OpenMeter

Stripe Billing forces high-volume API and AI workloads into proprietary meters that update with delay and charge fees per transaction. OpenMeter ingests CloudEvents directly into an open event pipeline backed by ClickHouse and Kafka, processing millions of usage records in real time without passing raw telemetry to an external billing provider. Teams retain operational control over usage data while keeping payment collection decoupled.

Why OpenMeter instead of Stripe Billing

Stripe Billing is designed around subscriptions and card processing. When teams introduce granular metrics like LLM token counts or API calls, pushing raw events into Stripe hits rate limits, processing delays, and billing lock-in. OpenMeter decouples usage tracking from payment collection. It serves as an operational metering layer that ingests events, computes aggregations, and enforces real-time quotas before dispatching finalized line items to payment processors. Unlike Stripe Billing, OpenMeter does not process credit cards directly; it requires an external provider to collect payments.

From an operations perspective, the choice is infrastructure ownership versus managed convenience. Stripe Billing eliminates database maintenance but keeps data locked in a proprietary cloud. OpenMeter runs as a Go service with Kafka for ingestion, ClickHouse for high-throughput columnar storage, and PostgreSQL for metadata and subscriptions. That stack delivers sub-second query latency for customer portals and entitlement gating, but it requires maintaining distributed data stores. Teams often keep Stripe for payments while routing usage ingestion through OpenMeter to avoid Stripe rate limits and high-volume billing fees.

What it does well

• Real-time entitlement enforcement: Checks customer quotas and feature flags with millisecond latency, letting ops leads block overages or enforce grace periods before expensive compute runs.
• Standardized CloudEvents ingestion: Ingests usage payloads over HTTP using the CloudEvents format, letting teams pipe telemetry from proxies and API gateways without custom schema adapters.
• Dedicated ClickHouse and Kafka pipeline: Buffers traffic surges through Kafka and writes deduplicated records into ClickHouse, preventing database bottlenecks under heavy event volumes.
• Multi-metric pricing calculations: Computes tiered, graduated, and flat-fee usage charges alongside promotional and prepaid credit burn-downs.