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LLMs charge by the token, not the request, now an unchecked prompt length can quietly erode gross margins. System prompts, long outputs, hidden triggers, and prompt drift all contribute to rising costs. Without visibility into token usage and cost per feature, teams risk margin leakage at scale. This post unpacks where teams get trapped, the metrics that matter, and how disciplined prompt management turns token economics into a competitive advantage.

AI costs aren’t just a finance problem—they shape how you build, launch, and scale features. When visibility is missing, teams operate on guesswork and discover issues only when invoices arrive. But when visibility is built in from the start, engineers, PMs, and finance work from the same source of truth. The result? Smarter roadmaps, lower waste, and more predictable margins. This post explores the competitive advantage of making visibility the default in your AI workflow.

AI features don’t behave like infrastructure. Every interaction is a cost event — and most teams can’t see where those costs come from. In this post, we unpack the blind spots that make AI spend unpredictable, why visibility needs to move upstream into product and engineering, and how FinOps for AI turns hidden spend into decisions you can actually act on.

AI is changing the economics of software. Traditional FinOps practices, built for predictable cloud infrastructure, fall short when every prompt, embedding, or vector search carries unpredictable, usage-based costs. In this post, we break down what FinOps for AI means, why it matters now, and how teams can bring visibility, predictability, and control to the hidden costs of intelligence inside their products.

AI features don’t just cost you to build, they cost you every time they’re used. This post unpacks where those costs hide, why traditional tracking falls short, and how to stop the silent budget drain.

The FinOps layer for AI teams — real-time visibility into tokens, models, and GPUs so you can scale without burning margin.

A Guide to LLM Governance Using Revenium and MuleSoft: Enterprises today face a critical challenge: enabling developers to harness the power of OpenAI's APIs while maintaining security, governance, and cost control. In this post, we'll explore how combining Revenium and MuleSoft creates a robust framework for managing, monitoring, and governing OpenAI API usage across your organization

The “AI economy” has changed how businesses leverage data to develop new products; requiring new observability and monetization capabilities.

For SaaS applications integrating OpenAI functionality, metering usage and offering usage-based pricing are important for determining a marketable pricing schema and understanding total solution costs. Revenium simplifies this process, empowering SaaS vendors to scale solutions with robust usage metering and flexible billing while saving development costs and time.