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Building usage-based billing in-house? Think it’s easy?
S3 for data, a cron job to aggregate, then send to payments. Done. Right?
Wrong.
You're thinking like an engineer, not a billing expert. Billing gets complex fast.
Remember Google Gemini? One dev's bill jumped from $0.56 to $800 in minutes! Billing failed.
Here's where in-house projects often stumble:
*Event Volume Chaos *
Real-time APIs can generate hundreds of thousands of events per second. Miss a single burst, and you’re undercounting. Process it twice, and you’re overbilling. Without robust deduplication and back pressure management, your metering pipeline can quickly become a liability.
*Queue Congestion *
If your message queues (Kafka, RabbitMQ) get clogged, your “real-time” data isn’t real-time anymore. This leads to delayed or duplicate events, completely breaking billing accuracy. And trust me, when your CFO asks why last month’s bill is 10x normal, “Kafka lag” isn’t going to cut it.
Precision and Rounding Errors
Even small rounding errors or timestamp mismatches can add up to significant revenue leakage or overcharges. You’re not just counting tokens or milliseconds – you’re managing financial transactions, and the stakes are high.
Timezones, Calendars, and Billing Cycles
Do you bill every 30 days (calendar) or every month (anniversary)? What about leap days and daylight savings? A missed timezone conversion can lead to major discrepancies.
*Pricing Version Control *
Not all pricing changes should affect existing customers. You need to track distinct pricing versions to ensure long-term customers are billed correctly based on their original agreements.
Billing is interconnected and unforgiving. Finance, support, product, legal – all touched. Breaks badly.
Before you build, consider the scale, precision, and complexity. It's tougher than it looks.
Here is our Github:
S3 for data, a cron job to aggregate, then send to payments. Done. Right?
Wrong.
You're thinking like an engineer, not a billing expert. Billing gets complex fast.
Remember Google Gemini? One dev's bill jumped from $0.56 to $800 in minutes! Billing failed.
Here's where in-house projects often stumble:
*Event Volume Chaos *
Real-time APIs can generate hundreds of thousands of events per second. Miss a single burst, and you’re undercounting. Process it twice, and you’re overbilling. Without robust deduplication and back pressure management, your metering pipeline can quickly become a liability.
*Queue Congestion *
If your message queues (Kafka, RabbitMQ) get clogged, your “real-time” data isn’t real-time anymore. This leads to delayed or duplicate events, completely breaking billing accuracy. And trust me, when your CFO asks why last month’s bill is 10x normal, “Kafka lag” isn’t going to cut it.
Precision and Rounding Errors
Even small rounding errors or timestamp mismatches can add up to significant revenue leakage or overcharges. You’re not just counting tokens or milliseconds – you’re managing financial transactions, and the stakes are high.
Timezones, Calendars, and Billing Cycles
Do you bill every 30 days (calendar) or every month (anniversary)? What about leap days and daylight savings? A missed timezone conversion can lead to major discrepancies.
*Pricing Version Control *
Not all pricing changes should affect existing customers. You need to track distinct pricing versions to ensure long-term customers are billed correctly based on their original agreements.
Billing is interconnected and unforgiving. Finance, support, product, legal – all touched. Breaks badly.
Before you build, consider the scale, precision, and complexity. It's tougher than it looks.
Here is our Github: