Fragmented billing
Invoices arrive from several model providers and cloud accounts, each with different units, granularity, and timing. Reconciling them into one number is manual work.
In development · Early access list now open
Know where every AI dollar goes.
Meterlane brings AI usage and cost data from every model, application, and team into one view, so engineering and finance can see what drives spending and decide what to optimize.
- AI startups
- SaaS companies
- Engineering managers
- Finance teams
Product
One place to see AI spend, from model to customer.
A look at the experience we're building. Every number below is sample data.
Meterlane · Demo workspace
Total AI spend
$48,210
▲ 18.4% vs prior period
Model requests
12.4M
▲ 9.1%
Cost per 1K requests
$3.89
▲ 8.5%
Monthly budget used
72%
Daily spend
Spend by model
Spend by project
Cost to serve by customer
| Customer | AI cost | AI cost / plan revenue |
|---|---|---|
| Customer 1042 | $6,820 | 41% · high |
| Customer 0877 | $4,110 | 18% |
| Customer 1310 | $3,540 | 22% |
| Customer 0519 | $2,960 | 12% |
| Customer 1188 | $2,275 | 9% |
Budget alerts
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Support Assistant crossed 100% of its $9,000 monthly budget Critical
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Document Search is at 85% with 9 days left Warning
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Code Review Bot is on track at 54% On track
Why did spend rise 18% this period?
Most of the increase came from Support Assistant. On day 22, a prompt template change roughly doubled average input length, and the project moved traffic from the small model to the large model. Customer 1042 accounted for 38% of that project's requests. Consider reviewing the template change and whether the large model is needed for all request types.
The problem
AI features ship fast. Understanding their cost doesn't.
As teams add AI across products, spending spreads across models, applications, and teams, and nobody has the full picture.
Unexpected usage spikes
A prompt change, a retry loop, or one heavy user can multiply spend overnight. Too often, the first sign is the end-of-month bill.
Unclear cost to serve
Without per-customer attribution, it is hard to know which accounts are profitable, how to price AI features, or where margins are shrinking.
Features
Built for the people who ship AI and the people who pay for it.
Planned capabilities for the first release. Scope may change as we learn from early access teams.
Usage and cost tracking
Collect tokens, requests, and cost in one ledger, normalized across models so figures are comparable day to day.
Project and customer allocation
Tag usage by project, feature, team, or customer, then see what each one costs and how that changes over time.
Budget thresholds and alerts
Set monthly limits per project or team and get notified at the thresholds you choose, before the invoice does it for you.
Model cost comparisons
Compare what the same workload costs on different models using your own usage history, not list prices alone.
AI-generated explanations of spending changes
When spending moves, get a plain-language summary of what changed, where, and which projects or customers drove it. Summaries point you to the data behind them so you can check the reasoning.
How it works
From raw usage records to informed decisions in four steps.
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1
Connect usage records
Send usage events from your application, or upload exported usage records from your providers.
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2
Organize spending
Map usage to projects, teams, and customers with tags and rules you control.
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3
Investigate trends
Break spend down by model, project, or customer and find what changed and when.
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4
Review recommendations
Read suggested optimizations and decide what to act on. Meterlane doesn't change your systems on its own.
Architecture
Planned AWS architecture
Here is how we plan to build Meterlane on AWS. Planned infrastructure: not yet in production
Source
Your applications
Usage events and exported records
Ingest
Amazon API Gateway + AWS Lambda
Receive, validate, and normalize usage events
Retain
Amazon S3
Keep historical usage records
Analyze
Amazon Athena
Query spend by model, project, and customer
Explain
Amazon Bedrock
Generate plain-language summaries of spending changes
This design may change during development. AWS service names describe the planned technology stack only; Meterlane is not affiliated with, endorsed by, or a partner of Amazon Web Services.
About
Our mission: make AI operating costs understandable.
AI is becoming a core operating expense for software companies, yet most teams can't answer simple questions about it: what did this feature cost last month, which customers are expensive to serve, and why did the bill jump?
We're building Meterlane so engineers and finance teams can work from the same numbers, explain changes in plain language, and make cost decisions with confidence rather than guesswork.
Founding team
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[Founder name][Title] · [Short background]
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[Co-founder name][Title] · [Short background]
Company details
- Legal entity
- [Company legal name]
- Registered address
- [Registered address]
- Company number
- [Registration number]
- Contact
- [contact email]
FAQ
Questions, answered.
What data sources will Meterlane support?
We're designing Meterlane around two inputs: usage events sent from your application through an API, and usage or billing records exported from your model providers. No provider integrations are live today. We'll decide which sources to support first based on what early access teams use, and we'll publish the list before launch.
What does onboarding look like?
For early access, we'll work with each team directly. We'll start with a short call about how you use AI today, then help you send a sample of usage records and set up projects and customer tags. The aim is to show you a useful breakdown of your own spending as quickly as possible.
Is Meterlane available today?
Not yet. Meterlane is in development. The dashboard on this page is an illustrative preview with sample data, and the AWS architecture is planned, not in production. We'll contact the early access list when there is something ready to try.
How does early access work?
Fill in the form below. We'll review requests and contact teams whose use cases match what we're building first. Early access partners get hands-on onboarding and a direct say in what we build. Joining the list doesn't commit you to anything.
Who is Meterlane for?
AI startups and SaaS companies running AI features in production, especially where engineering managers and finance teams both need to understand the same spend.
Early access
Request early access
Tell us a little about your team and how you use AI. We'll be in touch when we're ready for your use case.
- Hands-on onboarding with the team building it
- Direct input into what we build first
- No commitment and no payment details