Business Compass LLC

Stop Overpaying for AI Responses You Could Get for a Fraction of the Cost If you’re running generative AI workloads on AWS, you’ve probably already felt the sting of an unexpectedly large bill. Not every prompt needs a frontier model. Sending a simple classification task to the same large language model you use for complex reasoning is like hiring a surgeon to put on a bandage — technically works, but wildly expensive. This guide is for ML engineers, cloud architects, and AI product teams who are building on AWS and want smarter control over what they spend on GenAI without sacrificing output quality. Here’s what we’ll walk through together: The real cost problem behind GenAI workloads on AWS and why a one-model-fits-all approach quietly drains your budget How to design a dynamic model selection architecture using AWS Bedrock and related services to route each request to the right model at the right price Practical cost optimization strategies you can...

Deploy EC2 Behind an Application Load Balancer on AWS If your app goes down every time traffic spikes, you already know the problem. Putting EC2 instances behind an AWS Application Load Balancer fixes that by spreading incoming requests across multiple servers automatically — so no single instance carries the full load. This guide is for developers, cloud engineers, and AWS beginners who want a working, production-ready setup without the guesswork. Here’s what we’ll walk through together: The core building blocks — EC2 instances, target groups, listeners, and how they all connect inside an AWS ALB EC2 architecture Step-by-step configuration — launching your EC2 instances, setting up the Application Load Balancer, and registering instances so traffic actually reaches them Security and optimization — locking down your setup with the right security groups, health checks, and AWS ALB security best practices so your app stays stable under real-world conditions By the...

AWS Data Stack Explained: Databases, Data Lakes, Warehouses, and Analytics If you’re building on AWS and trying to figure out where your data should actually live — and how to make sense of it — you’re in the right place. This guide is for developers, data engineers, and tech leads who want a clear picture of how the AWS data stack fits together. No fluff, no hand-waving — just a straight breakdown of the tools and when to reach for them. Here’s what we’ll cover: AWS databases explained — RDS, DynamoDB, Aurora, and how to pick the right one for your workload Data lakes vs. data warehouses on AWS — what each one does, why they’re different, and when you need both Amazon Redshift analytics and AWS analytics services — how to turn raw data into decisions your team can actually act on By the end, you’ll have a solid foundation for building a scalable data architecture on AWS without second-guessing every choice along the way. Understanding t...

Designing a Secure and Scalable AWS Cloud Platform: What You Actually Need to Know If you’re building on AWS and want your infrastructure to hold up under real-world pressure, security and scalability can’t be afterthoughts. They need to be baked in from the start. This guide is for cloud architects, DevOps engineers, and technical leads who are responsible for building or hardening AWS environments — whether you’re starting fresh or tightening up what you already have. Here’s what we’ll walk through: AWS network security foundation and identity management — how to structure your VPCs, lock down access with AWS Identity and Access Management, and make sure only the right people and services can touch the right resources Data protection and encryption — covering AWS data protection encryption strategies for data at rest and in transit, so your sensitive information stays safe at every layer Scaling and continuous improvement — cloud platform scala...

Ask Your Data a Question—And Actually Get an Answer Most business teams still rely on data analysts to pull reports, write SQL queries, and translate numbers into decisions. That works, but it’s slow—and it creates a bottleneck every time someone needs a quick answer from a dataset. Conversational data analytics on AWS flips that model. Instead of waiting on a query, business users type a plain-English question and get a direct, data-backed answer in seconds. That’s the combination Amazon Athena and AWS Bedrock make possible—serverless analytics paired with large language models that understand what you’re actually asking. This guide is for data engineers, cloud architects, and technical product managers who want to build or evaluate AI-powered business intelligence on AWS. Here’s what we’ll cover: How Amazon Athena works as the analytical backbone —running serverless SQL queries against your S3 data lake without managing infrastructure How AWS Bedr...

AI Infrastructure Reality Check: AWS vs Azure vs Vertex AI If you’re building AI at scale, you’ve probably already lost a few hours down the rabbit hole of comparing cloud AI platforms. AWS, Azure, and Google Cloud all promise to handle your machine learning workloads — but they each make different trade-offs, and picking the wrong one can cost you serious time and money. This guide is for ML engineers, cloud architects, and technical decision-makers who need a straight answer on which platform actually fits their needs — not a sales pitch. Here’s what we’ll break down: How AWS AI tools, Azure AI services, and Vertex AI machine learning each approach infrastructure differently — and where each one quietly falls short A real cost and performance comparison so you can see what the AWS vs Azure vs Google Cloud debate actually looks like in numbers How to match your AI strategy to the right platform based on your team size, existing stack, and workload typ...