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
SageMaker vs Self-Managed GPU Instances: Convenience vs Cost
Managed ML platform or raw GPU instances you run yourself? Weigh SageMaker against DIY GPU on cost, control, and effort.
Oracle Cloud GPU vs AWS: The Underdog Hyperscaler for GPUs
Oracle Cloud is a quieter hyperscaler with competitive GPU and networking. Compare OCI and AWS for GPU workloads.
DeepInfra vs Together AI: Cheapest Open Model Inference?
Both serve open-weight models by the token at aggressive prices. Compare DeepInfra and Together AI on cost, models, and performance.
CoreWeave vs Lambda vs Crusoe: Three Neoclouds Benchmarked
Three specialist GPU clouds, three strategies. Compare CoreWeave, Lambda, and Crusoe on GPU access, pricing, and scale.
Google Vertex AI vs AWS Bedrock: Managed LLM Platforms Compared
Two hyperscaler managed LLM platforms, two philosophies. Compare Vertex AI and Bedrock on model choice, pricing, and integration.
Azure OpenAI vs OpenAI Direct: Pricing, Limits, and Compliance
Same models, two front doors. Compare Azure OpenAI and OpenAI direct on pricing, rate limits, data handling, and enterprise compliance.
Paperspace vs RunPod: Notebooks and GPU Rental Compared
From hosted notebooks to raw GPU pods, Paperspace and RunPod overlap but lean different ways. Here is how to pick the right one.
Hyperscalers vs Neoclouds: Total Cost of Ownership for GPU Workloads
The cheapest GPU hour rarely wins. Here is how to compare hyperscalers and neoclouds on total cost of ownership for GPU workloads.
Replicate vs Modal: Serverless GPU Platforms Head to Head
Run models without managing servers. Here is how Replicate and Modal differ in approach, pricing, and the kind of builder each suits.
Groq vs Cerebras: Specialized Inference Hardware Compared
Two custom silicon makers chasing the same goal: dramatically faster LLM inference. Here is how Groq and Cerebras differ in approach and fit.
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.
OpenAI vs Anthropic API Pricing: Cost Per Task Compared
Per-token rates only tell half the story. Here is how to compare OpenAI and Anthropic on the metric that matters: cost per completed task.
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.
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.
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.
GPU Sharing With MIG: Splitting One A100 Across Many Jobs
Multi-Instance GPU lets you partition one A100 into isolated slices for many small jobs, raising utilization and cutting cost per workload.
Caching Strategies to Cut LLM Inference Bills by Half
Prompt caching, semantic caching, and KV reuse can dramatically cut LLM inference spend. Here is how each works and when to use it.
AMD MI300X Cloud Providers: Where to Rent and What It Costs
A guide to renting the AMD MI300X in the cloud: where it is available, how pricing compares, and the workloads where it makes the most sense.
Managed Kubernetes pricing guide: every line item
The control plane is the smallest part of your Kubernetes bill. Here is where the real money goes - nodes, load balancers, egress, and GPU pools.
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.