Opportunities › Serverless GPU inference platform › Proposed app
Pilotlight
Deploy GPU models and jobs that wake fast, scale on demand, and show every dollar
- Score
- 40
- confidence 57
- Evidence
- 8
- items on the page
- Rivals
- 5
- named in sources
- First version
- 90 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2021
- Emerging market · AI estimate
ScreensConcept
Desktop app: ML engineers configure scaling rules, monitor costs, deploy fine-tuning jobs, and manage endpoints from a technical workstation. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Endpoints
Spend this month$184.20
About this appAI proposal
Pilotlight is a serverless GPU console and runtime for teams that run LLM inference, fine-tuning and background jobs in production. Point it at a container or model, set a scaling policy, and get an endpoint. No GPU nodes to patch, no cluster to babysit.
It is built for machine learning engineers and small AI teams who find general serverless platforms lack GPUs, and GPU clouds either hands-on to run or tied to one vendor. Keep a small warm pool so requests start fast, and let the rest scale to zero.
The difference is control and clarity. Autoscaling rules are plain text you can read and edit, cost per request sits next to latency, and the same deployment spec runs on managed capacity or on GPUs you host yourself.
- Tagline
- A small flame always lit, so your GPUs are ready when requests arrive.
- Voice
- PlainCalmPrecise
- Why this name
- A pilot light stays lit so the burner starts at once. That is the warm pool idea: a little standing cost for a fast start.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Deploy from a container or model
Push an image or pick an open model, choose a GPU type, and get an HTTPS endpoint with keys and logs.
No member yet
Warm pools for fast starts
Hold a set number of ready replicas on a schedule. Cold-start time is measured and shown for every scale-up.
No member yet
Readable autoscaling rules
Scale on queue depth, concurrency or time of day. Rules are short text you can review, diff and roll back.
No member yet
Background jobs and fine-tuning
Queue batch runs and fine-tuning jobs on the same spec, with retries, time limits and checkpoints.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#D9480FEmber · primary#FFF8EECream · surface#2B1B12Char · text#0F766ETeal · accent#F2B84BAmber · data
Typography
Aa
Pilotlight
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
A small flame always lit, so your GPUs are ready when requests arrive.
PricingAI proposal
Verified: Modal lists one paid plan at $250 a month plus custom pricing; Runpod and Together AI list usage-based plans. Team and Scale undercut Modal's plan and are starting points to test. GPU time is extra.
Free
Free
- 1 endpoint
- Pay per GPU second
- Basic logs and cost view
Team
$79 a month
- Unlimited endpoints
- Warm pools and schedules
- Budget caps and alerts
- GPU time billed at usage
Scale
$199 a month
- Custom autoscaling rules
- Self-hosted GPU agent
- Priority capacity
- Audit log and roles
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. Reaches customers through content, open examples and direct sales to ML teams. Endpoints are called by the customers' own apps, and Pilotlight is not visible to those apps' end users.
- Do they become customers?
- End users of customer apps never see Pilotlight, so there is no built-in acquisition. Test whether benchmark posts and open deployment templates bring in engineers who sign up and pay.
- First customers
- Small teams of 2 to 10 engineers at seed-stage startups serving an open-weight LLM in production, who now rent GPUs by hand or pay a serverless GPU plan.
- Where to reach them
- GitHub and open model communities: Publish deployment templates and cold-start benchmarks for popular open models, with a clear way to try them.
- ML engineer forums and Discords: Answer deployment and cost questions, and offer a free migration of one endpoint.
- Direct outreach: Contact teams who post about GPU bills or scaling pain and offer a side-by-side cost comparison.
- First milestone
- Ten teams run a production endpoint for 30 days, and five pay for Team.
Why people already pay for this
- VerifiedTogether AI's pricing page lists 7 paid plans, from $0.00 to $8.99 by usage
- VerifiedRunpod's pricing page lists 9 paid plans, from $0.27 to $9.98 a month, plus prices on request for larger customers
- VerifiedModal's pricing page lists 1 paid plan, at $250 a month, plus prices on request for larger customers
- VerifiedAWS Lambda's pricing page lists 1 paid plan, at $0.20 by usage, plus a free plan
- VerifiedGitHub repository gained 1808 stars in 1061 days
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 · GPU capacityRented GPU supply from cloud or GPU providersThe product needs real GPUs to run workloads; the shared stack only hosts the console and billing.
- This app · Container orchestrationKubernetes with GPU node pools and a scalerRuns containers, warm pools and scale-to-zero, and powers the self-hosted agent.
RisksAI inference
- CommercialModal, Runpod and Together AI already serve this need. Without a clear edge in start time or price, teams may not switch.
- TechnicalFast cold starts with large model weights are hard. Image and weight caching must be good or the main promise fails.
- CommercialGPU supply costs and availability can squeeze margins. Idle warm pools cost money whether or not requests arrive.
- PlatformDepending on upstream GPU providers means their price changes, outages or quotas pass straight to customers.
- TechnicalRunning customers' arbitrary containers needs strong isolation and abuse controls, which add security work.
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.