PaaS Platforms That Run Kubernetes Inside Your Own GCP or Azure Subscription
Six platforms now let you run Kubernetes in your own cloud account.
A PaaS platform that deploys Kubernetes into a customer's own GCP or Azure account solves three problems a shared-tenant platform cannot: it lets committed cloud spend and credits apply to the actual workload, it keeps the cluster running if the vendor itself goes down, and it keeps data inside a compliance boundary the customer already controls. Porter, Rafay, Aiven, OpenShift, Rancher, and Ownkube all offer some version of this bring-your-own-cloud (BYOC) model, but they differ sharply in which clouds they support, how much of the stack they manage, and how well they fit a small engineering team. This guide maps the category precisely, compares the platforms that actually deliver on it, and explains why GCP and Azure in particular change the economics of running Kubernetes this way.
Why Startups with GCP or Azure Commitments Are Looking Past Shared PaaS
Startups that negotiate committed spend with GCP or win credits through a startup program face a strange problem once they adopt a shared-tenant PaaS: the credits go unused. The vendor owns the infrastructure, so the PaaS bill runs separately against it, and the cloud commitment just sits idle in the account the startup actually pays into. That mismatch alone is reason enough to reconsider the model, but it's not the only one.
Vendor risk compounds the economics. On a shared PaaS, the provider's infrastructure is the only place the workload exists. If that provider has an outage, or shuts down entirely, the customer's application goes down with it. Under a BYOC model, the GKE or AKS cluster lives inside the customer's own cloud account. It stays reachable no matter what happens to the platform vendor, because the vendor never held the infrastructure.
Compliance exposure adds a third pressure. If a customer's data never leaves their VPC, it's easier to audit than data sitting on shared infrastructure they don't control. On a shared PaaS, a startup has to independently verify the vendor's SOC 2 report, its HIPAA posture, its subprocessor list, on top of whatever standing agreements it already has with its own cloud provider. That's duplicated audit work for every regulated customer a startup signs.
None of these three pressures, stranded credits, vendor risk, compliance friction, is a hypothetical concern. They compound as a company scales, and that tends to happen right when shared PaaS pricing also starts to get expensive. The fix isn't to abandon a managed platform for a hand-rolled Kubernetes setup. A distinct category of platform now exists to solve exactly this problem.
What BYOC Kubernetes PaaS actually means and how it works
A BYOC Kubernetes PaaS provisions and manages a Kubernetes cluster inside the customer's own cloud account: GKE on GCP, AKS on Azure. The data plane belongs to the customer. So does the VPC, the IAM roles, and every node the cluster runs on. The platform vendor operates the management plane, the tooling, the UI, the automation that provisions and updates the cluster, but it never owns the compute underneath it. The customer's cloud account is the substrate the whole system runs on.
That definition draws a clean boundary around three categories that get lumped in with BYOC but aren't the same thing. With shared-tenant PaaS, a customer just gets a logical slice of infrastructure that the vendor owns. Raw IaaS hands the customer a blank cloud account and leaves them to provision and manage Kubernetes themselves, with no managed layer. Container-only services like Cloud Run or App Runner run a container at a URL, which is useful, but they stop well short of owning the full deployment lifecycle: no managed databases, no networking layer, no built-in compliance controls.
The practical consequence of the BYOC definition is straightforward. Workloads run on whatever a customer already has: their committed-spend contracts, their cloud credits, and their compliance posture. The PaaS layer adds orchestration on top of that foundation. It doesn't extract the underlying economics the way a shared-tenant vendor does.
That boundary also determines which platforms belong in this guide. One such platform appears later in this piece just to mark that edge.
GCP and Azure Kubernetes Pricing's Structural Cost Advantage Over AWS
You can see the clearest structural difference between the three major clouds in control plane pricing. AKS charges nothing for the control plane. GKE charges nothing for the control plane on one zonal Standard cluster per billing account, and $0.10 an hour for any additional or regional Standard cluster. EKS charges a meaningful monthly fee per cluster, a cost that scales directly with how many clusters a team runs. A startup running several clusters across environments feels that difference compound month over month.
GKE Autopilot pushes the advantage further. Autopilot bills only for the pod-level CPU and memory actually scheduled and running, with no charge for capacity that sits idle. Most early-stage startups run variable traffic, so their clusters aren't consistently saturated, and for them that pricing model is structurally cheaper than paying for reserved node capacity whether or not it's in use.
GCP adds a second mechanism on top: sustained use discounts apply automatically to Compute Engine instances that run for a meaningful share of the billing month, with no upfront commitment and no purchase required. AWS has no direct equivalent at the on-demand tier. Azure, meanwhile, wins on a different axis. Organizations running existing Microsoft licenses can apply the Azure Hybrid Benefit, one of the highest-return optimizations available to a Windows-stack shop, effectively bringing Windows compute down to Linux pricing.
List prices across AWS, GCP, and Azure sit within a narrow band of each other. The real cost differentiation comes from mechanisms like these, not from the headline rate per hour. That matters directly for the BYOC decision: a PaaS that deploys natively into GCP or Azure lets a customer capture these structural savings automatically. If a platform deploys only into AWS, it forces a team to choose between the BYOC model and the pricing advantages their cloud provider actually offers. A PaaS that deploys into GCP or Azure natively, as Porter does across all three cloud providers, removes that tradeoff.
Which PaaS platforms actually deploy Kubernetes into your GCP or Azure account
The field calling itself "BYOC" is narrower than the marketing suggests. Most platforms that use the term either support only one cloud, run on ECS instead of Kubernetes, or restrict BYOC to an enterprise tier that's out of reach for an early-stage team. Buyers evaluating this category should check each vendor against the same criteria: which clouds it actually supports, where the data plane lives, how much of the deployment lifecycle it owns, and whether compliance controls come built in or bolted on later.
Porter provisions and manages GKE, AKS, and EKS clusters directly inside a customer's own cloud account, which makes it one of the few platforms offering genuine multi-cloud BYOC with Kubernetes as the substrate across all three providers. It covers the full deployment lifecycle: VPC creation, CI/CD, autoscaling, GPU workloads, and automatic CVE patching, so the managed layer handles the undifferentiated operational work and it doesn't land on product engineers. Pricing is resource-based and transparent, with a dedicated program for early-stage startups, and customers pay for compute inside their own cloud account rather than a markup layered on top of shared infrastructure. Porter fits startups and growth-stage companies that need production-grade infrastructure with compliance built in, that are running on GCP or Azure credits or committed spend, and that don't have a dedicated DevOps team to run Kubernetes by hand.
Railway takes a different shape as a full-stack PaaS. Its 2026 platform overview describes native managed Postgres, MySQL, Redis, and Mongo, private service-to-service networking, and multi-region stateless services. One-click high-availability Postgres on Patroni shipped in early 2026, alongside agentic provisioning through the Stripe Projects CLI. Railway's usage-based pricing means your bill can grow quickly during a traffic spike, so Railway itself recommends that you set spend alerts right away. It's a strong fit for teams shipping full-stack products who want a single platform with a real managed database and who are comfortable with usage-based billing.
Encore.cloud is at the lighter end of the BYOC spectrum. It's framework-driven, built around services written in Go and TypeScript, with automatic infrastructure provisioning for pub/sub, cron jobs, and managed databases, plus traces and logs built in from the start. BYOC into AWS or GCP is available starting at the Pro tier, and the framework constraint means it suits teams willing to build inside Encore's programming model. It's a good match for small teams building in Go or TypeScript who'd rather have infrastructure provisioned from code annotations than configured through a control plane UI.
Flightcontrol belongs in this comparison as a contrast case rather than a recommendation for GCP or Azure readers. It deploys into a customer's AWS account using ECS with Fargate by default, or EC2, not Kubernetes, though Kubernetes support is listed as coming soon. As of February 2026, Flightcontrol has no GCP, Azure, or multi-cloud support and no announced plans for one. It's a legitimate BYOC platform on its own terms, built for AWS-committed teams whose workloads don't need Kubernetes and who just want the simplest managed deploy into an account they already run. It just falls outside the GCP/Azure Kubernetes scope this guide is built around.
Platforms like Rafay, Aiven, OpenShift, Rancher, and Ownkube occupy adjacent parts of this same landscape, generally oriented toward teams that already run Kubernetes operations at some scale and want a management layer on top of clusters they provision themselves, rather than a fully managed deploy-to-production experience built for a small team without dedicated infrastructure staff.
The GCP and Azure Cloud Substrate for AI and GPU Workloads
For an AI startup choosing where to host Kubernetes-based inference and training, the choice between GCP and Azure carries weight well beyond pricing. GCP's strengths run toward AI and ML tooling directly: BigQuery, the Gemini Enterprise Agent Platform (formerly known as Vertex AI), and GKE itself as a mature Kubernetes environment. Azure's strengths run toward Microsoft-stack integration and access to Azure OpenAI Service, including GPT-5 and its successors through GPT-6, alongside a broader set of enterprise AI tools.
GPU availability splits the two providers further. Azure carries the strongest GPU availability of the two in 2026, including a primary, non-exclusive partnership with OpenAI. GCP offers something neither AWS nor Azure can match directly: TPU v5e, v6e, and the v7 generation known as Ironwood, giving teams a training and inference path that doesn't depend on NVIDIA hardware.
BYOC carries particular weight for AI workloads because model weights, training data, and inference logs are usually the most sensitive assets a startup holds. Keeping them inside a customer's own VPC, rather than on shared-tenant infrastructure, reduces both the compliance burden and the risk of data mixing between customers on the same platform. A BYOC Kubernetes platform that natively supports GPU scheduling, which Porter's platform does as part of its full deployment lifecycle, lets an AI team run inference endpoints next to its standard services without building a separate GPU orchestration layer from scratch.
Running Kubernetes in Your Own Account for SOC 2 and HIPAA Compliance
The compliance case for BYOC follows a clean structural line. Data that lives inside a customer's own GCP or Azure account is covered by that provider's existing compliance certifications. Both GCP and Azure maintain SOC 2 Type II, HIPAA, ISO 27001, PCI DSS Level 1, and FedRAMP Moderate at the commercial level, with Azure Government reaching FedRAMP High. A customer running BYOC inherits that coverage directly, instead of having to independently verify a shared PaaS vendor's own certifications on top of it.
SOC 2 Type II and HIPAA overlap heavily but aren't interchangeable. A large majority of their controls match up, but HIPAA adds requirements SOC 2 never tests: a signed Business Associate Agreement, specific obligations around handling protected health information, and breach notification rules with fixed timelines a company has to meet regardless of its SOC 2 status.
AWS, Azure, and Google Cloud have all signed BAAs and offer HIPAA-compliant storage and compute. Deploying BYOC into any of these clouds preserves that coverage automatically, while a shared PaaS provider requires separate, independent verification of its own BAA standing before a regulated customer can trust it with sensitive regulated data. A platform that packages one-click SOC 2 and HIPAA compliance controls, as Porter does, turns what would otherwise be a multi-month engineering project into a configuration step, while preserving the underlying control that makes the audit defensible.
The operational overhead a BYOC PaaS eliminates for a small engineering team
The objection to this model is a fair one. Self-managing a Kubernetes fleet without any platform layer carries real, ongoing operational cost: cluster upgrades, OS patches, CVE remediation, backup-restore drills, and on-call response all have to land on someone's desk. For a small team, that workload competes directly with time spent on the actual product.
A BYOC PaaS doesn't push that work onto the customer. It makes the platform responsible for it, the same way a shared-tenant PaaS would. The compute lives inside the customer's own account rather than the vendor's; who's doing the maintenance work day to day stays the same.
Cluster management, automatic CVE patching, cost optimization, and CI/CD pipeline management are exactly the kind of work a startup shouldn't need a dedicated DevOps hire to handle. In the BYOC model, the platform vendor supplies the control plane, the UI, the automation, and the orchestration layer, while the customer's cloud account remains the substrate underneath it. Porter illustrates that division directly: it provisions GKE, AKS, or EKS clusters inside a customer's own account and handles cluster upgrades, CVE patching, and networking without taking a cut of the underlying economics.
The real comparison isn't BYOC PaaS against a managed shared-tenant PaaS. On operational complexity, the two are roughly equivalent, since both hand the maintenance burden to the platform. The comparison that actually matters is BYOC PaaS against DIY Kubernetes, where the managed layer removes the operational overhead without asking the team to give up control of their infrastructure or the cost advantages of running it in their own account.
Choosing Between GCP and Azure as the Substrate for Your BYOC Deployment
The choice between GCP and Azure isn't purely a pricing decision. The two providers sit within a narrow band of each other on cost for most workloads, so the real differentiation comes from the structural mechanisms covered earlier, GKE Autopilot's per-pod pricing on one side, the Azure Hybrid Benefit on the other, plus the AI tooling and ecosystem fit that matters most to the specific workload being deployed.
A team already standardized on Microsoft infrastructure, with existing Windows licenses and a Microsoft-centric enterprise sales motion, gets more out of Azure's Hybrid Benefit and its OpenAI access than it would out of switching providers to chase a marginal pricing difference. A team building on BigQuery, running TPU-based training, or operating in an AI/ML-first stack is better served by GCP's tooling and its GKE maturity. Neither choice is wrong in isolation, but locking into the wrong one early can mean leaving real savings or real capability on the table later.
That's the case for choosing a BYOC platform that doesn't force the decision permanently. A platform capable of deploying across GCP, Azure, and AWS lets a startup pick the substrate that fits its workload today without foreclosing a move later, if the AI tooling, the GPU availability, or the licensing math shifts. That flexibility is what makes the GCP-or-Azure decision one worth making deliberately.
