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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Spot Instance Pricing Guide: How Much You Save and What You Risk
Spot instances can slash GPU costs, but they can be reclaimed anytime. Learn how spot pricing works, the real savings, and how to use it safely.
Block vs Object Storage Pricing in the Cloud: A Practical Breakdown
Block or object storage? Compare how each is priced, the performance tradeoffs, and which fits training data, checkpoints, and model serving.
Cloud Egress Fees Explained: Why Moving Data Out Costs So Much
Why does moving data out of the cloud cost so much? Learn how egress fees work, why ingress is usually free, and practical ways to cut transfer costs.
Hidden Costs in GPU Cloud Bills: Egress, Storage, and IP Charges
Your GPU bill is more than the hourly rate. Learn the hidden line items, egress, storage, IPs, and idle resources, and how to keep them under control.
How GPU Hourly Pricing Works: Reading the Fine Print
GPU hourly rates hide a lot. Learn what the per-hour price does and does not include, how billing increments work, and how to compare offers fairly.
GPU Cloud Availability by Region: Where H100s Are Actually In Stock
H100 availability varies wildly by region. Learn why GPU stock is uneven, how to find capacity, and how to plan around scarcity without overpaying.
Bare Metal vs Virtualized GPU Cloud: Performance and Price Tradeoffs
Bare metal or virtualized GPU cloud? Compare performance overhead, isolation, flexibility, and price so you can pick the right one for your workload.
Best GPU Cloud for Stable Diffusion and Image Generation
How to choose GPU cloud for Stable Diffusion: which cards fit, how VRAM and batch size drive cost, and a workflow to find the cheapest image throughput.
GH200 Grace Hopper in the Cloud: Superchip Pricing and Use Cases
What the GH200 Grace Hopper superchip is, how its CPU plus GPU design changes pricing, and the workloads where renting one actually pays off.
GPU Cloud Free Tiers and Credits: How to Test GPUs for Free
A practical guide to GPU cloud free tiers, trial credits, and startup programs so you can benchmark H100s and A100s without paying upfront.
InfiniBand vs Ethernet in GPU Clouds: Why Interconnect Matters
At scale, the network between GPUs can matter more than the GPUs. Here is how InfiniBand and modern Ethernet compare for distributed training.
GPU Cloud Cold Start Times Compared: Provisioning Speed Benchmarks
Provisioning speed is a hidden cost in GPU cloud. Here is what drives cold start times and how to benchmark them across providers.
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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.
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.
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.
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.
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.