Opportunities › AI agent for GPU cluster operations › Proposed app
Rigwatch
An agent that triages failed GPU jobs and fixes them, with your approval
- Score
- 35
- confidence 48
- Evidence
- 12
- items on the page
- Rivals
- 3
- named in sources
- First version
- 45 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2012
- Mature market · AI estimate
ScreensConcept
Desktop app: Engineers monitor fleet dashboards, review failure diagnostics, and approve resource corrections—operational monitoring requiring wide visibility and detailed reasoning logs. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Failed run
Fix proposedOOM on node 14
- Job
About this appAI proposal
Rigwatch keeps a live model of your GPU fleet: nodes, jobs, GPU metrics and Kubernetes events in one place. When a training run fails, an agent reads those signals together and tells you what went wrong, in plain words, with the evidence it used.
It is built for platform engineers and ML researchers who run multi-day training on their own clusters. The agent can resubmit a job with corrected resources or cordon a bad node. Anything that touches production or another team waits for a person to approve it.
Unlike a stack of Prometheus, Grafana and scripts you maintain yourself, the fleet model and the agent's tools come ready to use. Every action shows its reasoning and can be reviewed afterwards.
- Tagline
- Your GPU fleet, watched and explained.
- Voice
- CalmPrecisePlainspoken
- Why this name
- A rig is what many teams call their GPU machines, and watch says what the agent does: it keeps looking so you don't have to.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Failure triage in one view
Correlates Kubernetes events, node logs and GPU metrics for a failed run and proposes a root cause with linked evidence.
No member yet
Live fleet model
See every GPU, node and job with current state and utilization, without building dashboards first.
No member yet
Approval gates
Routine fixes can run on their own. Cross-team or production changes pause until a named person approves.
No member yet
Resubmit with corrected resources
The agent proposes a fixed job spec, such as memory or GPU count, and shows the diff before it resubmits.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#0F3D3EDeep Teal · primary#F4F7F6Cool Paper · surface#14201FInk · text#F2A33ASignal Amber · accent#C8412FFault Red · data: failures#3E9B6BHealthy Green · data: healthy
Typography
Aa
Rigwatch
PricingAI proposal
The material gives no GPU-ops competitor prices. Grafana's pricing page lists a free plan and paid plans from $19 a month; the Team tier mirrors that as a starting point to test. Per-GPU pricing is untested.
Read-only
Free
- Up to 16 GPUs
- Fleet view
- Failure diagnosis, suggestions only
Team
$19 a month
- Up to 64 GPUs
- Agent actions with approvals
- Reasoning log export
Fleet
Contact us
- Per-GPU pricing
- Unlimited approvals policies
- SSO
- Priority support
Would you pay Team · $19 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
- No loop in the product. Used inside a company's infrastructure, so it spreads mainly through sales, content and word of mouth among platform teams. Exported postmortems may be shared internally, but that is not a reliable loop.
- Do they become customers?
- Researchers who use it may push their platform team to buy. Test whether researcher self-serve diagnosis leads to a paid team plan.
- First customers
- Platform engineers at ML teams of 10 to 50 people running their own Kubernetes GPU cluster with multi-day training jobs.
- Where to reach them
- Hacker News and infra forums: Share failure-triage write-ups and offer read-only trials to people discussing GPU ops pain.
- Direct outreach: Message platform leads at AI startups who run their own clusters and offer a free read-only trial.
- MLOps communities: Join Slack and Discord groups and answer failed-run debugging questions, using Rigwatch examples.
- First milestone
- Five teams run it read-only on a live cluster, and two pay for approved agent actions within 60 days.
Why people already pay for this
- VerifiedHacker News post received 26 points and 7 comments
- AI inferencePlatform engineers spend approximately half their time on infrastructure operations and maintenance rather than development.
- Public claimResearchers experience significant time loss when debugging failed training runs due to fragmented tooling and lack of integrated diagnostics.
- Public claimTeams spend as much time maintaining their monitoring and orchestration stack as actually using it.
- Public claim
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 · Cluster agentSmall read-only in-cluster collectorNeeds to read Kubernetes events, node logs and GPU metrics from the customer's cluster and send them out.
- This app · LLM providerHosted large language model API with tool callingThe agent diagnoses failures and calls operations as tools.
RisksAI inference
- TechnicalA wrong automated action could kill a multi-day training run or spread across a cluster. Start read-only, require approvals, and keep every action reversible and logged.
- AIThe agent may state a wrong root cause with confidence. Each diagnosis must cite its evidence and be measured against failures that people have already diagnosed.
- CommercialTeams already run Prometheus and Grafana and may see this as one more tool. The material shows early signs of interest only (26 points, 7 comments on Hacker News).
- DataCustomers may refuse to send node logs and job metadata outside their network. A self-hosted collector and clear data limits will be needed.
- PlatformClusters differ in schedulers, GPU vendors and monitoring setups, so integrations could take longer than a first version allows.
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.