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The AI Cost 'Black Box' - And How CloudZero Provides Clarity Into Spend

AI adoption continues to explode, and so do their costs. By mid-2025, enterprise LLM spend had already hit $8.4 billion, more than double the year before. And in a major shift, Anthropic recently overtook OpenAI as the enterprise leader. Their Claude models are now core tools for companies adding generative AI technology into their products and workflows. CloudZero recently announced we are the first cloud cost platform to integrate with Anthropic.

How You Can Use Network as a Service (NaaS) to Future-Proof Your Network

Support global growth, AI integration, and complex use cases with a scalable, programmable connectivity layer. In a recent blog, we explored exactly what Network as a Service (NaaS) is and how it has redefined connectivity for enterprises. But in this blog, we take the next step of exploring how adopting NaaS future-proofs your network.

Best Practices for SQL Formatting: Write Clear and Consistent Code

Inconsistent SQL formatting is a silent productivity killer. The database will execute it, but for developers, poorly structured queries lead to slower reviews, harder debugging, and errors that slip through unnoticed. Over time, this lack of consistency compounds into costly technical debt. This guide shows how to format SQL code so it remains clear, consistent, and easy to maintain.

Mastering the User Off-Boarding Process

When someone leaves your organisation — whether they resign, retire, or are let go — it’s easy to think the hard work is over. But the moment an employee’s last day arrives, a new risk window opens. If their access isn’t revoked properly or their data isn’t captured, organisations face security breaches, data loss, compliance issues, and rising costs. This is why a well-designed user off-boarding process is just as important as onboarding.

AI's False Efficiency Curve: How To Save And Protect Your Margins

The popular narrative around AI economics is changing. At one time, Moore’s Law conditioned us to expect that smarter, faster computing would steadily get cheaper. When it comes to AI, that expectation holds true at the unit level. Per-token costs are indeed declining. But the number of tokens consumed per task is growing exponentially, making total costs spike. The tension here is important: on paper, inference is getting cheaper.