PandaStack vs Porter: Kubernetes-Backed PaaS

Both are Kubernetes — but whose Kubernetes?

Porter and PandaStack both deliver a PaaS experience backed by Kubernetes. The defining difference is ownership of the cluster.

• Porter typically provisions and manages Kubernetes inside *your* cloud account (AWS/GCP/Azure). You own the cluster and the cloud bill; Porter gives you the developer-friendly layer on top.
• PandaStack runs the Kubernetes itself (multi-region GKE). You never see a cluster, a node pool, or a kubeconfig. You push code; it runs.

That single distinction drives almost every other trade-off.

BYOC vs. fully managed

Porter's "bring your own cloud" model is powerful for teams that need workloads inside their own VPC for compliance, want to use existing cloud credits/committed-use discounts, or need direct access to other cloud resources (private RDS, internal services, IAM roles).

PandaStack's fully managed model is powerful for teams that don't want a cloud account, a Kubernetes bill, or cluster operations at all. There's nothing to provision; the free tier needs no cloud account.

Deploy experience

Both give you Git-driven deploys and abstract away raw manifests. PandaStack:

git push origin main
# auto-detect framework -> rootless BuildKit build in K8s Job
# -> image to Artifact Registry -> Helm deploy. Live.

Porter similarly connects to your repo and deploys into your cluster, exposing more of the Kubernetes underneath when you need it (you can drop down to the cluster because it's yours). PandaStack deliberately doesn't surface the cluster — there's no kubeconfig to fetch because the cluster isn't yours to touch.

The upshot: Porter is closer to "a friendly layer over my own K8s," PandaStack is closer to "I never think about K8s."

Databases

PandaStack includes managed databases as part of the platform: PostgreSQL (14.x, 16.x), MySQL (5.7, 8.x), MongoDB, and Redis via KubeBlocks, with scheduled and manual backups and an auto-injected DATABASE_URL.

With Porter, because workloads live in your cloud account, you'd commonly use your cloud provider's managed database (e.g., RDS) and connect over your VPC. That's a strength if you already standardize on RDS — you get its full feature set and your existing tooling. It's more setup if you just want a database wired in fast.

When BYOC really matters

Be honest about whether you need BYOC:

• You need workloads in your own VPC (private networking to internal services). → Porter's model fits.
• You have cloud committed-use discounts or credits. → Running in your account uses them. → Porter.
• Strict data residency in your own account. → Porter.

If none of those apply, BYOC is mostly added setup and a cloud bill you have to manage. Many teams over-index on "it's in my account" without needing it.

Operations and scaling

Porter manages the cluster, but it's still *your* cluster — node upgrades, cloud quotas, and the cloud bill are ultimately your domain even if Porter automates much of it. PandaStack abstracts all of that: scaling, node pools, and upgrades happen on the platform side. Free-tier apps use gVisor sandboxing, spot nodes, and KEDA scale-to-zero; you get the behavior without configuring it.

Pricing

PandaStack uses flat plans:

Compute ranges from Free (0.25 CPU / 512MB) to 8 CPU / 16GB (~$0.300/hr). There's no separate cloud bill — the plan is the price.

With Porter you pay Porter's platform pricing *plus* your underlying cloud spend. For large, steady workloads on discounted cloud commitments, that can be cost-effective. For small or spiky workloads, a bundled platform with a free tier is simpler and often cheaper at the low end. Check Porter's current pricing directly.

Honest recommendation

Choose Porter if:

• You need workloads inside your own cloud account/VPC.
• You want to leverage existing cloud credits or RDS.
• You want a PaaS layer but retain cluster ownership.

Choose PandaStack if:

• You don't want a cloud account or a Kubernetes bill.
• You want managed databases auto-wired into apps.
• You want a true zero-setup free tier.
• You'd rather never see a kubeconfig.

References

• [Porter documentation](https://docs.porter.run/)
• [Porter pricing](https://porter.run/pricing)
• [GKE documentation](https://cloud.google.com/kubernetes-engine/docs)
• [KubeBlocks documentation](https://kubeblocks.io/docs)
• [KEDA documentation](https://keda.sh/docs/)

---

If you'd rather skip the cloud account entirely, PandaStack's free tier runs your app and database on managed GKE with nothing to provision. Start at [dashboard.pandastack.io](https://dashboard.pandastack.io).