---
name: coreweave-cost-tuning-ccc3ba1a1823
description: 'Optimize CoreWeave GPU cloud costs with right-sizing and scheduling.

  Use when reducing GPU spend, selecting cost-effective instances,

  or implementing scale-to-zero for dev workloads.

  Trigger with phrases like "coreweave cost", "coreweave pricing",

  "reduce coreweave spend", "coreweave budget".

  '
allowed-tools: Read, Write, Edit, Bash(kubectl:*), Grep
version: 1.11.0
license: MIT
author: Jeremy Longshore <jeremy@intentsolutions.io>
tags:
- saas
- gpu-cloud
- kubernetes
- inference
- coreweave
compatibility: Designed for Claude Code
---
# 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

```yaml
autoscaling.knative.dev/minScale: "0"
autoscaling.knative.dev/scaleDownDelay: "5m"
```

### Right-Size GPU Selection

```python
def recommend_gpu(model_size_b: float, inference_only: bool = True) -> str:
    if model_size_b <= 7:
        return "L40" if inference_only else "A100_PCIE_80GB"
    elif model_size_b <= 13:
        return "A100_PCIE_80GB"
    elif model_size_b <= 70:
        return "A100_PCIE_80GB (4x tensor parallel)"
    else:
        return "H100_SXM5 (8x tensor parallel)"
```

### Quantization to Use Smaller GPUs

Use AWQ or GPTQ quantization to fit larger models on smaller GPUs:

```bash
# 70B model at 4-bit fits on single A100-80GB instead of 4x
vllm serve meta-llama/Llama-3.1-70B-Instruct-AWQ --quantization awq
```

## 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.
- A right-sizing or scale-to-zero recommendation with its performance guardrails.
- A reversible change record containing owner, observation window, and rollback threshold.

## Error Handling

| Condition | Likely cause | Safe response |
|---|---|---|
| Latency rises after downsizing | Insufficient GPU capacity or queueing | Restore the previous resource request and investigate with the service owner. |
| Cost data is incomplete | Labels or billing export are missing | Stop the optimization; repair allocation labels before making a pricing decision. |
| Quantized canary loses quality | Model or quantization setting is unsuitable | Route traffic back to the baseline model and retain the evaluation result. |
| Scale-to-zero causes cold-start failures | Delay or startup budget is too small | Restore minimum replicas for the affected service and tune in a non-production lane. |

## Examples

Run an approved staging canary with an explicit resource limit, then compare it to
the baseline before touching production:

```bash
kubectl -n inference-staging patch deployment summarizer \
  --type merge -p '{"spec":{"template":{"spec":{"containers":[{"name":"server","resources":{"limits":{"nvidia.com/gpu":1}}}]}}}}'
kubectl -n inference-staging rollout status deployment/summarizer --timeout=10m
```

If p95 latency, error rate, or evaluation quality crosses the signed threshold,
restore the prior manifest and attach the redacted measurements to the change record.

## Resources

- [CoreWeave Pricing](https://www.coreweave.com/pricing)
- [CoreWeave GPU Instances](https://docs.coreweave.com/docs/platform/instances/gpu-instances)

## Next Steps

For architecture patterns, see `coreweave-reference-architecture`.


---
Source attribution: jeremylongshore
Original project: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/ab85a061f7bcc039dac13ee193de7a4cd4388571/plugins/saas-packs/coreweave-pack/skills/coreweave-cost-tuning/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 Jeremy Longshore

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: jeremylongshore
Original project: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/ab85a061f7bcc039dac13ee193de7a4cd4388571/plugins/saas-packs/coreweave-pack/skills/coreweave-cost-tuning/SKILL.md
Attribution basis: Source repository record

Fields of work: Development & Engineering
Roles: Data Engineer, DevOps / SRE, ML / AI Engineer
Specialties: Backend, Design, DevOps
Tools: Kubernetes, Coreweave
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 32743a160b46a6e2198661d3d4ca761aee9291071cc02215ec5e75787e754d63