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.
Re-Architect Every Service
Moving a workload means re-architecting every managed service it depends on: databases, queues, object storage, streaming. 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 serves the cloud APIs your stack already uses on any cloud, so your workloads move on your terms, from one codebase instead of a fork per provider.
Adapt the Cloud, Not the Code
Your apps, SDKs, and Terraform keep calling the APIs they were written against. Each call is served by the target cloud’s native service: GKE, AKS, OKE, and managed databases and caches. Same app, different cloud.
Compatibility Layer
Where there is no wire-compatible equivalent, a compatibility layer presents the API your app was built for over the native backend, for the API surface your application actually uses, so your code keeps calling the same services it always has, unchanged.
One Source of Truth
Keep one codebase and one Terraform. The same code runs on every cloud instead of 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.
See How Every Call Is Served
Behavior differences for each service mapping are documented up front, and any request can be traced to the native service that handled it, so nothing surprises you in production.
How Tensor9 Works
Point, Run, Move
Point the SDKs, CLI, and Terraform your workload already uses at the Tensor9 endpoint running in the target cloud, without modifying them. Each call is served by that cloud’s native service, with a compatibility layer where there is no clean equivalent. Run in your own accounts on any cloud, on your own bill. 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. Your software keeps calling the cloud APIs it was built against, and Tensor9 serves them on each target, 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. Your app keeps calling the same APIs, and Tensor9 serves each one with a local equivalent on each cloud: a managed Postgres or Redis where one exists, and a compatibility layer that keeps your app’s existing API working for the API surface your application uses where one does not, so you deploy anywhere without maintaining separate codebases.
Ongoing. Nothing is ported, so there is no forked copy to drift: every release you ship runs on every cloud through the same code and the same Terraform, and portability stays a live capability instead of a one-time port.
Tensor9 serves your app on the target cloud, but moving live data across clouds and keeping it consistent through cutover is a separate problem it does not solve. We are honest about that line: you plan and run the data move yourself, and we stand up everything around it.