces: How Spot GPU Bidding Actually Works
How GPU cloud marketplaces and spot bidding work: where the cheap capacity comes from, the interruption risk, and how to use it safely.
GPU Sizing for LLM Serving: Matching VRAM to Model Size
Pick a GPU too small and the model will not load; too big and you overpay. Here is how to size VRAM to your model.
Batch Inference: How Async Processing Slashes Token Costs
If your workload can wait minutes or hours, batch inference can cut token costs sharply. Here is when and how to use it.