1. AWS EC2 – Your Computer in the Cloud
Think of EC2 (Elastic Compute Cloud) as your own remote computer that never sleeps. You decide how powerful it should be, when to start or stop it, and you only pay for what you use.
When to use it:
Whenever you need consistent computing power like hosting APIs, running model training, or handling large data processing jobs.
Example:
If you’re building a workflow that analyzes hundreds of company reports overnight, EC2 can take that load easily without overworking your local system. Once done, you can simply stop the instance and stop paying.
It’s flexible, scalable, and still one of the most reliable compute services around.
2. AWS Lambda – Code Without Servers
Now imagine running code without setting up a server. That’s AWS Lambda. You upload a function, define when it should run, and AWS takes care of everything else.
When to use it:
- When you need quick, event-driven actions.
- When your workload doesn’t need a full-time server.
Example:
Let’s say a new pitch deck lands in a shared folder. Lambda can automatically trigger a workflow to extract its text, summarize it, and update a database, all without you running a single server.
That’s the beauty of “serverless” computing, invisible infrastructure, visible results.
3. Azure Containers – Run Anywhere, Consistently
Containers have become the standard way to package and ship applications. Azure Container Instances let you deploy these instantly without worrying about dependencies or version mismatches.
When to use it:
- When you want the same code to run across multiple environments.
- When you’re testing microservices or modular AI agents.
- auto scaling
Example:
If you’ve built a Python-based investment analysis model, you can containerize it once and deploy it to Azure. The code, libraries, and configuration all travel together so it behaves the same way, every time.
4. S3 Buckets and Azure Blob Storage – Organized Chaos, Simplified
Every digital system needs a home for its data. That’s where S3 Buckets (AWS) and Blob Storage (Azure) come in. They’re like digital filing cabinets that never run out of space.
When to use it:
- To store files like pitch decks, reports, or logs.
- For backup, or to integrate with AI systems that need quick access to structured data.
Example:
In our internal automations, S3 often acts as the middle layer workflows push reports there, OpenAI nodes read from it, and results are sent back to dashboards or Sheets.
It’s reliable, inexpensive, and plays well with almost every tool in the ecosystem.
5. CloudTrail and Azure Monitor – The Watchdogs
Cloud environments are powerful but can get chaotic if not monitored. CloudTrail and Azure Monitor act like surveillance cameras, recording who did what, and when.
When to use it:
- For debugging failed runs.
- For tracking user activity or permission changes.
- For compliance and audits.
Example:
If a workflow suddenly stops working, CloudTrail can tell you exactly which user changed a setting or deleted a file. It’s the kind of visibility that saves hours of guesswork.
6. Redis – Speed in Its Purest Form
While most databases save information on disk, Redis stores it in memory, which makes it extremely fast.
When to use it:
- To cache frequent queries.
- To store temporary or session data.
- For real-time analytics or messaging.
Example:
If you’re building a chatbot, Redis can remember recent conversations. That means when a user asks a follow-up question, the system responds instantly without re-processing everything from scratch.
Bringing It All Together
When you combine these services intelligently, you start seeing patterns, EC2 handles heavy lifting, Lambda automates triggers, S3 stores results, and CloudTrail watches over everything.
This modular way of building allows us to move fast without losing control. And the same principle applies whether you’re an AI engineer, a data analyst, or simply someone curious about how digital systems keep running seamlessly.
At Capria, most of our internal workflows follow this exact philosophy: automate wherever possible, monitor everything, and design for flexibility. The tools are already there,
At Capria, most of our internal workflows follow this exact philosophy: automate wherever possible, monitor everything, and design for flexibility. The tools are already there, it’s how we connect them that makes the difference
