Hey, look at this.

People love to talk about waste and spend. We’re told to reduce it, manage it, recycle it, and transform it. In an age of hyper-consumption and endless spending, we’re constantly urged to be mindful, efficient, and minimal. And in the world of cloud, that same logic applies. We're told we can streamline spending and manage costs without friction. But the reality is far more complex.

Cloud spend is often a company’s most elusive, least understood cost - and one of its largest. Part of the reason for that is that the tools we use to “manage” it are built on yesterday’s assumptions: static infrastructure, clear ownership, and the idea that artificial intelligence can simply be layered on after the fact. Today’s cloud doesn’t work that way. It’s no longer a box in the sky. It’s a dense, shape-shifting organism, interconnected, dynamic, and constantly evolving.

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Today's cloud is no longer a box in the sky. It’s a dense, shape-shifting organism, interconnected, dynamic, and constantly evolving.

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Boundaries blur. Responsibilities mutate. And costs? They flow in unpredictable ways, making value harder to trace and effort harder to measure.

The real challenge isn’t just technical, it’s cultural. In most organizations, cloud responsibility is distributed across teams, vendors, tools, and processes. Everyone touches it, but no one fully owns it. That’s how inefficiency creeps in, not through bad intentions, but through a lack of shared visibility.

We believe it's time to bring the cloud back down to earth.

We built a platform that doesn't just track spending; it explains it. Down to the line item, across teams, in real time. And with AI copilots in the loop, your FinOps and engineering teams won’t just get alerts, they’ll get answers. Fast, unapologetic, and dead-on.

For geniuses  

The cloud Is complicated, sorry

Convenience is great. It starts with skipping the hassle of plugging things in or remembering passwords, and ends with entire industries built around avoiding hard thinking. This isn’t a complaint but an observation. Convenience wins. It always does.

Take the cloud. It started as a utility where you pay for storage, pay for compute, and forget about the servers. It was all about abstraction. But over time, it turned into the beating heart of global infrastructure: always on, always available, and almost opaque. Sure, you can spin up a server in seconds, but figuring out which customer is responsible for your $14,237 S3 bill? Good luck.

Enter tagging. The idea was intuitive: label your resources, track your spend, regain control. Maybe even impress Finance with a pie chart. But tagging assumes resources are static and self-contained - an assumption that breaks down in today's cloud, where everything is shared, ephemeral, and constantly changing. Multi-tenant systems resist neat labels, and you end up with dashboards full of precision without accuracy. You get numbers, but they’re based on assumptions. Shared costs vanish into overhead, pricing models drift, and cost anomalies go unnoticed.

That’s where Pelanor decided to do something very boring and very radical: rebuild the data layer. This wasn’t a retrofit. It was a rethink. No rearranging the same tools. No hoping for cleaner tags. Just a clean start, with a system built for what the cloud truly is - not what we wish it were.

In practice, that means Pelanor maps cloud spend with real granularity, across complex multi-tenant environments. It tracks shared costs down to specific workloads, services, and customers, surfacing relationships between usage and cost that tagging alone can’t reveal. And it turns FinOps from a reporting function into something closer to strategy.

This level of visibility unlocks true unit economics: what each customer, product, team, or environment actually costs, and whether that cost makes sense. No more averages. No guesswork. Just clean, living data that remains accurate even as your architecture scales or shifts.

The modern cloud isn’t just big, it’s weird, messy, and intentionally dynamic. Which means it can’t be managed with shortcuts or surface-level fixes that assume stability. Our next-generation optimization tools aren’t about simplifying the cloud; they’re about understanding its complexity well enough that it stops being surprising.

For dummies

Let’s integrate, and go have lunch.

There's a conversation we need to have about AI in FinOps, one that goes deeper than the usual talk of tools and metrics. It's about how we fundamentally think about cloud infrastructure itself - what we truly see versus what we understand.

At Pelanor, we built our platform with intelligence as the foundation, not as an afterthought or a feature layered on top. This core decision has shaped every aspect of our development. It's also forced us to confront a simple but uncomfortable truth: much of what currently passes for 'insight' in cloud cost management is actually just reporting, dressed up to look like understanding

This brings us to a critical distinction between describing a system and truly understanding it. Most tools are optimized for the former. They'll show you that costs spiked but rarely tell you why. Without a grasp of causality, without tracing effects back to their root causes, you're ultimately left guessing and reacting. True understanding means recognizing that "Model X retried a failed job 6,000 times overnight" isn't just operational noise but a consequence of a deeper change - perhaps a new API rate limit, a dependency update, or a subtle configuration drift - that no standard dashboard would catch on its own.

We believe the returns on achieving genuine intelligence in this space are exceptionally high. Modern infrastructure is ruthlessly literal. It will allocate thousands of containers on command, regardless of actual need. It will duplicate effort, hoard expensive storage, and rerun jobs indefinitely because no one has effectively defined for it what "waste" or "inefficiency" looks like within its operational context.

At Pelanor, we're building FinOps systems that can actually reason. 

Systems that learn what "normal" looks like for your specific environment, autonomously notice when something genuinely isn't right, and then explain the anomaly clearly, without abstraction or apology. When systems can truly reason about your infrastructure, the path from complex data to confident action becomes remarkably short. That's how foundational intelligence gives your teams back their time - essentially enabling you to integrate the insights and, yes, go have lunch.

This pursuit has led us down two interconnected paths. The first is deep research, not in an abstract, academic sense, but as a disciplined investigation into hard, practical questions. How can causal reasoning uncover critical signals in high-noise environments? How do we accurately model financial responsibility when costs are shared, indirect, or emergent?

The second is relentless delivery. We're developing solutions for real-world problems our customers face now. We release new capabilities as they mature, rigorously tested and refined in partnership with teams grappling with these challenges daily. It's a constant balancing act: move fast, stay grounded in reality, and build certainty where it matters most.

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