Vylara Labs: How Your Repo Becomes AWS You Own

Vylara Labs is a deployment platform that reads your code repository, works out what cloud infrastructure your app actually needs, and provisions and manages it in your *own* AWS account — no DevOps hire required. The key word is own: Vylara never hosts your workloads. Every server, database, cache, and bucket is created inside your AWS account, billed directly to you by AWS, and yours to keep if you ever revoke access. Vylara charges for the orchestration and AI layer, not for the compute. If you want the longer conceptual version, start with our guide on how to deploy without a DevOps team.

The core promise: you own everything #

Most "just push your code" platforms make a quiet trade: convenience now, lock-in later. Your data lives on their multi-tenant infrastructure, your bill runs through their pricing, and extracting yourself means a migration project. Vylara is built around the opposite principle. You connect your AWS account once, and from then on all resources are created there. Billing flows straight from AWS to you, your data stays in your account, and if you disconnect Vylara tomorrow you keep every resource it created. This is what people mean by bring your own cloud, and it’s the single most important thing to understand about the product.

We don’t hand you a folder of infrastructure code to maintain, either. What lands in your repo is the application-facing stuff — a Dockerfile, a CI pipeline, deployment configs — delivered as a pull request from vylara-ai[bot] that you review like any other PR. The cloud environment itself is created on your first deploy, not when you merge that PR. Merging updates the repository; deploying is the explicit step that stands up the AWS environment.

What happens after you connect a repo #

You install the Vylara GitHub App and pick a repository. The Vylara agent clones it and streams its analysis to the dashboard in real time: it detects the language and framework, reads dependencies, and identifies service boundaries and environment variables. Crucially, it also infers what backing infrastructure the app relies on. If it finds prisma or psycopg2 in your manifest, it flags a PostgreSQL database. If it finds ioredis, it flags a Redis cache. You see this as a plain "Your app needs" card — not raw infrastructure code — with rough monthly costs attached, roughly ~$15/mo for a managed Postgres and ~$8/mo for a managed cache in the examples the product surfaces.

For each resource you choose one of two things: Create for me, and Vylara provisions it in your account, or I already have one, and you paste a connection string. That choice matters more than it looks — if you’re weighing the trade-offs, our comparison of managed versus self-hosted databases on AWS walks through where the numbers actually break.

Nothing ships without your approval #

The honest part: AI-generated infrastructure is a draft, not gospel, and Vylara treats it that way. There’s a human checkpoint after analysis where you review the architecture the agent proposes before any config is generated. When configs are ready, they arrive as a reviewable diff with plain-language explanations of what each file does and why. Nothing touches your repository until you approve the PR, and nothing touches your AWS account until you approve the deploy. This mirrors a principle we’ve written about at length in why AI can write your infrastructure but shouldn’t apply it unsupervised: the machine drafts, the human gates.

Sizing deserves the same caveat. The first cut of resource sizing is an educated guess based on your code, not a load test. You’ll want to pin it once you see real traffic — which is exactly what the monitoring layer is for.

How the deploy itself works #

Every deploy uses a blue/green strategy by default. The new version goes to a standby target group, waits for all tasks to pass health checks (an HTTP 200 on /health), and only then takes traffic. That gives you zero-downtime releases and an instant rollback path without having to think about deployment strategy at all. After a successful deploy, Vylara records the service’s connection details — container port, primary URL, health path — so the dashboard and the in-app chat always reference the same URLs and ports when something needs debugging.

One deploy today lands on AWS. GCP and Azure are on the roadmap, but if a page tells you Vylara deploys your app to a running environment right now, that environment is AWS. Secrets are handled the same disciplined way: Vylara detects environment variables in your code, stores them encrypted, and syncs them to AWS Secrets Manager, injecting them at deploy time rather than leaving them scattered across chat threads and .env files.

{
  "detected": "PostgreSQL",
  "via": "prisma (package.json)",
  "choice": "create_managed",
  "est_monthly_usd": 15,
  "account": "your-own-aws"
}

After launch: chat that can read your logs #

Once your app is live, the in-app AI chat can read server logs, metrics, resource inventory, and cost data from your account — all read operations, no approval needed. Ask it "why is my service returning 502?" and it will pull the relevant CloudWatch logs and tell you what it found, then propose a fix. Any write action, like restarting a cache instance, still waits for your explicit confirmation. It’s the same approve-before-apply pattern that governs the whole platform, extended into day-two operations. If you’ve been running AWS without a dedicated engineer and want to see where a tool like this fits, our piece on managing AWS without a DevOps engineer is a good companion read.

That’s the whole shape of Vylara Labs: connect a repo, review what the agent proposes, approve a PR, approve a deploy, and end up with a monitored, rollback-capable AWS environment you fully own — without hiring for the roles that usually make this possible.