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.
RAG Pipeline Costs: Where Retrieval-Augmented Generation Spends Money
RAG spends money in more places than the final answer. Here is a full breakdown of where retrieval-augmented generation costs add up and how to trim them.
On-Device vs Cloud Inference: When to Skip the GPU Cloud Entirely
Not every model needs a cloud GPU. Here is when running inference on the device wins on cost, latency, and privacy, and when the cloud is unavoidable.
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.
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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.
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.
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.
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.
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.
RAG Pipeline Costs: Where Retrieval-Augmented Generation Spends Money
RAG spends money in more places than the final answer. Here is a full breakdown of where retrieval-augmented generation costs add up and how to trim them.
On-Device vs Cloud Inference: When to Skip the GPU Cloud Entirely
Not every model needs a cloud GPU. Here is when running inference on the device wins on cost, latency, and privacy, and when the cloud is unavoidable.
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.