Architect real-time usage-based billing for a PLG company

WHAT THIS TESTS: This question evaluates whether you can architect revenue-critical infrastructure that bridges high-velocity product telemetry with financial accuracy. Interviewers want to see that you understand the difference between observability metrics and billable events, and that you know how to build idempotent, auditable pipelines that scale to millions of micro-events per day without losing customer trust.

A GOOD ANSWER COVERS: First, an event ingestion layer that accepts billable events with strict schema validation, customer attribution, and timestamping. Second, a stream processing tier using something like Kafka or Flink to aggregate raw events into billable units in near real-time, handling different aggregation strategies such as sum, unique count, or latest value. Third, a pricing engine decoupled from usage collection that maps aggregated units to pricing rules including tiers, prepaid credits, and entitlements without requiring code changes. Fourth, a user-facing dashboard that reads from pre-aggregated usage tables to show current spend without hitting the raw event stream. Fifth, a reconciliation and correction pipeline that handles late events, duplicates, and retries so invoices remain accurate.

COMMON WRONG ANSWERS: Proposing to query a production OLTP database directly for billing counts. Suggesting nightly batch jobs when the prompt asks for near real-time. Ignoring idempotency and assuming events arrive exactly once. Hardcoding pricing logic into the aggregation layer so every pricing change requires a deploy. Overlooking the need for audit trails and double-entry reconciliation between usage and revenue.

LIKELY FOLLOW-UPS: How would you handle a pricing change mid-month for existing customers? What happens if a customer disputes a bill and you need to reconstruct their usage? How do you prevent a surge in API calls from overwhelming the billing pipeline? How would you model prepaid credits that draw down in real time?

ONE CONCRETE EXAMPLE: Imagine a PLG company charging per GB of storage and per API call. Every upload or API request emits a validated event to a streaming bus. Flink windows aggregate API calls by customer hourly and compute storage deltas from upload and delete events. The pricing engine applies a tiered rate for API calls and a per-GB rate for storage, then writes the running totals to a read-optimized store. The dashboard polls this store to show today's spend. At month end, a reconciliation job compares the aggregated totals against a cold storage archive of raw events to catch duplicates or gaps before invoicing.

Source: flexprice.io