Hunt down CoreWeave GPU cost leaks โ idle reserved capacity, wrong-GPU-type
right-sizing waste, allocated-but-idle instances, and on-demand spend that should
be committed โ then produce a CFO-grokkable, dollar-ranked FinOps report. CoreWeave
ships no cost dashboard and no billing API, so the spend view is built from PromQL
against its managed Grafana. Use when a user asks why their CoreWeave GPU bill is
high, wants to find wasted GPU spend or idle reservations, or needs a GPU FinOps
cost report. Trigger with "coreweave cost", "why is my coreweave bill",
"wasted GPU spend", "idle reserved capacity", "GPU cost leak".
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
A direct command skips the review prompt. Inspect the source before running it.
Hunt down CoreWeave GPU cost leaks โ idle reserved capacity, wrong-GPU-type
right-sizing waste, allocated-but-idle instances, and on-demand spend that should
be committed โ then produce a CFO-grokkable, dollar-ranked FinOps report. CoreWeave
ships no cost dashboard and no billing API, so the spend view is built from PromQL
against its managed Grafana. Use when a user asks why their CoreWeave GPU bill is
high, wants to find wasted GPU spend or idle reservations, or needs a GPU FinOps
cost report. Trigger with "coreweave cost", "why is my coreweave bill",
"wasted GPU spend", "idle reserved capacity", "GPU cost leak".
Designed for Claude Code, also compatible with Codex
tags
["saas","coreweave","gpu-cloud","finops","cost"]
CoreWeave GPU Cost Leak Hunter
Community-contributed. Not affiliated with, endorsed by, or sponsored by
CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc.
Audits a CoreWeave GPU cluster for real-dollar cost leaks โ idle reserved capacity,
GPUs on the wrong SKU, allocated-but-idle instances, and steady on-demand spend that
should be committed โ then emits a CFO-grokkable, dollar-ranked FinOps report.
Overview
CoreWeave ships no cost dashboard and no billing API (usage-monitoring
docs). There is also no single "dollars" metric โ spend is reconstructed by
querying usage from CoreWeave's managed Grafana in PromQL and multiplying each
resource's usage by its rate-card price. This skill does exactly that, then ranks the
leaks by monthly dollar impact.
The math is deterministic: PromQL returns usage counts, and the bundled
scripts/rank-and-report.py does every multiplication, sum, and ranking โ the agent
never eyeballs a number. Two of the four categories are billed waste (Confirmed);
the other two are a right-sizing model (Estimated) and a commitment decision
(At-risk), labeled so a CFO never reads a modeled number as recoverable cash.
Deep domain knowledge lives in references/, loaded only when a leak needs it.
Prerequisites
CoreWeave managed Grafana access โ the Prometheus data source is reachable only
to a member of the admin, metrics, or write group in the CoreWeave Cloud
Console (usage-monitoring docs). This is the hard dependency; Step 1 probes it
and fails fast if the group is missing.
A Prometheus/Grafana query endpoint in $CW_PROM_URL (the Grafana data-source
proxy, e.g. https://grafana.ORG.coreweave.com/api/datasources/proxy/uid/UID)
and a bearer token in $CW_TOKEN for curl.
kubeconfig for the cluster (CoreWeave-issued) so kubectl get can corroborate
live GPU allocation and node labels.
The rate card โ CoreWeave publishes no price metric, so on-demand and committed
rates are supplied to the ranker from references/gpu-right-sizing.md (dated
snapshot of coreweave.com/pricing) or the customer's contract.
jq and python3 for parsing query JSON and running the ranker.
Authentication. All auth comes from the environment ($CW_PROM_URL, $CW_TOKEN,
$KUBECONFIG) โ no secrets are hardcoded. Grafana enforces the group membership above
on every query.
Instructions
The pipeline is detect โ price โ rank โ report. PromQL returns usage; the dollar
arithmetic runs in scripts/; deep knowledge loads from references/ on demand:
Verify metric access, fail fast if the group is missing.
Pull the 30-day spend baseline (usage ร rate card).
Detect Leak 4 โ on-demand spend that should be committed (At-risk).
Rank by monthly dollar impact and render the CFO report.
Step 1: Verify Metric Access (fail fast, not mid-flow)
Probe billing:instance:total before anything else. An HTTP 401/403 or empty result
means the token's principal is not in admin/metrics/write โ STOP and report it;
do not continue into the scans.
If status is not success with a non-empty result, load
references/promql-billing-setup.md and report
the missing group access verbatim. Stop here.
Step 2: Pull the Spend Baseline
Reconstruct 30-day GPU node-hours per instance type. CoreWeave has no dollars metric,
so this returns usage โ the ranker multiplies by the rate card. Write the JSON to
the working dir for the ranker.
The rate card and the per-category PromQL live in
references/gpu-cost-leak-categories.md.
Load it now โ the four scans below reference its recording-rule notes.
Reserved GPUs bill at the committed rate whether used or not. A reserved GPU
sitting below a utilization floor is confirmed waste โ you paid for it and it did no
work. Cross reserved allocation (billing_gpu, filtered by the reservation label)
against SM-active from DCGM.
curl -sS -H "Authorization: Bearer $CW_TOKEN" --data-urlencode \
'query=sum by (instance_type,node) (avg_over_time(billing_gpu{reservation!=""}[30d:1h]))
and on(node) (avg by (node) (avg_over_time(DCGM_FI_PROF_SM_ACTIVE[30d:1h])) < 0.05)' \
"$CW_PROM_URL/api/v1/query" > "$OUT/leak1-idle-reserved.json"
The reservation label key is provider-specific โ confirm yours with kubectl get nodes --show-labels. Waste = idle reserved GPU-hours ร committed rate (ranker input).
H100/H200 running small-model (~7Bโ30B) inference is over-paying: for that regime
L40S is cheaper per token (directional โ see gpu-right-sizing.md). Flag those
instance-hours; the ranker re-prices them at the L40S rate.
This is Estimated: the rate delta is exact rate-card math, but throughput
equivalence on L40S is a model. Confirm the served model size with the cluster owner
before acting; FP8 serving needs Hopper/Ada, not Ampere (see gpu-right-sizing.md).
On-demand GPUs that are allocated (billing) but running at low SM-utilization / low
MFU bill the full on-demand rate for no work โ confirmed billed waste, the GPU twin of
an idle cluster.
curl -sS -H "Authorization: Bearer $CW_TOKEN" --data-urlencode \
'query=(avg by (node,instance_type) (avg_over_time(DCGM_FI_PROF_SM_ACTIVE[30d:1h])) < 0.05)
and on(node) (sum by (node) (avg_over_time(billing_gpu{reservation=""}[30d:1h])) > 0)' \
"$CW_PROM_URL/api/v1/query" > "$OUT/leak3-idle-ondemand.json"
Corroborate with kubectl get pods -A --field-selector=status.phase=Running to
confirm nothing is actually scheduled on the flagged node. Waste = idle on-demand
GPU-hours ร on-demand rate.
Step 6: Leak 4 โ On-Demand Spend That Should Be Committed (At-risk)
A stable on-demand floor โ GPUs of one type always running across the window โ is
paying on-demand for capacity a commitment discounts up to 60% (pricing).
Measure the always-on floor with min_over_time.
This is At-risk: the up-to-60% saving is pending a commitment decision, and a
commitment is itself a paid obligation โ see the over-reservation caution in
gpu-cost-leak-categories.md. Savings = floor GPU-hours ร on-demand rate ร discount.
Step 7: Rank and Write the Report
Assemble one leak object per category from the PromQL usage results plus the rate
card, then pipe them to the deterministic ranker โ the LLM does NOT do the arithmetic.
Because CoreWeave exposes no dollars metric, each object carries usage_gpu_hours and
its rate-card rate; the ranker multiplies usage ร rate itself (and applies the
re-price or discount factor). Each object's kind (confirmed / estimated /
at-risk) tells the renderer to split the headline confirmed-vs-pending, rank
descending by monthly dollars, and stamp a Confidence column.
OUT="${OUT:-$(pwd)/cost-leak-out}" && mkdir -p "$OUT"# Each Step wrote a leak-N.json {category, root_cause, fix, kind, usage_gpu_hours,# rate_usd_per_gpu_hour, ...}; the ranker does usage ร rate deterministically.
jq -s '.'"$OUT"/leak-*.json | \
python3 scripts/rank-and-report.py \
--monthly-spend 180000 --window-end "$WINDOW_END" \
--out "$OUT/cost-leak-report.md"
Render the output using the verbatim template in
references/cfo-output-format.md. Use Glob to
collect the per-leak JSON, Write the report, and Edit it to rescale the headline
spend on request.
Output
A CFO-grokkable report leading with a split headline that never sums
confirmed and unconfirmed dollars under one verb โ A $180K/month CoreWeave GPU cluster is burning ~$40K/month (confirmed), plus up to ~$29K/month pending review โ
each with a /year companion.
A trailing-30-day window stamp so every figure has an explicit calendar window.
The ranked leak table (# | Where it's leaking | $/month | Confidence | The fix), one row per category, highest dollar impact first, each fix a single change.
The #1-line callout โ the top leak annualized, named, with its confidence.
Per-leak detail artifacts โ the flagged nodes/instance types, the PromQL that
found them, and the underlying $/GPU-hour rates for the cluster engineer.
Error Handling
Error
Cause
Solution
HTTP 401/403 on /api/v1/query
Token principal not in admin/metrics/write
Run Step 1; report the group requirement from promql-billing-setup.md. Stop.
Empty result for billing:instance:total
Wrong data-source proxy UID, or org has no billing metrics enabled
Verify $CW_PROM_URL points at the Grafana Prometheus proxy; confirm in Grafana Explore.
DCGM_* series absent
DCGM exporter not scraped on the node pool
Skip Leaks 1/3 utilization filter for that pool; note "utilization unavailable" rather than reporting $0.
reservation label missing
Provider label key differs per org
Confirm the reservation/committed label with kubectl get nodes --show-labels; substitute it in the query.
Ranker prints ~$0/month confirmed
A kind value was mis-cased and dropped from the sum
The ranker normalizes case; verify each leak object's kind is one of the three tiers.
Examples
Example 1: "Why is my CoreWeave bill so high?"
Runs the full pipeline. The access probe passes, the four scans return rows, and the
ranker emits a split, confidence-stamped report:
### A $180K/month CoreWeave GPU cluster is burning **~$44,986/month** (confirmed), plus up to **~$29,110/month** pending review
Trailing 30 days ending 2026-06-22. Confirmed **~$540K/year**; up to **~$349K/year** more pending review. Spend is reconstructed from PromQL against CoreWeave's managed Grafana (no billing API). Every line below is one change.
| # | Where it's leaking | $/month | Confidence | The fix |
|---|---|--:|---|---|
| 1 | **Idle reserved GPUs** โ reserved capacity billing around the clock below a utilization floor | **$26,280** | Confirmed | Right-size or release the reservation |
| 2 | **Allocated-but-idle on-demand GPUs** โ nodes up at <5% SM-active, paying full rate for no work | **$18,706** | Confirmed | Scale-to-zero / deschedule the idle nodes |
| 3 | **H100/H200 on small-model inference** โ L40S is cheaper per token in the 7Bโ30B regime | **$16,629** | Estimated | Move small inference to L40S |
| 4 | **Steady on-demand that should be committed** โ an always-on floor paying on-demand | **$12,481** | At-risk | Commit the stable floor (up to 60% off) |
**The #1 line alone โ idle reserved gpus (confirmed) โ is ~$315K/year, fixed in one setting.**
Example 2: Idle-Reservation Sweep
User asks "are we paying for idle reserved GPUs?" The skill runs Step 3 only, crosses
billing_gpu{reservation!=""} against DCGM_FI_PROF_SM_ACTIVE, and reports each
reserved node below the floor with its 30-day committed spend.
