What Vylara Is: Your Repo Becomes Managed AWS You Own

Vylara is a deployment platform that connects to your code repository, works out the cloud infrastructure your application actually needs — databases, caches, storage, queues, the service itself — and provisions and manages it in your own AWS account. You never hand workloads to a third-party host; Vylara orchestrates the AWS layer on your behalf, and you can revoke its access and keep everything it created at any time. If you’ve been putting off a DevOps hire, this is the shape of the alternative: deploy without a DevOps team while your data and your bill stay entirely inside your own cloud account.

The core promise: you own the infrastructure #

The most important fact about Vylara is architectural, not cosmetic. Vylara never hosts your workloads. When it creates an RDS database, an ElastiCache Redis instance, or an ECS service, those resources live in the AWS account you connected. Billing flows directly from AWS to you, so there’s no compute markup — Vylara charges for the orchestration and AI layer, not for the servers. That also means the usual escape hatch is real: if you disconnect Vylara tomorrow, the load balancer, the database, and the container service keep running because they were always yours. This is the same reasoning behind managing AWS without a DevOps engineer — the goal is leverage, not a new dependency you can’t leave.

How a repo becomes running AWS #

The flow starts when you install the Vylara GitHub App and pick a repository. Because it’s a GitHub App rather than a broad OAuth grant, permissions are scoped and every change arrives with a bot identity, vylara-ai[bot], so you can see exactly what it proposed. From there the Vylara agent clones the repo and analyzes it: language, framework, dependencies, environment variables, service boundaries, and — crucially — what backing services the code implies. If your package.json pulls in prisma, it reads that as a PostgreSQL database. ioredis reads as a Redis cache. multer plus the AWS S3 SDK reads as file storage. The result isn’t a wall of infrastructure code; it’s a plain ’Your app needs’ card with rough monthly costs next to each item.

On that screen you decide per resource whether Vylara should create a managed version or wire up one you already have via a connection string. The sample estimates the product surfaces are honest and small — roughly $15/mo for a managed PostgreSQL database, about $8/mo for a Redis cache, around $1/mo for a storage bucket. Those are starting points, not promises; sizing is a guess until you run real traffic and pin it down. For the deeper cost trade-offs behind those numbers, the managed vs self-hosted database cost comparison is a good companion read before you commit to a database tier.

Nothing happens without your approval #

Vylara is built around human checkpoints, which is the honest way to ship AI-generated infrastructure. After analysis, the agent generates the Dockerfile, CI/CD pipeline, and deployment configuration from battle-tested reference templates and opens a pull request. You review the diff — with the agent’s plain-English explanation of each change — and merge only when you’re satisfied. Merging updates your repo. It does not create cloud resources. The AWS environment is created on the first deploy, as a separate, deliberate step.

That deploy runs blue/green by default: the new version comes up on a standby target group, waits for health checks to pass with an HTTP 200 on /health, and only then takes traffic — which is what gives you instant rollback if something looks wrong. After a successful deploy, Vylara records the service’s connection details so both the dashboard and the in-app chat reference the same URLs and ports.

A minimal service config the agent might propose looks approachable rather than intimidating:

{
  "service": {
    "container_port": 3000,
    "health_path": "/health",
    "public_ip_static": false,
    "domain_name": "api.example.com"
  }
}

After launch: secrets, chat, and staying honest about limits #

When Vylara detects environment variables like DATABASE_URL or JWT_SECRET in your code, it offers to manage them and sync them into AWS Secrets Manager, injecting them at deploy time. That’s a cleaner default than the reality most teams live with — if your secrets are currently pasted in a Slack thread, the secrets sprawl exit plan explains why that’s a problem worth closing early.

Once the app is live, the infrastructure chat becomes your read-first troubleshooting surface. It can pull recent CloudWatch logs, describe your VPC and security groups, list deployed resources, and check cost data — all without asking permission, because those are read-only. Anything that modifies infrastructure, like restarting a service, is shown as an explicit action with an Apply button you press yourself. When you ask why your service is returning 502s, the chat reads the logs and might come back with a concrete ECONNREFUSED to your Redis host, then propose a fix for you to confirm.

Be clear-eyed about the boundaries. Vylara deploys to AWS today; other providers like GCP and Azure are on the roadmap, not shipping. AI-generated configs still need a human to read the diff, which is exactly why the PR gate exists. And stateful migrations are genuinely harder than standing up a stateless service — moving an existing production database is not a one-click affair. Within those honest limits, though, the value is straightforward: a five-person team can go from a connected repo to a running, monitored, owned-by-you AWS environment without writing the pipeline, the Dockerfile, or the infrastructure code by hand.