Heroku to Google Cloud in an hour.
Fully automated.
One command. An agent reads your repository and deploys it into the Google Cloud project you already own - after you approve the plan.
“We migrated our whole staging environment with a single prompt. Production was a one-click clone of staging. Once the tests gave us confidence we moved DNS off Heroku - we have been running production on our own cloud with Qovery ever since.”
“We liked the Heroku experience, and we knew the cloud meant skills and effort we did not have spare - that is why we kept putting the migration off. We do not regret doing it with Qovery. Deployments that took me over two hours now take 30 minutes.”
Three steps, then the agent does it.
Your side takes about five minutes. Everything after that is automated.
- 01
A service account key provisions the network and a GKE cluster in your own project - about twenty minutes, and nothing leaves your boundary. Qovery gets a role you can read, scope down or revoke. Talk to a migration engineer before you connect anything.
- 02
One command teaches your coding agent how to read the repository - a Procfile, a Dockerfile, a buildpack, a set of config vars - and describe the deployment in Qovery’s own terms. Nothing is deployed at this point. You are only giving the agent the vocabulary.
- 03
The agent detects every service, writes a Dockerfile where one is missing, maps your Heroku config vars to Qovery variables and secrets, and shows you the plan. You approve it before a single resource is created - and your Heroku Postgres stays where it is, connected over the network, so you validate against real data before migrating a byte.
- 04Optional
Take this first or last - some teams want it as step zero, before they connect anything. A Qovery staff solution engineer reviews the cluster setup and environment layout with your team, then goes through the practices that keep the estate cheap and quiet: node sizing and Spot policy, autoscaling thresholds, preview-environment lifetimes, secret scoping and how to promote the same artifact between stages.
- network
- your VPC, three fixed egress IPs
- cluster
- GKE, in your region
- access
- a role scoped to Qovery
Works with any coding agent that reads skills. Nothing is deployed at this point.
web Node.js 20 Dockerfile generated worker Node.js 20 detected from Procfile scheduler cron 2 jobs database PostgreSQL 15 connected remotely
Nothing has been created yet. You approve the plan first.
- cluster
- node sizing, Spot policy, autoscaling
- workflow
- stages, approval gates, preview TTLs
- access
- secret scoping, roles per environment
From there it is automatic. Your apps come up running and live on Google Cloud, reachable on a public URL - while the Heroku stack keeps serving traffic untouched.
You pay Google Cloud. Usually a lot less.
Heroku charges you for dyno hours. Google Cloud charges you for what you use - and as its customer you get committed use discounts, sustained use discounts and Spot VMs, none of which exist on a dyno-hour bill.
Moving a workload off Heroku onto Google Cloud usually cuts infrastructure cost - up to 60% for the same workload that was running on Heroku.
* Depends on your dyno counts and sizes, add-ons, data volume and region. A migration engineer will model your estate against the equivalent Google Cloud shape before you commit to anything.
What Google Cloud gives you that a dyno tier cannot.
Heroku sells you a fixed dyno size at list price. Google Cloud prices the machine you actually asked for, and discounts it whether or not you negotiate.
Sustained use discounts you do not have to ask for
- Google Cloud applies sustained use discounts automatically to instances that run for a large part of the month, and committed use discounts on top if you commit for one or three years. The first of those needs no contract and no forecast - it simply happens because the workload is long-running, which is precisely what a web service is.
Custom machine types
- Google Cloud lets you define the exact vCPU and memory a service needs instead of rounding up to the next fixed shape. This is the direct opposite of dyno tiers, where a service that wants slightly more memory has to buy a whole size class more of everything. A memory-heavy worker and a CPU-heavy API stop subsidising each other.
BigQuery and Vertex AI over internal networking
- If your data or model serving already lives in Google Cloud, running the applications in the same project means reaching them over internal networking with Workload Identity rather than public endpoints and long-lived service account keys. From Heroku, every one of those calls leaves the platform and is authenticated with a static credential in a config var.
Our control plane. Your account, your bill.
Qovery sits above the infrastructure and never owns it. Every cluster, database and bucket is provisioned inside the Google Cloud project you already hold - Google Cloud invoices you directly.
- We do not take a cut of your Google Cloud spend. A flat subscription, whether your bill is $2k or $200k.
- We push it the other way: idle nodes, oversized requests and preview environments left running get flagged so you stop paying for them.
- Heroku and the PaaS successors run all of this in their account. Qovery runs it in yours.
Not sure which Google Cloud services fit your workload? A solution engineer will map it with you.
CONTROL PLANEStop paying Qovery and the stack keeps running - the manifests and qovery/qovery Terraform are already in your account.
Your first app on Google Cloud,
live within the hour.
Any Google Cloud region, with multi-region and multi-cluster from one control plane. Nothing you do here touches your existing Heroku stack until you decide to move the domain.
Heroku to Google Cloud
Something not covered here? Talk to a migration engineer - they have done this on estates larger than yours.