Vylara Labs: How Your Repo Becomes Running AWS Infrastructure

Vylara Labs is a deployment platform that reads your code repository, works out what cloud infrastructure your app needs, and provisions and runs it in your *own* AWS account — with a human approving every critical step. You install a GitHub App, the Vylara agent analyzes the repo, it proposes the databases, caches, and services it detected, and once you approve, the first deploy creates the environment in your account. There is no Vylara-hosted layer for your workloads: everything lives where your AWS bill already does.

That last point is the whole design philosophy, so it is worth stating flatly before anything else. Vylara never hosts your application. It orchestrates infrastructure on cloud accounts you own, stores encrypted credentials to manage them on your behalf, and lets you revoke that access at any time while keeping everything it created. Billing flows straight from AWS to you; Vylara charges for the orchestration and agent layer, not for compute. If you have read our take on how to manage AWS without a DevOps engineer, this is the product-shaped version of that argument.

What actually happens when you connect a repo #

You start by installing the Vylara GitHub App and selecting a repository. It is a GitHub App rather than an OAuth app, which means installation-scoped tokens, fine-grained permissions, and a bot identity — anything Vylara opens in your repo arrives as a pull request from vylara-ai[bot], never as a silent push. The agent engine then clones the repo and analyzes it: language, framework, dependencies, environment variables, service boundaries, and the infrastructure those dependencies imply.

Detection is dependency-driven, not guesswork. If it sees prisma, pg, or typeorm in a Node project it flags a PostgreSQL database; ioredis or redis flags a Redis cache; @aws-sdk/client-s3 or multer flags object storage; bullmq flags a Redis-backed queue. It then shows a plain "Your app needs" card with each detected service and a rough monthly cost — a managed PostgreSQL database around ~$15/mo, a managed Redis cache around ~$8/mo, a storage bucket around ~$1/mo. For each one you choose "Create a managed database" or "I already have one" and paste a connection string. You see costs and choices, not raw infrastructure code.

The approval gates are the point, not a formality #

After analysis, the workflow stops at a human checkpoint labeled "Review analysis" before it generates any configuration. When you approve, the agent produces the Dockerfile, CI/CD pipeline, and deployment configs from battle-tested reference templates, and it validates them before they reach you. You review the generated files in a diff view with the agent’s explanations attached. Nothing touches your repository without that approval — this is the honest version of AI-generated infrastructure, where a machine writes the config but a person still decides whether to apply it. We wrote more about why that boundary matters in AI can write your infrastructure config, it still shouldn’t apply it unsupervised.

A distinction that trips people up: merging one of those PRs updates your repository, but it does not create cloud infrastructure. The first deploy is what provisions your AWS environment. Git and cloud are two separate approval surfaces — one changes files, the other changes running resources — and Vylara keeps them separate on purpose.

Deploys are blue/green by default #

Every deployment uses a blue/green strategy. The new version builds an image, deploys to a standby target group, and only takes traffic once all its tasks pass health checks — an HTTP 200 on /health. Because the previous version stays warm in the other group, rollback is effectively instant. You do not choose a deployment strategy or hand-write a load balancer rule; that is the outcome you get for free. After a successful deploy, Vylara writes back a service object with the connection details so both the dashboard and the in-app chat reference the exact same URLs and ports.

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

Secrets follow the same principle of storing sensitive material once and pushing it to the right place. Vylara detects environment variable usage during analysis — process.env.DATABASE_URL, os.getenv("SECRET"), and similar patterns — lists the names on a setup checklist, and offers to store the values encrypted and sync them to AWS Secrets Manager. The default runtime contract stays boring and portable: environment variables injected at deploy time, which is exactly what most teams want and a saner default than the scattered approach in Your Secrets Are in Slack Right Now.

After launch: chat that can read, and asks before it writes #

Once your app is running, the in-app chat can read server logs, metrics, resource inventory, network configuration, recent deployments, and cost data without asking permission — all read operations. When it diagnoses something (say, a 502 because Redis is refusing connections) it proposes a concrete fix as an inline action with Apply and Dismiss buttons. Any write — restarting a service, changing infrastructure — requires your explicit approval. The same read/write boundary that governs the initial deploy governs day-two operations, which is what makes it reasonable to run this without a dedicated ops person.

The honest limits are worth naming. AWS is the deploy target today; other providers are on the roadmap but not something to plan around yet. Resource sizing starts as an estimate and needs a real look once you have traffic. Stateless services are the smooth path; stateful migrations are genuinely harder and deserve care. Vylara is built to make the first 80% of infrastructure disappear so a small team can ship, and to keep you in the loop for the decisions that actually carry risk. If that trade sounds right, the deploy without a DevOps team pillar goes deeper on where it fits.