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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vLLM vs TGI: Inference Throughput and Cost per Token Benchmarked
vLLM and TGI are two leading LLM serving engines. Here is how they compare on throughput, latency, and the cost per token that follows from both.
Self-Hosting LLMs vs Using an API: The Real Cost Breakeven
Self-hosting an LLM looks cheaper per token, but the breakeven depends on volume and utilization. Here is how to find where it actually pays off.
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.
Nebius vs CoreWeave: Comparing the New GPU Cloud Challengers
Nebius and CoreWeave are two leading neoclouds built for AI. Here is how their GPU offerings, pricing, and platforms compare for demanding workloads.
Salad vs Vast.ai: Distributed and Crowdsourced GPU Compared
Salad and Vast.ai both rent GPU capacity from distributed sources at low prices. Here is how their models compare and when each fits your workload.
Mistral vs Cohere API: European LLM Providers Compared
Mistral and Cohere both offer credible alternatives to the largest LLM APIs. Here is how their models, pricing, and strengths compare for builders.
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 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.
Crusoe vs FluidStack: Sustainable and Aggregated GPU Clouds Compared
One builds low-carbon data centers, the other aggregates GPU supply. Compare Crusoe and FluidStack for AI compute.
OpenRouter vs Direct LLM APIs: Does the Router Markup Pay Off?
One API for many models versus going direct to each provider. Weigh OpenRouter's convenience against any markup.
DigitalOcean vs Akamai Linode GPU: Developer-Friendly GPU Clouds
Two developer-loved clouds now offer GPUs. Compare DigitalOcean and Akamai Linode on GPU pricing, simplicity, and fit.
Baseten vs Modal vs Replicate: Model Deployment Platforms Compared
Three platforms that turn model code into scalable endpoints. Compare Baseten, Modal, and Replicate on deployment, scaling, and cost.
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.
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.
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.
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.
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.