Opportunities › Self-hosted AI agent orchestration platform › Proposed app
Stablehand
Run durable AI agents on your own servers, with a managed-service feel
- Score
- 48
- confidence 56
- Evidence
- 20
- items on the page
- Rivals
- 3
- named in sources
- First version
- 45 d
- estimate
- Type
- Desktop and mobile
- PWA: on computers and phones alike
- Market since
- 2025
- New market · AI estimate
ScreensConcept
Desktop and mobile: Teams configure agents and set budgets at desks, but approving risky actions must work on phones during daily operations. Sample screens on a computer and a phone, in the proposed brand. Their numbers are sample data.
Agents
1 needs attention12 running
About this appAI proposal
Stablehand is a self-hosted platform for running AI agents. You deploy it on your own cloud or hardware, and it does the work a managed service would: starting agent sessions, keeping them alive, retrying failed steps, and recording what happened.
It is for software teams and enterprises that want agents in production but cannot or do not want to send data to a vendor-hosted service. Your keys, logs and network rules stay with you.
Unlike hosted agent APIs, you choose the model provider per agent, can switch providers without rewriting your runtime, and see costs in your own infrastructure bill. A small PWA console lets you check on agents from any device.
- Tagline
- Your agents, your servers, kept running.
- Voice
- SteadyPlainPractical
- Why this name
- A stablehand looks after animals day and night so the owner does not have to. It suggests care, reliability and a home you own.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Durable sessions
Agent runs survive restarts and deploys. Each step is saved, so a crashed worker resumes where it stopped instead of starting over.
No member yet
Bring any model provider
Set the model per agent and switch providers in config. No lock-in to one vendor's agent format.
No member yet
Runs on your infrastructure
One container set for Docker or Kubernetes. Data, logs and secrets stay inside your network.
No member yet
Run timeline and replay
See every model call, tool call and retry in order. Replay a failed run with the same inputs to find the cause.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#2F5D50Forest · primary#F6F1E7Oat · surface#1F2A24Charcoal Green · text#D9822BAmber · accent#3B6EA5Slate Blue · data
Typography
Aa
Stablehand
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Your agents, your servers, kept running.
PricingAI proposal
The material gives no competitor prices. It only suggests support, enterprise features or a hosted tier as ways to earn. These tiers are a starting point to test.
Community
Free
- Open-source core, self-hosted
- Durable sessions and run timeline
- Community support
Team
$99 a month
- Approval inbox with push alerts
- Budgets and guardrails
- Audit export
- Email support
Business
$399 a month
- Single sign-on and role-based access
- Priority support
- Upgrade and deployment guidance
Enterprise
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 the open-source repository, documentation, content and direct sales. Runs are internal, so there is no built-in sharing or invite loop.
- Do they become customers?
- Developers who try the free core could become paying teams. Test how many installs turn into Team plans once approvals and budgets are needed.
- First customers
- Platform or backend engineering leads at 50 to 500 person software companies in regulated fields, such as finance or health, who are running agents in production.
- Where to reach them
- GitHub and open-source forums: Publish the core, a deployment guide and migration notes from hosted agent APIs. Answer issues openly.
- Developer communities: Write posts on durable agents and data control in AI engineering groups and newsletters.
- Direct outreach: Contact engineering leads in regulated companies and offer a guided setup in their own cloud.
- First milestone
- Five teams in one regulated vertical pay for Team or Business plans after running Stablehand in production for 30 days.
Why people already pay for this
- VerifiedGitHub repository gained 2898 stars in 457 days
- Public claimTeams building AI agents seek managed agent platforms to handle orchestration and reliability.
- Public claimExisting managed agent solutions are vendor-locked cloud services, creating demand for self-hosted alternatives.
- AI inferenceThe repository has significant adoption and community interest.
- Verified
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 · Durable job enginePostgres-backed workflow queueSaves each agent step so runs resume after a crash, with no extra database to run.
- This app · Sandboxed tool executionContainer sandbox for agent toolsLets agents run code and tools with limited access to the host.
RisksAI inference
- CommercialOpen-source projects already draw stars here. Teams may pick a free alternative, and paying for support or approvals is unproven.
- TechnicalDurable execution, sandboxing and retries are hard to get right. Agent steps with side effects can repeat if resumed badly.
- PlatformModel providers change their agent and tool formats often, so adapters need constant upkeep.
- CommercialSelf-hosted installs are hard to support. Setup on varied infrastructure may take more time than a small team has.
- RegulatoryEnterprise buyers may ask for security reviews or certifications before trusting a new platform with sensitive data.
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.