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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Set Up Multi-GPU Distributed Training With PyTorch DDP
A hands-on tutorial on scaling training across multiple GPUs with PyTorch DistributedDataParallel, covering setup, launch, and common failure modes.
Serve a Quantized LLM in the Cloud With Ollama
A practical tutorial on running a quantized LLM on a cloud GPU with Ollama, from instance choice to a secured, production-ready endpoint.
Build a GPU Cost Dashboard From Billing Exports
A FinOps tutorial on turning raw billing exports into a GPU cost dashboard that reveals waste, drivers, and trends per team and workload.
How to Buy and Apply a Reserved GPU Instance Correctly
A clear tutorial on buying reserved GPU capacity, matching commitments to real usage, and confirming the discount actually applies to your bill.
Migrate a GPU Workload Between Two Clouds Without Downtime
An advanced playbook for moving a live GPU workload from one cloud to another with zero downtime using traffic shifting and parallel running.
Quantize a Model to INT8 for Cheaper Deployment, Step by Step
A hands-on walkthrough to quantize an LLM to INT8, cut GPU memory and cost, and keep accuracy acceptable for production inference.
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.
Autoscale LLM Inference on Kubernetes With KEDA
Autoscale LLM inference on Kubernetes with KEDA so GPU pods grow with real demand signals like queue depth, not just raw CPU usage.
Cut Egress Costs by Serving From Zero-Egress Object Storage
Migrate to zero-egress object storage to stop paying per gigabyte every time you serve files, and learn when the move actually pays off.
Benchmark H100 vs A100 Yourself: A Reproducible Test Guide
Run your own reproducible H100 vs A100 benchmark so you compare these GPUs on your real workload, not on someone else's marketing numbers.
Deploy a Serverless Inference Endpoint on Modal
Deploy an LLM inference endpoint on Modal that scales to zero, so you pay only for the GPU seconds your requests actually use.
Set Up Cloud Cost Budget Alerts Step by Step
Set up cloud budget alerts so a forgotten GPU or runaway job never produces a surprise bill, with thresholds and actions explained for beginners.
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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.