Opportunities › Runtime governance for enterprise AI deployment › Proposed app
Tollgate Ledger
Track, cap and audit what each AI workload spends and does
- Score
- 34
- confidence 52
- Evidence
- 4
- items on the page
- Rivals
- 0
- named in sources
- First version
- 45 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2023
- Emerging market · AI estimate
ScreensConcept
Desktop app: Finance and compliance teams monitor spend dashboards, set budgets, write policy rules, and export audit evidence in sustained work sessions with many fields and controls. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Spend this month
3 workloads near cap$412,860
About this appAI proposal
Tollgate Ledger is a control plane for finance and compliance teams at large enterprises running AI systems. It sits between applications and model providers, records each call, and ties cost to a team, project and cost centre. Budgets and policies apply while the workload runs, not after the invoice arrives.
Finance sees spend by owner, with forecasts and alerts before a cap is hit. Compliance sees which models and data classes each workload used, which policy applied, and who approved it. Both teams work from one record instead of reconciling separate spreadsheets.
It is aimed at organisations with thousands of staff where many teams deploy AI independently. This page is a proposal. The market evidence is public claims from one discussion thread and is not verified.
- Tagline
- Know what every AI workload costs and whether it followed the rules.
- Voice
- PreciseCalmAccountable
- Why this name
- A tollgate charges and checks at the point of passage, and a ledger is the record finance and auditors trust. Together they describe runtime control plus a record.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Spend by owner
Every AI call is tagged to a team, project and cost centre, so finance can allocate and charge back without manual digging.
No member yet
Runtime budget caps
Set soft and hard limits per workload. Warn owners at a threshold, then throttle or block calls when the cap is reached.
No member yet
Policy gates
Rules for allowed models, regions and data classes are checked on each call. Violations are blocked or flagged for review.
No member yet
Audit trail export
A tamper-evident log of calls, policy versions and approvals, exported as a report for internal audit or regulator requests.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#14352FDeep Pine · primary#F6F3ECPaper · surface#1F2A27Ink · text#C48A1DBrass · accent#B3402FSignal Red · alerts#7FA38FSage · data
Typography
Aa
Tollgate Ledger
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
PricingAI proposal
The material gives no competitor prices or willingness to pay. These tiers are a starting point to test, scaled by number of monitored workloads as the monetization inference suggests.
Pilot
$2500 a month
- Up to 10 monitored workloads
- Spend by owner and budget caps
- Basic policy gates
- 90-day audit log
Enterprise
$9000 a month
- Up to 75 workloads
- Forecasts and anomaly alerts
- Approval workflow
- Audit export, 3-year retention
- SSO and role-based views
Global
Contact us
- Unlimited workloads
- Custom policy packs
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. Sold directly to finance and compliance leaders. Reports and audit exports go to internal auditors and executives, but they are evidence, not a viral surface.
- Do they become customers?
- Report recipients are inside the same customer, so they bring no new customers. Test whether direct outreach and content on AI cost and compliance win paid pilots.
- First customers
- Finance operations or FinOps leads, with a compliance partner, at enterprises of 50,000+ employees already running several AI workloads in production.
- Where to reach them
- Direct outreach: Contact FinOps and risk leads at very large firms, offering a free spend-and-policy gap review of their AI usage.
- FinOps communities: Share practical guides on AI cost allocation and chargeback in FinOps forums and meetups.
- Hacker News and technical posts: Publish write-ups on runtime governance to reach platform teams who influence finance and compliance buyers.
- First milestone
- Three paid pilots at enterprises of 50,000+ employees, each with finance and compliance both using the product within 60 days.
Why people already pay for this
- Public claimThere are paying customers in production at enterprises with 5,000-50,000 employees and 50,000-100,000 employees.
- Public claimEnterprises with 50,000+ employees have more urgent need for runtime governance control planes than smaller companies.
- Public claimFinOps and compliance are major drivers for adoption of control planes for AI.
- Public claimGartner has begun tracking AI middleware as a new product category related to runtime governance.
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 · Gateway proxyLow-latency API proxy for model callsRuntime caps and policy checks must sit in the call path without slowing applications.
- This app · Event storeAppend-only log with time-series rollupsMillions of call records need cheap storage, fast spend queries and tamper-evident audit history.
RisksAI inference
- TechnicalA proxy in the call path adds latency and becomes a point of failure. Enterprises will need fail-open or fail-closed options and strong uptime.
- CommercialEvidence is unverified public claims. Large enterprises have long security reviews and may favour cloud providers' built-in tools or existing vendors.
- RegulatoryAudit and compliance reports must map to real regulatory requirements, which vary by country and sector. Wrong mapping could mislead buyers.
- DataLogging AI calls may capture sensitive prompts. Storing metadata only by default, with strict retention controls, is needed to pass reviews.
- PlatformModel providers may change APIs or add native budget and policy controls, shrinking the gap this product fills.
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.