Built on the open-source reap CLI · v0.2

Catch GPU waste before it hits your cloud bill.

reap is a static linter for your Kubernetes ML manifests. It flags idle GPUs, missing limits, and runaway jobs right in your pull requests — with the dollar cost attached. No agents. No cluster access. Runs in CI.

$ go install github.com/reap-labs/reap@latest  ·  free & open source
reap ./manifests/ — GPU cost review
reap ./manifests/train-job.yaml
✗ GPU-004  train-job.yaml:22  Job has no activeDeadlineSeconds
  A hung job can hold 4× A100 indefinitely  → ~$118/day at risk
⚠ GPU-001  infer.yaml:14  GPU request without matching limit
  Scheduler may pack extra pods onto the node  → noisy-neighbor risk
⚠ GPU-007  infer.yaml:14  Inference server missing readiness probe
  Traffic routed to cold pods  → wasted warmup GPU-seconds
✓ 7 rules passed   3 findings   est. $3,540/mo exposure
 
→ full report + PR comment posted to #42
5%
Average GPU utilization in K8s clusters
+15%
H200 price rise, Jan 2026 — first hike since 2006
10×
Gap between average and achievable GPU efficiency
0
Agents or cluster credentials reap needs
The problem

Your GPU bill is decided in code review — nobody's looking there.

The expensive mistakes ship as YAML: a job with no deadline, a GPU request with no limit, an inference pod with no readiness probe. By the time a dashboard shows the waste, you've already paid for it. reap moves the check left — to the pull request, where the config is still cheap to fix.

// idle

Idle & runaway GPUs

Jobs that never terminate, notebooks left running, pods sized for peak and idle all night. reap flags the patterns that turn into dollars-per-hour bleed.

// safety

Production-readiness gaps

Missing shared-memory config that crashes PyTorch/NCCL, absent probes, no gang scheduling for distributed training. Caught before deploy, not after the page.

// governance

Policy across the org

Baseline the issues you can't fix yet, gate CI on new ones, and enforce one standard across every team's repo — without touching a running cluster.

How it works

Three steps. No infrastructure to install.

1

Add reap to CI

Drop in the GitHub Action or run helm template . | reap -. It reads your manifests locally — no cluster credentials, ever.

2

Get findings on every PR

reap comments on the pull request with each GPU risk and its estimated monthly cost. New issues fail the check; known ones stay baselined.

3

Track it in the dashboard

The hosted app rolls up findings and dollar exposure across all repos over time — so you can prove the waste you prevented.

Why reap

The enterprise tools weren't built for your team.

Cast AI, ScaleOps and friends want an agent in your cluster, write access to your node pools, a multi-week rollout, and 10–20% of your cloud spend. If you run a handful of GPUs, the fee costs more than the savings. reap is the opposite end of the stack.

Enterprise autoscalersreap
Cluster agent requiredYes — write accessNo — static only
Time to first valueWeeksMinutes
Catches waste…After it's runningBefore merge
Pricing10–20% of cloud spendFlat, from free
Fits < 50 GPUsROI doesn't add upBuilt for it
Open-source coreNoYes
Pricing

Start free. Pay when it saves you money.

CLI
$0
The open-source linter. Yours forever.
  • All GPU & readiness rules
  • Run locally or in CI
  • JSON output & baselines
  • Hosted dashboard
  • Cost estimates
Install from GitHub
EARLY ACCESS
Team
$49/mo
For teams that want dollars attached and history tracked.
  • Everything in CLI
  • GitHub App — auto PR comments
  • Dollar-cost estimates per finding
  • Cross-repo dashboard & trends
  • Shared team baselines
Get early access
Enterprise
Custom
Self-hosted, for security-conscious orgs.
  • Everything in Team
  • Self-hosted / air-gapped
  • Custom & private rule packs
  • SSO & audit logs
  • Priority support
Talk to us
Early access

Stop paying for GPUs you never used.

Join the early access list. We'll onboard teams in small batches and you'll help shape the rules that matter for your stack.

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No spam. Just a note when your batch opens.