Blog

Credit-based pricing is increasingly popular among AI products. It helps mitigate high upfront costs and manage vendor and customer expenses.

Pricing AI products poses unique challenges due to the high cost of goods and increased automation. We see AI companies experiment with multiple models, like usage-based pricing, adopting credits, and prepaid billing.

Explore the top challenges of adopting usage-based pricing and uncover insights on billing complexities like entitlements, customer communication, data-driven sales strategies, and cost management.

Our collaborations have painted a clear picture of what leading companies seek to meter in 2024.

OpenMeter standardizes usage metering to enable AI monetization.

Consider the two aspects of metering: 1. Usage volume and 2. Use cases building on aggregated usage.

Feature announcement of subject display names, webhook usage sync filters, and new collectors for Postgres and ClickHouse

The tight feedback loop between customers interacting with your product and the usage reflected on their billing and usage dashboard is essential to control their spending.

OpenMeter's FinOps unit economics solution enables companies to attribute shared costs to customers and internal teams.

Meter container CPU time and pod execution time on Kubernetes with OpenMeter.

With OpenMeter's native Stripe integration, you can easily implement usage-based pricing for AI, API, and infrastructure consumptions like logs, Kubernetes, S3, and databases.

Unit economics and rate cards can ensure that engineering efforts align with strategic business objectives and drive sustainable growth.

Ingest usage data to OpenMeter from any cloud infrastructure and vendor with our Benthos plugins.

Announcing production-ready Kubernetes Helm Chart for OpenMeter with a bring-your-own-databases options.

Differences between tiered pricing model, volume-based pricing model and tiered pricing strategy and how they work.

FinOps Unit Economics and Rate Cards help to attribute shared costs and create a contract among teams providing services to each other.

Key API Monetiztion strategies, covering models like Subscription, Usage Based Pricing, and Pay-Per-Transaction.

Essential components of a great customer usage dashboard and exploring what leading companies are doing in this domain.

1st Developer Tool of the week and 2nd Product of the day

Businesses with tiered or usage-based pricing, especially in the AI and DevOps sectors, need to shield their profit margins from undue overuse.

Our journey from Kafka Connect to building a robust and scalable Kafka to ClickHouse sink by implementing a Kafka Consumer worker in Go.

Generating sample data with Benthos to test stream processing with real-world usage data

Cost Per Transaction and Rate Cards helps Finance and Engineering to speak the same language and leads to higher cloud savings, and better outcomes.

Metering LLMs token usage with message tokenization, OpenAI streaming APIs, and Vercel AI SDK for Next.js.

Explore how OpenMeter utilizes ClickHouse and Kafka Connect for robust usage metering to power accurate analytics and billing.

Announcing the Launch of OpenMeter Cloud Beta: Real-time, Scalable Metering for Engineers

Implementing robust Stripe usage reporting that can withstand rate limits and errors for accurate usage-based billing.

Usage-based pricing can increase NRR through organic account growth and reduced churn.

Turning application logs into usage with Vector for usage-based billing. Useful in serverless environments like AWS lambda where integrating SDKs is challenging

Guide to avoid overcharging customers with usage-based billing by double counting your usage data. By implementing an idempotent stream processing with Kafka.

Deep dive into metering OpenAI API usage and ChatGPT token consumption for chargeback and billing use-cases.

Guide to implementing usage-based pricing with Stripe. Developer-friendly tutorial to metered pricing and Stripe usage-based billing (UBP).

Comparing lifecycle event and heartbeat driven metering to measure CPU Hours of containers, pods, and serverless on second granularity.

Understanding Auditable and Operational Data. Choosing between Event-driven Metering vs. Time-Series Metrics.