OpenMeter is an open-source platform designed to help AI, API, and DevOps companies collect, aggregate, and act on usage data in real time. It solves the challenge of tracking granular usage events—like API calls, Kubernetes pod execution, or AI model invocations—and converting them into actionable billing and cost insights. Built for scalability, OpenMeter handles millions of events per day using Kafka and ClickHouse, making it ideal for companies moving beyond flat subscription models to usage-based pricing. Whether you’re a startup building an AI API or an enterprise managing microservices, OpenMeter provides the infrastructure to meter usage accurately and enforce limits without relying on third-party SaaS tools.
What You Get
- Real-time usage metering - Collect and aggregate millions of events per day from APIs, Kubernetes pods, logs, and other sources with low latency using Kafka as the event ingestion layer.
- Usage-based billing integration - Export metered usage data to Stripe for automated, pay-as-you-go billing with pre-built integrations in Go and Node.js.
- Kubernetes pod execution time metering - Automatically track resource usage of Kubernetes pods by ingesting events from the Kubernetes API and aggregating execution duration per customer or service.
- Log-based metering - Parse application logs to extract usage metrics (e.g., number of AI inferences, API calls) and feed them into the billing pipeline without modifying application code.
- REST API and SDKs - Expose metering data via a Swagger-defined REST API with official client SDKs for JavaScript, Python, and Go to integrate metering into existing applications.
- Self-hosted and cloud options - Deploy OpenMeter on-premises via Docker Compose or Helm, or use the managed cloud version with a free tier for quick evaluation.
Common Use Cases
- Building an AI API with usage-based pricing - A startup offers a text-to-speech API and needs to bill customers based on the number of characters processed; OpenMeter ingests each API call, aggregates usage per customer, and exports to Stripe for automated invoicing.
- Tracking microservice costs in Kubernetes - A DevOps team wants to allocate cloud costs per team based on actual pod usage; OpenMeter collects Kubernetes event data to measure CPU/memory time and generates reports for FinOps dashboards.
- Problem: Manual usage tracking leads to billing errors → Solution: Automated event ingestion - Before OpenMeter, teams exported logs and manually calculated usage; now, they configure log collectors to auto-ingest events into Kafka → ClickHouse for real-time aggregation and Stripe billing.
- Team: AI product teams managing usage limits - An AI company enforces daily API call quotas per customer; OpenMeter’s real-time analytics and limit enforcement APIs allow them to block usage when thresholds are reached, reducing revenue leakage.
Under The Hood
OpenMeter is a comprehensive platform designed for real-time usage metering and billing, built with a modular architecture that supports multi-language client SDKs and integrates seamlessly with event streaming platforms. It emphasizes scalable infrastructure and spec-driven development to enable flexible billing workflows.
Architecture
OpenMeter adopts a modular, service-oriented architecture focused on decoupling core components for extensibility and maintainability.
- The system is structured around distinct services for API handling, client SDKs, and infrastructure support
- Clear separation of concerns is maintained across backend and frontend modules
- Emphasis on scalable design with support for real-time event processing and billing integration
Tech Stack
OpenMeter leverages a diverse tech stack to support cross-platform development and robust infrastructure.
- Built primarily in Go with Nix for reproducible builds and Docker for containerization
- Integrates TypeSpec and OpenAPI for API definition and client generation across languages
- Uses TypeScript/JavaScript tooling for SDKs and Vitest for testing in client environments
- Employs Confluent Kafka, Alpine Linux, and various Go modules for backend services and data flow
Code Quality
Code quality in OpenMeter reflects a mature approach to API development with strong testing and linting practices.
- Comprehensive test coverage is present across multiple client SDKs and backend services
- Error handling follows standard patterns, though some inconsistency in propagation exists
- Code style and conventions are reasonably consistent with clear module organization
- Linting and automated testing are well-integrated into the development workflow
What Makes It Unique
OpenMeter distinguishes itself through its unified approach to usage metering and billing, with a focus on multi-language client support and real-time integration.
- Offers spec-driven API versioning and client generation that supports multiple programming languages
- Deep integration with Stripe and other billing providers enables flexible monetization workflows
- Combines event streaming capabilities with billing logic in a single cohesive platform