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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Scheduling Batch Jobs for Off-Peak Spot Pricing
Move flexible batch workloads to off-peak windows and spot capacity to cut GPU costs without changing the work itself.
Tracking Cost Per Request: Unit Economics for AI Features
Learn to measure the true cost per request for AI features so you can price, forecast, and optimize inference with confidence.
Mixed Precision Training: Faster Runs at a Fraction of the Cost
Mixed precision training uses lower-precision math to speed up runs and shrink memory use, cutting GPU cost while preserving model quality.
Avoid Overprovisioning Cloud Storage: Pay for What You Use
Overprovisioned volumes, forgotten snapshots, and the wrong storage tier quietly inflate cloud bills. Learn to right-size storage and pay for what you use.
GPU Sharing With MIG: Splitting One A100 Across Many Jobs
Multi-Instance GPU lets you partition one A100 into isolated slices for many small jobs, raising utilization and cutting cost per workload.
Preemptible vs Spot vs Interruptible: Same Discount, Different Names
Spot, preemptible, and interruptible all describe the same idea: deep discounts on capacity that can be reclaimed. Here is what actually differs.
Model Distillation for Cost: Shrinking Models to Cut Inference Spend
Model distillation trains a small student to mimic a large teacher, cutting inference cost dramatically. Here is how it works and when it pays off.
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.
Architecting for Low Data Transfer: Keep Compute Near Your Data
Egress and cross-region transfer quietly dominate many cloud bills. Learn to architect around data gravity and keep compute close to data.
Multi-Cloud GPU Arbitrage: Chasing the Cheapest Rates Across Providers
Multi-cloud GPU arbitrage routes workloads to the cheapest provider in real time. Here is when it pays off and when hidden costs eat the savings.
Caching Strategies to Cut LLM Inference Bills by Half
Prompt caching, semantic caching, and KV reuse can dramatically cut LLM inference spend. Here is how each works and when to use it.
Negotiating Committed Spend Discounts With GPU Cloud Vendors
Learn how to negotiate committed spend discounts with GPU cloud vendors, from baselining usage to structuring flexible multi-year deals.
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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.