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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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.
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.
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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.
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.
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.
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.
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 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.