What Vylara Is: Your Repo, Analyzed and Deployed to Your Own AWS

Vylara is a deployment platform that connects to your code repository, works out the cloud infrastructure your application actually needs — databases, caches, queues, storage, the running service itself — and provisions and manages it in your own AWS account. You don’t hand hosting to a third party, and you don’t hire a DevOps engineer to wire it up. Vylara orchestrates the infrastructure; you own every resource it creates, and you can revoke its access at any time. Today the deploy target is AWS. This post walks through that flow end to end and states the honest limits along the way.

The one-line version, and why it matters #

The idea Vylara ships against is simple: push your code, and the AI handles the cloud. In practice the Vylara agent reads your repository, proposes the infrastructure, generates the config files as pull requests, and — on your approval — runs the first deploy into AWS. The distinction that trips people up is that connecting your repo and merging a config PR does not create cloud resources. A merge updates your repository (Dockerfile, CI pipeline, deployment configs). The first deploy is the step that provisions the AWS environment. Keeping those two events separate is deliberate: you can review generated infrastructure without anything going live by accident. For the broader argument for why a small team can run production on AWS this way, the deploy without a DevOps team pillar lays it out.

Step one: connect the repo #

You install the Vylara GitHub App — a GitHub App, not an OAuth app, so permissions are fine-grained and installation-scoped — and select a repository. The bot identity that opens pull requests is vylara-ai[bot], so every generated change is attributable and reviewable in your normal Git workflow. Vylara requests read/write on repository contents and pull requests (to push generated configs), and read-only on webhooks and metadata so it can react to push events. Nothing is force-pushed; changes arrive as PRs you merge on your terms.

Step two: analyze the codebase #

Once connected, the Vylara agent clones the repo and detects the stack: language, framework, dependencies, environment variables, service boundaries, and — the useful part — the managed resources your app implies. Detection is dependency-driven. If it sees prisma or psycopg2 in your manifest it flags a PostgreSQL database; ioredis or redis flags a Redis cache; bullmq implies a Redis-backed queue; @aws-sdk/client-dynamodb or a DYNAMODB_TABLE_NAME env var maps to DynamoDB; multer plus the S3 SDK flags a storage bucket. You then see a plain "Your app needs" screen — not raw infrastructure code — where each resource offers two choices: "Create a managed one for me" or "I already have one" with a field for your existing connection string.

The estimates on that screen are concrete and worth quoting when you’re budgeting: a managed PostgreSQL database shows around ~$15/mo, a managed Redis cache around ~$8/mo, and a storage bucket around ~$1/mo. Those are starting figures, not guarantees — real cost tracks usage — but they’re useful anchors. If you want to sanity-check the database line specifically, the trade-offs in managed vs self-hosted databases on AWS are the right companion read before you pick "create for me."

Step three: review, then approve #

Analysis ends at a human checkpoint. The agent generates a Dockerfile, CI/CD pipeline, and deployment configs from battle-tested reference templates, and presents them in a diff view with plain-language explanations of what each file does. This is the honest part of the pitch: AI-generated infrastructure needs a human reading it before it runs. Vylara agrees — the checkpoint is built into the flow, and generated configs are validated (Dockerfile linting, CI workflow linting) before they’re offered to you. If you’ve read why AI-written infrastructure still shouldn’t apply itself unsupervised, this is the same principle enforced in the product: nothing touches your repo or your account without an explicit yes.

Step four: the first deploy #

Approving the deploy is what provisions AWS. Vylara uses a blue/green strategy by default: the new version deploys to a standby target group, waits for all tasks to pass health checks with an HTTP 200 on /health, and only then shifts traffic — so releases are zero-downtime with an instant rollback path. After a successful apply, Vylara persists a service object into your project’s config with the container port, primary URL, browser URL, public host, and health path. The same fields surface in the dashboard and in infrastructure chat, so debugging always references identical URLs and ports.

{
  "service": {
    "container_port": 3000,
    "primary_url": "https://api.internal",
    "browser_url": "https://app.example.com",
    "health_path": "/health"
  }
}

Step five: manage it without a console tab open #

After launch, Vylara collects usage metrics and its agent suggests scaling down to save money or up under load, offered as one-click actions. There’s also an in-app infrastructure chat that can read your server logs, metrics, resource inventory, network config, and cost data with no approval needed — because those are read operations. Anything that changes infrastructure (restart a service, resize a resource) is a write operation and requires your explicit inline approval. So when chat says your service is throwing 502s because Redis is unreachable, it shows you the log lines it found and proposes a restart with an Apply button; it does not act on your behalf. Secrets follow the same shape: Vylara detects environment variables in your code, stores them encrypted, syncs them to AWS Secrets Manager, and injects them at deploy time.

The reason all of this stays in your account matters commercially: your data lives in your cloud, your AWS bill flows directly from Amazon to you, and there’s no hosting lock-in to Vylara. If you cut ties, the infrastructure it built stays exactly where it is. Vylara charges for the orchestration and AI layer, not for your compute. The honest caveats are worth stating plainly: resource sizing is an educated guess until you pin it against real traffic, stateful migrations are harder than stateless ones, and generated infrastructure is a starting point you review, not gospel you rubber-stamp. Used that way, it’s a genuine substitute for the DevOps hire a five-person team can’t yet justify — and because you own the account, outgrowing Vylara never means re-platforming your infrastructure.