Opportunities › AI workload orchestration on Kubernetes › Proposed app
Queuebay
Fair GPU scheduling and idle-accelerator tracking for Kubernetes AI teams
- Score
- 45
- confidence 38
- Evidence
- 2
- items on the page
- Rivals
- 1
- named in sources
- First version
- 45 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2018
- Established market · AI estimate
ScreensConcept
Desktop app: Engineers monitor dense GPU queues, job dependencies, and team quotas over long sessions while managing cluster operations from their desk. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Cluster overview
12 GPUs reclaimable71%
- GPUs in cluster
About this appAI proposal
Queuebay is a lightweight control layer for teams that run AI jobs on shared Kubernetes clusters. It reads your GPU nodes and queues, then shows who holds which accelerators, which are idle, and which jobs are waiting and why.
Platform engineers set team quotas, priorities and borrowing rules in one place. ML engineers submit jobs with a plain request (GPU type, count, deadline) and see an estimate of when it will start, instead of guessing or writing their own scripts.
It is a web dashboard and policy service, not a replacement for your cluster. It installs as a small in-cluster agent and works alongside the standard Kubernetes scheduler. Built for small and mid-size platform teams.
- Tagline
- Every GPU in its place. Every job in line.
- Voice
- PlainPreciseCalm
- Why this name
- A bay is where vehicles wait their turn to dock, like jobs waiting for accelerators. Short, pronounceable, plain.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Idle GPU finder
Flags accelerators that are allocated but barely used, with the owning team and job, so you can reclaim them.
No member yet
Team quotas with borrowing
Give each team a guaranteed share. Unused capacity can be lent out and taken back when the owner needs it.
No member yet
Priority queues
Rank jobs by priority and deadline. Urgent training runs move ahead without manual node juggling.
No member yet
Start-time estimates
Every waiting job shows the reason it waits and a plain estimate of when it will start.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#0F4C5CDeep Teal · primary#F3F6F5Mist · surface#14232AInk · text#E09F3EAmber · accent#5B8C5AMoss · data
Typography
Aa
Queuebay
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Every GPU in its place. Every job in line.
Sora for headlines and numbers, IBM Plex Sans for everything else.
PricingAI proposal
The material gives no competitor prices. These tiers, scaled by GPUs managed as the opportunity suggests, are a starting point to test.
Starter
Free
- Up to 8 GPUs
- Cluster overview
- Idle GPU finder
Team
$299 a month
- Up to 64 GPUs
- Quotas and borrowing
- Priority queues
- Usage reports
Platform
$999 a month
- Up to 256 GPUs
- Job dependencies
- Alerts
- SSO
Would you pay Team · $299 a month?
Your answer tells PWAS which prices people accept. It changes no score.
No answers yet.
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$194,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- Users invite others. Platform engineers invite team leads and ML engineers to see their own quota, queue and start estimates. Each team lead invited sees the product daily.
- Do they become customers?
- Invitees are inside the same organization and can push for a paid plan, but they do not buy alone. Test whether invites from teams lead the platform owner to upgrade.
- First customers
- Platform engineers at companies with 3 to 6 ML teams sharing one on-prem or cloud Kubernetes GPU cluster of 16 to 128 GPUs.
- Where to reach them
- Kubernetes community forums: Share a free idle-GPU check and write up findings in CNCF Slack and ML infrastructure groups.
- Technical content: Publish guides on fair sharing of GPUs, with an open-source agent for the Starter tier.
- Direct outreach: Message platform leads at teams posting about GPU contention or scheduling in public repositories.
- First milestone
- Ten clusters on Starter and three paying Team customers within 90 days, each showing reclaimed idle GPU hours.
Why people already pay for this
- VerifiedGitHub repository gained 640 stars in 61 days
- Public claimRun:ai is a monorepo containing control plane services, in-cluster Kubernetes operator/controllers, shared libraries and clients, the installer, and local dev environment for AI workload orchestration on Kubernetes.
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · In-cluster agentSmall Kubernetes operator in GoReads nodes, pods and GPU metrics and applies queue decisions inside the customer's cluster.
- This app · Metrics ingestionTime-series store or Postgres extensionHolds GPU utilization samples to detect idle accelerators and build reports.
RisksAI inference
- CommercialAn established NVIDIA-linked product occupies this space; buyers may choose it, or open-source queueing, over a small newcomer.
- TechnicalRunning an agent in customer clusters across versions, GPU vendors and security setups is hard to support for a small team.
- DataAccurate idle detection needs reliable GPU utilization metrics, which differ by driver and may not be exposed.
- PlatformKubernetes scheduling projects change fast; the integration may break or be absorbed by upstream features.
- RegulatoryEnterprise buyers may require security reviews before allowing an agent to read cluster and job metadata.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.