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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Long Context Inference: Why 128K Windows Get Expensive Fast
Large context windows are convenient but costly. Here is why filling a 128K window inflates both price and latency, and when to use retrieval instead.
Function Calling and Tool Use: The Hidden Token Overhead
Tool definitions and multi-step tool loops quietly inflate token counts. Here is where function calling spends tokens and how to trim the bill.
How to Benchmark LLM Inference Providers Fairly
Vendor benchmarks rarely match production. Here is a fair methodology for comparing inference providers on speed, cost, and quality.
Tensor Parallelism for Inference: Splitting Big Models Across GPUs
When a model is too large for one GPU, tensor parallelism splits each layer across several. Here is how it works and what it costs you.
Cold Starts in Serverless Inference: Causes and Fixes
Serverless GPU inference saves money when idle but can stall on cold starts. Here is what causes the delay and how to keep responses fast.
Multi-Model Routing: Sending Easy Prompts to Cheap Models
Most prompts do not need your most expensive model. Routing easy requests to cheaper models can cut inference bills sharply without hurting quality.
Generating Embeddings at Scale: Cheapest Path for Billions of Vectors
Embedding billions of documents is a throughput problem, not a chat problem. Here is how to find the cheapest path from raw text to stored vectors.
Streaming LLM Responses: Time to First Token and Why It Matters
Time to first token shapes how fast an LLM feels. Learn what TTFT measures, what drives it, and how to compare providers on streaming latency.
NVIDIA L40S Cloud Pricing: A Budget GPU for Inference and Rendering
A guide to NVIDIA L40S cloud pricing: why this versatile GPU is a budget pick for inference and rendering, and how to compare its value.
A100 40GB vs 80GB in the Cloud: Does VRAM Justify the Price?
A100 40GB vs 80GB in the cloud: how the extra VRAM affects model size, batch size, and cost, and when paying more is worth it.
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.
Neoclouds Explained: The New GPU Providers Undercutting Hyperscalers
What neoclouds are, how these specialist GPU providers undercut hyperscalers on price, and the trade-offs to weigh before you switch.
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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.
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.
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.
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.