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.
Fresh off the desk
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.
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 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.
Open vs Closed Models: The Inference Economics That Actually Matter
The open versus closed model debate is really about who pays for the GPUs. Here is the economics that decides it.
Speculative Decoding: Faster, Cheaper LLM Inference Without Quality Loss
Speculative decoding speeds up generation by guessing ahead with a small model and verifying with the big one. Same output, less time.
KV Cache Explained: How It Drives Inference Memory and Cost
The KV cache is the quiet driver of LLM serving cost. Understand how it grows and you can serve more users per GPU.
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.
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.
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.
Quantization for Cheaper Inference: FP8, INT8, and INT4 Tradeoffs
Quantization shrinks models so they run on cheaper GPUs and serve faster. Here is how FP8, INT8, and INT4 trade cost against quality.
Reader favourites
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.
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 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.
Open vs Closed Models: The Inference Economics That Actually Matter
The open versus closed model debate is really about who pays for the GPUs. Here is the economics that decides it.
KV Cache Explained: How It Drives Inference Memory and Cost
The KV cache is the quiet driver of LLM serving cost. Understand how it grows and you can serve more users per GPU.
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.
LLM Inference Cost Optimization: 12 Levers to Cut Your Bill
Inference can quietly become your largest AI cost. Here are twelve practical levers to cut your LLM serving bill without wrecking quality.
Google TPU vs GPU: When Tensor Processing Units Beat NVIDIA
TPUs can be cheaper and faster than GPUs for the right workload. Here is how to tell when a Tensor Processing Unit beats NVIDIA, and when it does not.
AWS vs CoreWeave for H100s: Hyperscaler vs Neocloud Economics
Renting H100s from a hyperscaler versus a neocloud is a study in trade-offs. Here is how AWS and CoreWeave compare on real H100 economics.
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.
Image Generation API Pricing: Cost Per Image Across Providers
How image generation APIs price each render, from resolution and steps to quality tiers, and how to estimate your true cost per image at scale.