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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Single GPU vs Cluster Rental: How Much Compute Do You Actually Need?
Most workloads need one GPU, not a cluster. Here is how to size your compute honestly and avoid renting more than the job requires.
Best GPU Cloud for Fine-Tuning LLMs Without Overpaying
Fine-tuning an LLM rarely needs the biggest cluster. Here is how to pick GPU cloud capacity that fits the method and avoids overpaying.
On-Demand vs Reserved GPU Instances: Picking the Right Commitment
On-demand keeps you flexible; reserved cuts the rate if utilization stays high. Here is how to choose the commitment that fits your workload.
GPU Cloud Glossary: 40 Terms Every Buyer Should Know
From HBM and NVLink to spot pricing and egress, this glossary defines the 40 GPU cloud terms that show up on every quote and datasheet.
Multi-GPU NVLink Clusters in the Cloud: 8x H100 Nodes Compared
Eight H100s in one node is the workhorse of modern AI training. Here is how NVLink, NVSwitch, and node design shape real cloud performance.
GPU Cloud for Startups: Picking Infrastructure Without Burning Cash
A startup playbook for choosing GPU cloud infrastructure that scales with demand without locking you into expensive idle capacity.
RTX 4090 Cloud vs Datacenter GPUs: When Consumer Cards Win
Rented RTX 4090s are cheap and fast for many jobs. Here is where consumer cards beat datacenter GPUs and where they fall short.
H200 vs H100: Is the Extra HBM3e Memory Worth It in the Cloud?
The H200 keeps the H100 compute engine but adds far more HBM3e memory and bandwidth. Here is when that upgrade pays off in rented cloud GPUs.
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.
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.
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.
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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.