Operations | Monitoring | ITSM | DevOps | Cloud

Amazon Athena is heralded for its simplicity, pay-per-query pricing, and speed. Others say Athena has its fair share of limitations, which could affect your overall analytics experience and quality of analyses. So, what is what? In this post, we’ll share what AWS Athena does, compare it to Amazon Redshift, and share how to understand and control your Athena costs in AWS (with just one tool).

If your SaaS organization is experimenting with OpenAI, your cloud bill just got a new line item. And unless you know exactly what drives it, that line item can go from manageable to margin-killer, fast. It’s also worth clarifying that OpenAI is not the same as ChatGPT. ChatGPT is the familiar end-user app with a flat monthly subscription. OpenAI, meanwhile, is the platform behind it — a mix of models, features, and usage-based pricing that shifts depending on what you build.

The most challenging question in AI today isn’t how to build with it. It’s whether you can prove it’s worth what you’re spending on it. Every week, I hear the same thing from engineering and finance leaders: “We know the AI bills are big.

The cloud, as we know it today, was created as recently as 2006. For most of its lifespan since then, companies have been throwing money at cloud services with abandon. The competitive edge gained by having the newest, best, and most powerful tools at their disposal made it worthwhile for companies to spend ever-increasing amounts without too much worry.

If your team is experimenting with Google’s latest Gemini models, you’ve probably noticed the pricing can get murky. And like any cloud service, usage-based billing means those features can quickly rack up your SaaS costs. Free tiers fade fast. Token usage gets fuzzy. And before long, your GenAI bill feels more like a guessing game than a budget line item.

Some teams won’t touch DeepSeek because it’s Chinese. Others are quietly running pilots and rethinking how much reasoning and context they actually need, or can afford. For SaaS teams staring down runaway AI costs, DeepSeek’s mix of open-source freedom, massive context windows, and token rates 10–30X cheaper than OpenAI or Anthropic is tough to ignore. However, DeepSeek pricing comes with cache hits, cache misses, off-peak discounts, that September pricing shift, and more.

Anthropic’s Claude is one of the most powerful and developer-friendly large language models (LLMs) available. But as usage grows, so does cost. Here’s the reality: A single unoptimized development loop or unmonitored QA job can multiply costs 10x overnight. Most teams experimenting with Claude lack the visibility and guardrails needed to prevent runaway costs, especially once usage moves from R&D into production.

Organizations face increasingly complex cloud environments — from hybrid clouds to multi-cloud deployments — where costs can quickly spiral without real-time visibility and intelligent controls. This is why setting clear goals for cloud cost optimization is necessary to keep your organization proactive. The key to success lies not just in setting goals, however, but in ensuring those goals are clear, realistic, and supported by continuous measurement and actionable insights.

When OpenAI surged into the spotlight with ChatGPT, not everyone inside the company agreed on the path forward. In 2021, a group of senior researchers broke away. They had concerns about safety, transparency, and the direction of AI development. They went on to found Anthropic. And their answer to ChatGPT was Claude. Anthropic’s mission is for openness now. Yet, Claude’s pricing can feel as mysterious as the model weights behind the scenes.

Cloud costs keep rising, and most companies are struggling to contain it. That’s where today’s finance teams can step up their game, not only as a professional opportunity but as a leading protagonist on the cloud cost optimization stage. A bit of background first: Global public cloud spending is projected by Gartner to exceed $720 billion in 2025. That’s up from nearly $600 billion in 2024. And a lot of that is sheer, unmitigated waste.