Getting your app to work locally? That’s the easy part.
Getting it to run reliably in production, at scale, without waking up at 3 AM? That’s where most teams hit a wall.

You’ve spent days (or weeks) building an API, refining features, squashing bugs, and testing locally. Everything looks solid. Then someone asks, “When are we deploying to production?”

Suddenly, you’re not just writing or shipping code anymore. You’re wrestling with:

• Dockerfiles that behave differently in prod
• Container registries and IAM permissions
• EKS vs ECS debates
• Load balancers, VPCs, subnets, and security groups
• CI/CD pipelines that break for “reasons”
• Monitoring, logging, and access control

The app is done. But the infrastructure around it? That’s a whole new project.

Why deployment eats more time than anyone expects

On paper, deployment feels like a single step: “Push to production.”
In reality, it’s a chain of interconnected decisions, each with its own failure modes.

First, you need a container that actually works outside your laptop:

• Pick a base image
• Install the right dependencies
• Configure environment variables
• Make sure ports bind correctly

Then that image needs to land in a registry with the right tags, auth, and permissions.

Next, it needs somewhere to run. On AWS, that usually means:

• Spinning up Amazon EKS or ECS
• Writing Kubernetes manifests (pods, services, config maps)
• Setting up networking, load balancers, and a public entry point
• Layering on CI/CD, logging, and monitoring

Individually, none of this is rocket science. But you’re not doing it once. You’re doing it for every service, every sprint, every new microservice.

The real problem isn’t complexity; it’s repetition

Here’s what we see again and again with founders and dev teams:

• A Python service needs one setup
• A Node.js API needs another
• A PHP app has its own quirks

The code changes. The infra busywork stays the same.

You end up:

• Configuring containers… again
• Preparing environments… again
• Debugging networking… again

Over time, deployment becomes a tax you pay on every release. And that tax adds up fast.

One founder we worked with estimated his team was spending 6–8 hours per service just on initial deployment setup. That’s time not spent on features, not spent on customers — just plumbing.

What if deployment felt more like “click, done”?

That’s exactly why we built CloudLaunchPad, a DevOps automation platform for AWS. 

The idea is simple: stop treating every deployment like a greenfield infra project. Instead, standardise the repetitive parts so your team can focus on what actually matters — architecture, performance, and reliability.

With CloudLaunchPad, a developer goes from code to production by providing just a few inputs:

• Tech stack (Node, Python, PHP, etc.)
• Runtime version
• Application port
• Deployment preferences (EKS or ECS, Git or container-first)

Under the hood, the platform handles:

• Containerising your app with optimised templates
• Pushing the image to your container registry
• Provisioning AWS infrastructure (EKS or ECS)
• Configuring networking, load balancing, and DNS

What used to take 1–3 hours of manual setup? Now it’s 30–40 minutes, end to end.

Git-based or container-first — your call

Not every team works the same way. Some live in Git; others already have mature container pipelines.

CloudLaunchPad supports both:

• Git-based deployments: Connect your repo, and we manage the build pipeline with CI/CD pipeline automation 
• ECR-based deployments: Deploy pre-built images straight from Amazon Elastic Container Registry

The goal isn’t to force a new workflow. It’s to plug into the one you already have.

EKS or ECS? You decide

We don’t believe in one-size-fits-all infra.

• EKS gives you managed Kubernetes for teams that need deep orchestration
• ECS offers a simpler, AWS-native model for teams that want less complexity

CloudLaunchPad integrates your choice directly into the deployment flow, with Kubernetes deployment automation for EKS. You keep control; we remove the repetitive config.

Smarter instance selection and networking

Even “simple” decisions like instance type can burn hours in research and second-guessing.

CloudLaunchPad includes an AI-assisted instance recommender that:

• Looks at your microservice count
• Considers your AWS region
• Suggests cost- and performance-optimised instances

You can always override manually — this is about reducing friction, not removing control.

For networking, you can:

• Drop into an existing VPC and subnet architecture
• Or let the platform spin up a new network environment

Either way, it integrates cleanly without disrupting your cloud topology.

Visibility from day one

Deploying is just step one. Once you’re live, you need to know what’s happening.

CloudLaunchPad bakes monitoring into the deployment environment:

• Zero-SSH log streaming: View app logs by microservice, no SSH needed
• Centralised dashboards: CPU, memory, disc, IOPS, and microservice status in one place
• Granular redeployments: Update a single service without redeploying everything
• Plug-and-play services: Attach databases, Redis, RabbitMQ, and Kafka as you scale

No more scrambling for access, no more “Which pod is this even running on?”

A consistent workflow as you grow

As teams scale, tribal knowledge becomes a bottleneck.

• One person “owns” Kubernetes manifests
• Another maintains the CI/CD pipeline
• New hires spend weeks learning how deployment actually works

A standardised workflow with clear access controls changes that. It doesn’t replace DevOps expertise — it frees your senior engineers from repetitive config so they can focus on architecture, security, and reliability.

Ready to stop rebuilding the same deployment pipeline?

App development will always be complex. Infrastructure will always need judgement.
But the repetitive plumbing around deployment? That’s solvable.

If your team is spending valuable cycles recreating the same pipelines for every service, it’s time to look at AWS deployment automation built specifically for your stack. 

Want to see how this would work for your stack?
Book a demo—we’ll walk through your current deployment process and map out a smoother path from code to cloud.