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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Egress-Free Cloud Providers: Who Offers Zero Data Transfer Fees
Egress fees quietly inflate cloud bills. Here is how zero-egress and free-tier providers differ, and how to verify a genuine no-fee policy.
Estimating Fine-Tuning Costs: A Pricing Formula for LLM Training
A practical formula for estimating LLM fine-tuning costs, covering GPU hours, token volume, method, and the hidden line items that inflate your bill.
Embedding API Pricing Compared: Cheapest Vector Generation in 2026
How embedding API pricing works, why dimensions and token volume drive the bill, and how to find the cheapest vector generation for your use case.
How to Read a GPU Cloud Invoice and Spot Overbilling
A beginner's guide to reading a GPU cloud invoice line by line, decoding the charges, and catching the overbilling patterns that quietly inflate it.
Committed Use Discounts vs Savings Plans: Which Saves More?
Committed use discounts and savings plans both reward commitment, but they flex differently. Here is how they compare and which one fits your usage.
GPU Price Per TFLOP: Normalizing Cloud GPU Costs by Compute
How to normalize cloud GPU prices by compute using price per TFLOP, where the metric helps, and the caveats that keep it from telling the whole story.
Cloud Storage Tiers and Pricing: Hot, Cool, and Archive Compared
How hot, cool, and archive storage tiers are priced, the access and retrieval tradeoffs of each, and how to match data to the cheapest right tier.
Inter-Region Data Transfer Pricing: What Cross-Region Traffic Costs
Why moving data between cloud regions costs money, how inter-region transfer is billed, and practical ways to keep cross-region traffic in check.
GPU Cloud Pricing Models Compared: On-Demand, Spot, Reserved, Committed
A clear taxonomy of GPU cloud pricing models, from flexible on-demand to deeply discounted committed use, and how to choose the right mix.
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.
LLM Token Pricing Explained: Input vs Output Token Costs
A beginner-friendly guide to how LLM token pricing works, why input and output tokens cost different amounts, and how to estimate your bill.
Reserved Instance Discounts Explained: 1-Year vs 3-Year Commitments
How reserved instance discounts work, why 3-year terms cut more, and when a 1-year commitment is the safer bet for cloud GPU and compute spend.
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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.
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.
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.
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.