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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Object Storage Pricing Guide: The Five Costs That Matter
Object storage looks cheap at $0.02/GB-month, but storage is only one of five costs. Here is how egress, requests, durability, tiers, and minimum durations shape the real bill.
How to Cut S3 Egress Costs: 9 Levers That Actually Work
Egress is often the surprise line item on an object-storage bill. Here are the practical levers - caching, free-egress providers, same-region traffic, and compression - that cut it.
Self-Hosting LLMs vs Using an API: The Break-Even Math
When does renting a GPU beat paying per token? Work the break-even using GPU-hour cost, throughput, and utilization - with a concrete example and ranges.
Open-Weight vs Closed LLMs: Cost, Control, and Privacy
Open-weight models give you portability and self-hosting; closed APIs give you frontier quality with zero ops. Here is how to decide on cost, control, and data privacy.
How to Cut LLM Inference Costs Without Hurting Quality
Nine levers that reliably reduce LLM spend - cheaper provider, prompt caching, shorter prompts, batching, smaller models - ranked by effort and payoff.
LLM API Pricing Explained: Tokens, Context, and Blended Cost
Input, output, and cached tokens are priced differently, context windows cost more than you think, and the same model varies across providers. Here is how to read the bill.
How much GPU VRAM do you need?
The 2 GB-per-billion-params rule for FP16, why training needs far more, how LoRA and quantization cut it, and a table mapping model sizes to GPUs.
Spot vs on-demand vs reserved GPUs
The three GPU pricing modes explained: typical discounts, interruption risk, checkpointing strategy, and exactly when each one saves you the most money.
H100 vs A100 vs H200: which training GPU
VRAM, memory bandwidth, BF16 throughput, and hourly price compared across the three GPUs most teams choose between for training and fine-tuning.
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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.
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.
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.
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.
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 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.
Throughput vs Latency in LLM Inference: Optimizing the Right Metric
Optimizing throughput and latency at the same time pulls in opposite directions. Know which one your product actually needs.
Serverless vs Dedicated Inference Endpoints: Picking by Traffic Pattern
Serverless or dedicated? The right choice depends almost entirely on how your traffic behaves. Here is the decision framework.
Cost to Run Llama 3 70B in Production: GPU Sizing and Pricing
Running Llama 3 70B yourself means picking the right GPUs and keeping them busy. Here is how to size hardware and estimate the real production cost.
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.