Cloud Portability for Enterprise
Move Workloads When Cost, Risk, or a Regulator Demands It
The internal software you run assumes one cloud, so “we could move if we had to” stays a slide, not a capability. Tensor9 runs your existing stack natively on AWS, Azure, GCP, and OCI from one codebase, so you can hand a regulator the tested exit they mandate and keep real negotiating leverage at every renewal, without a rewrite. Cost and resilience follow.
Locked to One Cloud, by Architecture
Your workloads run great on the cloud you built on. But the moment cost, a regulator, or a resilience plan says move, your architecture says no.
Locked to One Provider
Your software assumes one cloud, so you renew from weakness. The leverage that comes from being able to move is exactly the leverage you don’t have.
Concentration Risk
Regulators like the EU’s DORA require a documented, tested exit from any single provider. A stack that can’t move is a resilience and compliance liability, not just an engineering one.
The Rewrite Tax
Moving a workload means re-architecting every managed service: S3, RDS, DynamoDB, MSK. Months of engineering per app, then forever to maintain.
Multiplied Operations
Every cloud you add is another deployment pipeline, another observability stack, another on-call surface. Running in two places doubles the work, not the value.
Run on Any Cloud, Without the Rewrite
Tensor9 translates the stack you already have for any cloud, so your workloads move on your terms, from one source of truth instead of a fork per provider.
Translate, Don’t Rewrite
Tensor9 reads your AWS stack and translates it to native equivalents on GCP, Azure, and OCI: GKE, AKS, OKE, Cloud SQL, Memorystore. Same app, different cloud.
Compatibility Layer
Where there is no wire-compatible equivalent, a compatibility layer presents the AWS API for the API surface your application uses over the native backend, so your code keeps calling S3 and DynamoDB, unchanged.
One Source of Truth
Define the stack once. Each cloud comes out of the same Terraform rather than forking away from it, so you don’t maintain a separate variant per provider.
A Tested Exit, on Demand
A workload that can stand up on another cloud on demand is a straight answer for procurement and regulators, and the pricing leverage you keep at every renewal.
Drift Detection
A controller in each deployment watches for drift and keeps every cloud on your latest release, with logs, metrics, and traces streamed back to the tools you already use.
How Tensor9 Works
Bind, Translate, Deploy
Bind the stack you already have, the Terraform that describes your workload, without modifying it. Tensor9 translates it into a deployment for each target cloud, mapping managed services to native equivalents and adding a compatibility layer where there is no clean one. Deploy into your own accounts on any cloud. A controller stays resident in each environment to handle runtime translation, drift detection, and telemetry back to the tools you already watch. Egress and data-plane consistency across clouds are real costs to plan for. Tensor9 removes the re-architecture, not the physics of moving bytes.
Frequently Asked Questions
Tensor9 lets you run your existing internal software on any cloud, in your own AWS, Azure, GCP, and OCI accounts, without a rewrite. We translate your stack for each target from one source of truth, so you run everywhere from one codebase instead of forking your infrastructure per cloud.
- Avoiding lock-in: you want the pricing leverage and negotiating position that comes from being able to move a workload, without maintaining it in two places.
- Concentration risk and exit mandates: your regulators, for example EU financial firms under DORA, require a documented, tested exit from any single cloud provider.
- Cost and placement: a workload is cheaper, faster, or required to run on a specific cloud or region, and today it cannot follow.
- Resilience: you want a second cloud you can stand up from the same source of truth as the first, instead of a paper failover plan.
AWS, Google Cloud, Microsoft Azure, and Oracle Cloud, with or without Kubernetes. Google Cloud and Azure are the most mature today, and Oracle Cloud is expanding. The deployment experience stays the same for you regardless of the target.
No. Tensor9 translates your existing cloud-native stack into local equivalents for each cloud, a managed Postgres or Redis where one exists, and a compatibility layer that keeps the AWS API working for the API surface your application uses where one does not, so you deploy anywhere without maintaining separate codebases.
Ongoing. Tensor9 stands up the target and keeps a controller resident to hold every cloud on your current release, so portability stays a live capability instead of a one-time port that drifts the moment you ship again.
Translating the stack stands up the target infrastructure, but moving live data across clouds and keeping it consistent through cutover is a separate problem that stack translation does not solve. We are honest about that line: you plan and run the data move yourself, and we stand up everything around it.