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 a Fault-Tolerant Spot Training Job From Scratch
Build a training job that survives spot interruptions through checkpointing, automatic resume, and a sensible fallback.
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.
Build a Spot-to-On-Demand Fallback for Reliable Cheap GPUs
An automation tutorial for combining cheap spot GPUs with an on-demand fallback so workloads stay reliable when spot capacity is reclaimed.
Deploy a RAG App on a Cloud GPU: Embeddings to Endpoint
An end-to-end tutorial on deploying a retrieval-augmented generation app on a cloud GPU, from embedding documents to a live, served endpoint.
Deploy an LLM With vLLM on a Cloud GPU: Full Walkthrough
A complete walkthrough to serve an open LLM with vLLM on a rented cloud GPU, from install to an OpenAI-compatible endpoint.
Profile Your Inference Server to Find the Real Bottleneck
An advanced tutorial on profiling an inference server to find whether the GPU, memory bandwidth, batching, or the host is the true bottleneck.
Estimate Your Project's GPU Cost Before You Provision Anything
A walkthrough for estimating GPU cost before provisioning, so you size hardware, pick a pricing model, and avoid budget surprises.
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.
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.
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.