Git-Push Deploys to Your Own AWS, For a Small Team

Yes — you can keep the git-push workflow you loved on a PaaS and still run everything in your own AWS account, with no DevOps hire required. The trade-off that trips most small teams up is that a hosted platform owns your infrastructure, while owning your AWS account normally means owning Dockerfiles, pipelines, networking, and state. Vylara closes that gap: it reads your repo, proposes the AWS resources your app needs, and provisions them in your account, so you get PaaS-grade ergonomics without the PaaS lock-in. This post walks through what that flow actually looks like, where the honest limits are, and how it differs from the merge-to-deploy mental model people bring over from Heroku or Render.

The workflow a small team actually wants #

The appeal of git push deploys is that infrastructure stays invisible until something breaks. For a three- or five-person team, nobody wants to become the part-time platform engineer who babysits networking and release strategy. The practical target is simple: commit code, review a diff, and get a running service with a URL. Vylara’s version of this starts when you install the GitHub App and pick a repository. The agent clones it, detects your language and framework, and identifies the databases, caches, queues, and storage your code implies — Postgres from prisma, Redis from ioredis, S3 from the AWS SDK, and so on. You then see a plain "Your app needs" card with rough monthly costs (a managed Postgres around ~$15/mo, a small cache around ~$8/mo, a storage bucket around ~$1/mo) and choose "Create for me" or "I already have one."

That detection-first approach is the part that makes this feel like a PaaS rather than an infrastructure-as-code project. You’re not describing infrastructure; the agent is inferring it from code you already wrote. If you’ve read our walkthrough of how Vylara turns a repo into owned AWS, this is the same pipeline, viewed through the lens of a team migrating off a hosted platform.

Merge updates the repo. First deploy creates the cloud. #

Here’s the mental-model correction that matters most, because people coming from PaaS get it backwards. On a traditional platform, merging to main is the deploy. With Vylara, the git side and the cloud side are separate events. The agent opens a pull request from vylara-ai[bot] containing the generated Dockerfile, CI/CD pipeline, and deployment config. You review that diff — nothing lands in your repo without approval. Merging the PR updates your repository. It does not, by itself, spin up AWS resources.

Your first deploy is what creates the cloud environment: the database, cache, load balancer, and service all get provisioned in your AWS account at that point. This separation is a feature, not an annoyance — it means you can inspect exactly what will be committed and deployed before any real resource (or bill) exists. It also keeps your repo honest: configs live under version control the way everything else does, while the actual infrastructure is a deliberate, approved action.

What ’your own account’ really buys you #

Because every resource is created in your account, billing flows straight from AWS to you, your data never leaves your cloud, and you can revoke Vylara’s access at any time and keep everything that was provisioned. Vylara stores encrypted credentials to manage infrastructure on your behalf, and the connection is validated the right way — cross-account roles with an external ID rather than long-lived keys pasted into a dashboard. If you’re weighing the security model, our deep dive on cross-account IAM with external IDs explains why that pattern beats handing over access keys.

The ongoing experience leans on the same ownership. Deploys use a blue/green strategy by default, so releases are zero-downtime and rollback is immediate — you don’t configure that, it’s the standard path. After a successful deploy, the connection details (container port, primary URL, health path) are persisted so your dashboard and the in-app infrastructure chat always reference the same URLs and ports. That chat can read your CloudWatch logs and service status without approval, and it only asks for confirmation before a write action like restarting a service. That’s the difference between a platform that hides AWS from you and one that operates AWS for you while leaving the keys in your hand.

The honest limits #

A few things deserve straight talk. First, AWS is the only deploy target today — if you need Azure or GCP provisioning, that isn’t here yet. Second, AI-generated infrastructure is a strong starting point, not gospel: the generated configs pass validation checks, but you are the reviewer, and you should treat the diff like any other PR from a competent colleague. Third, resource sizing is an educated guess until real traffic pins it down — the cost figures above are starting estimates, and the agent surfaces scale-up and scale-down suggestions once usage metrics arrive. Finally, stateful migrations — moving a live database off your old platform — are genuinely harder than redeploying stateless services; if you’re coming from a hosted provider, read our notes on migrating from Render to your own AWS account before you cut over.

For the bigger picture of running production AWS with a tiny team — right-sizing, secrets, networking — the deploy without a DevOps team pillar collects the rest. But the headline for anyone chasing git-push simplicity on owned infrastructure is this: you don’t have to choose between the ergonomics you want and the ownership you need.