Opportunities › Browser automation orchestration for AI agents › Proposed app
Runlane
Run browser agents in production with queues, retries and replay
- Score
- 70
- confidence 62
- Evidence
- 9
- items on the page
- Rivals
- 2
- named in sources
- First version
- 45 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2024
- New market · AI estimate
ScreensConcept
Desktop app: Engineers manage agent deployments, monitor queues, replay runs, and review cost reports—intensive monitoring and debugging work done at a desk. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Runs
Queue healthy12 running
About this appAI proposal
Runlane is the production layer for browser-use agents. You bring the agent and the task definition; Runlane runs it in isolated browser sessions, queues the work, retries failed steps, keeps login state and records every run.
It is for engineering teams that have a working prototype and do not want to spend months building their own orchestration, tool management and context handling before they can ship.
Every run is stored with its actions, screenshots, model calls and cost, so you can see what a task cost and why it failed. Model routing is yours to choose, and the design keeps room for running it on your own infrastructure later.
- Tagline
- Your browser agents, ready for production.
- Voice
- PlainPreciseCalm
- Why this name
- Runs travel down lanes: each task gets its own lane, its own browser and its own record. Two plain words joined, easy to say and spell.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Task API with structured results
Submit an agent task by API and get back typed results, status and a run link. Webhooks tell your system when a run finishes.
No member yet
Queue with concurrency limits
Set how many browsers run at once per account or project, and schedule tasks to start later or on a repeat.
No member yet
Isolated session pool
Each run gets a clean browser. Save login profiles and cookies so agents can resume signed-in work without logging in again.
No member yet
Step retries and failure types
Timeouts and retries apply per step. Failures are sorted into causes such as page change, blocked, timeout or model error.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#1F5C4APine · primary#F6F3ECPaper · surface#1F2A24Ink · text#E0872BAmber · accent#3C6E91Slate blue · data#B3402EBrick · error status
Typography
Aa
Runlane
PricingAI proposal
The material gives no competitor prices: LiteLLM lists a free plan and paid plans on request. These tiers are a starting point to test, not derived from a market price.
Free
Free
- Small monthly run allowance
- 2 concurrent browsers
- 7-day run history
Team
$199 a month
- Larger run allowance, then pay per run
- 20 concurrent browsers
- 30-day run replay
- Saved login profiles
- Cost and failure reports
Scale
Contact us
- Custom concurrency
- Longer retention
- On-premises option when available
- Priority support
Would you pay Team · $199 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
$190,000
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. Reached through developer content, open-source integrations and direct outreach. Run replay links are shared inside teams, but the viewers are already customers' colleagues, not new buyers.
- Do they become customers?
- Nothing in the product's use reaches outside buyers. Test whether engineers who see a shared replay link on a team then sign up their own project.
- First customers
- Small engineering teams of 2 to 10 people that already have a working browser-use agent prototype and are moving it to production.
- Where to reach them
- Hacker News and dev forums: Post a technical write-up on failure types in browser agents, with a free tier link.
- Open-source browser-agent communities: Offer an adapter for popular open-source browser-use tools and answer production questions.
- Direct outreach to founders: Message teams that publicly show browser agents and offer to onboard them for free.
- First milestone
- Five teams run real weekly workloads on it, and at least three pay for the Team tier within 60 days.
Why people already pay for this
- Public claimGobii has paying customers for their browser-use agent deployment platform
- Public claimThere is demand for browser-use agents that navigate and execute on the real web
- Public claimEngineers building agentic systems see limitations in existing agent approaches
- Public claimStrong early traction is driving team expansion in this market
- 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 · Browser runtimeContainerised headless browsers on a worker poolIsolated, disposable browser sessions cannot run inside Vercel functions and need long-lived workers.
- This app · Job queuePostgres-backed or Redis queueConcurrency limits, retries, scheduling and timeouts need a durable queue outside request handlers.
RisksAI inference
- CommercialWell-funded hosted browser infrastructure and open-source browser-agent tools already exist; teams may judge a separate orchestration layer unnecessary or build it themselves.
- TechnicalRunning many isolated browsers reliably is costly and hard. Session leaks, memory use and crashes could erode margins and trust.
- PlatformTarget sites may block automated browsers, and customers' use may break site terms, so success rates may vary beyond our control.
- AIAgent behaviour changes with model and site updates. Retries and failure labels may not make results dependable enough for production.
- DataRecordings and saved logins hold sensitive customer data, so storage, encryption and retention must be right early, before on-premises is available.
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.