It’s all well and good to say that “every engineering decision is a buying decision.” It’s one of our favorite phrases here at CloudZero! However, it’s one thing to say it and quite another to LIVE IT. That’s pretty much the driving force behind CloudZero: We want your team to be able to access your company’s cloud spend data, analyze it from every angle that might be helpful, and use that information to grow the business.

Here is the thing. You don’t need more than one cloud service provider unless you are considering a multi-cloud strategy. And even if you plan to work with all three cloud vendors, you’ll want to know which one is right for specific services. To help you decide, we’ll share the strengths, pricing, and other differences between Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) below. Let’s start from the very beginning.

Businesses move to the cloud for several reasons. For some, moving to the cloud means gaining access to the best enterprise-level technology without the associated costs of handling, maintaining, and managing the underlying infrastructure. For others (like startups), the ability to scale quickly and respond faster to market demands is at the top of the list.

Pricing matters. Charge too little and you won’t earn enough revenue to stay afloat. Charge too much and you could lose thousands in potential business. You don’t want to price out some customers. So what can you do? This is where tiered pricing comes into play. The SaaS pricing approach can be helpful for companies looking to meet the needs and budgets of their different customer personas.

The Amazon Elastic Block Store (EBS) service should provide high-performance block storage for Amazon EC2 instances. Yet, without careful management, Amazon EBS can become inefficient, leading to performance bottlenecks and cost overruns. For example, EBS volumes persist even after an EC2 instance has been terminated. The unused EBS volumes continue to incur costs.

Cloud observability tools offer visibility into your cloud infrastructure. They collect data from various sources to help you understand your applications’ performance. The tools enable you to monitor, optimize, and troubleshoot your cloud environment. In this guide, we’ll share why cloud observability is important and the best tools to consider.

It’s a fast-paced business world out there. This means artificial intelligence (AI) models that help modern businesses win are more than welcome. The right model can drive innovation, streamline workflows, and save you money. But OpenAI’s powerful models can have a catch: High-frequency API usage, complex queries, and data processing can lead to unexpectedly high costs. Surprise costs eat up budgets and threaten other critical investments. Not good.

In this blog series we’re examining the Five Myths of Apache Spark Optimization. The fifth and final myth in this series relates to another common assumption of many Spark users: Spark Dynamic Allocation automatically prevents Spark from wasting resources.

Gartner predicts that by the end of 2025, 80% of organizations will use cloud automation tools to orchestrate workloads. The firm also forecasts that spending on public cloud services will hit $679 billion by the end of 2024, with SaaS leading the cloud computing market. This surge in cloud spending will shape the future of SaaS, and cloud automation tools will play a key role in this transformation.