Agent skill · data analytics · jeremylongshore
coreweave-cost-tuning
Optimize CoreWeave GPU cloud costs with right-sizing and scheduling.
Why this skill is useful
Provides specific cost optimization strategies and executable scripts for GPU instance selection that the AI wouldn't reliably generate on its own.
What it needs
Requires kubectl installed locally. Requires coreweave account access. About 3k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
CoreWeave Cost Tuning Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc. Overview Reduce GPU spend by matching a workload's memory, throughput, availability, and latency requirements to the smallest approved capacity. Cost changes must preserve the service SLO and retain a measured rollback path; approximate public prices are planning inputs, not a billing source of truth. Prerequisites Read-only access to workload utilization, namespace quota, and billing allocation data. A named service owner and target SLO for the workload being resized. Approval for any change that can reduce production capacity or alter availability. GPU Pricing Reference (approximate) GPU Per GPU/hour Best For ----- ------------- ---------- A100 40GB PCIe ~$1.50 Development, smaller models A100 80GB PCIe ~$2.21 Production inference H100 80GB PCIe ~$4.76 High-throughput inference H100 SXM5 (8x) ~$6.15/GPU Training, multi-GPU L40 ~$1.10 Image generation, light inference Cost Optimization Strategies Scale-to-Zero for Dev/Staging Right-Size GPU Selection Quantization to Use Smaller GPUs Use AWQ or GPTQ quantization to fit larger models on smaller GPUs: Instructions 1. Baseline seven days of GPU utilization, queue time, request latency, error rate, and allocated cost by namespace; do not decide from a single peak. 2. Propose one reversible change—right-size a non-production workload, set a conservative scale-down delay, or run a quantized canary. 3. Compare the canary against its SLO and budget for an agreed observation window. Promote only if quality, latency, and error rate remain within the published limit. 4. Record the instance choice, owner, forecast, and rollback trigger in the change record. Revert capacity immediately if the workload breaches its SLO. Output A documented utilization baseline and cost allocation for the selected workload. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/coreweave-cost-tuning