Vylara Labs Explained: Your Repo to Your Own AWS

Vylara connects to your code repository, figures out the cloud infrastructure your app needs, and provisions it in your own AWS account — with you approving every step. You install a GitHub App, the Vylara agent analyzes the codebase, it recommends managed resources like a database or cache, opens a pull request with the Dockerfile and CI/CD it generated, and after you approve, the first deploy stands up a real environment in your AWS account. No DevOps hire, no leaving your cloud provider. This post walks through what actually happens, and where the honest limits are.

The core idea: you own the infrastructure #

The single most important fact about Vylara is that it never hosts your workloads. Everything — the containers, the database, the load balancer, the storage bucket — is created inside your AWS account. Vylara stores encrypted credentials so its agent engine can manage that infrastructure on your behalf, and you can revoke that access at any time and keep everything it built. Your data stays in your account, and the AWS bill flows directly from Amazon to you. Vylara charges for the orchestration and AI layer, not for the compute. If you’ve read our take on how to manage AWS without a DevOps engineer, this is the same philosophy made concrete: the platform does the wiring, you keep the keys.

That ownership model is enforced through cross-account IAM rather than you pasting long-lived keys into a dashboard. Vylara validates access with a lightweight identity check and can assume a role scoped to your account, optionally gated by an external ID. We wrote about why that pattern matters in cross-account IAM with external IDs — the short version is that you never hand over root credentials, and the trust relationship is something you can inspect and tear down yourself.

What happens when you connect a repo #

You start by installing the Vylara GitHub App — a proper GitHub App with a vylara-ai[bot] identity, not an OAuth app hoovering up broad scopes. It requests read/write on repository contents and pull requests so it can read your code and push generated config, plus read-only webhook and metadata access. Once installed, the agent clones the repo and runs an analysis pass: it detects the language and framework, walks the dependency manifest, and identifies service boundaries and environment variables. You watch this progress stream in the UI rather than staring at a spinner.

The interesting part is infrastructure detection. When the agent sees prisma or psycopg2 in your dependencies it infers you need PostgreSQL; ioredis or redis points to a cache; bullmq implies a Redis-backed queue; @aws-sdk/client-dynamodb maps to DynamoDB. Rather than dumping raw infrastructure modules on you, it presents a plain "Your app needs" card with rough monthly costs attached — a managed Postgres database at roughly $15/mo, a Redis cache around $8/mo, an S3 bucket near $1/mo. For each one you choose "Create a managed resource" or "I already have one" and paste a connection string. Those figures are starting estimates, not guarantees; real cost depends on the instance size you land on, which is a topic we cover in the honest stages of AWS infrastructure cost.

Review and approve — nothing is silent #

After analysis, there’s a human checkpoint before anything is generated, and another before anything reaches your repo. The agent produces a Dockerfile, CI/CD pipeline, and deployment configuration from battle-tested reference templates, validates them with standard linters, and opens a pull request. You review that PR as a normal diff with plain-language explanations of each change. Merging the PR updates your repository — it does not create cloud infrastructure. That distinction matters: the repo and the cloud environment are two separate things.

The cloud environment comes into existence on the first deploy, not on merge. When you trigger that deploy, the agent applies infrastructure to your AWS account using a blue/green strategy: a new version deploys to a standby target group, waits for health checks to pass on /health, and only then shifts traffic. That gives you zero-downtime releases and instant rollback without you ever writing a load balancer rule by hand. After a successful deploy, Vylara persists the service’s connection details — container port, primary URL, health path — so the dashboard and the AI chat both reference the same URLs when something goes wrong.

{
  "service": {
    "container_port": 8080,
    "primary_url": "https://api.example.com",
    "health_path": "/health"
  }
}

After launch: chat, secrets, and honest limits #

Once your app is live, an in-app AI chat can read your service status, CloudWatch logs, metrics, and cost data without needing approval, because those are read-only. When it proposes an actual change — restarting a service, for instance — that action requires your explicit inline confirmation before it runs. So if your service starts returning 502s, you can ask why, and the agent will pull the logs, spot that it can’t reach Redis, and offer a fix you either apply or dismiss. Secrets follow the same discipline: the agent detects environment variables in your code, you enter the values, and it syncs them to AWS Secrets Manager, injected as environment variables at deploy time.

Be clear-eyed about the boundaries. Deploys are AWS-only today; GCP and Azure are on the roadmap, not shipping. Generated infrastructure genuinely needs human review — the agent is good, but sizing is a guess until you pin it against real traffic, and a stateless service is far easier to hand off than a stateful database migration. This is the same reason we argue that AI can write your infrastructure code but shouldn’t apply it unsupervised: the value is in the drafting and the checkpoints, not in removing the human. If your mental model is "push code, review a diff, approve a deploy, keep your AWS account," you understand Vylara. For the wider picture, see our pillar on how to deploy without a DevOps team.