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Tensor9 is an enterprise any-prem platform. We enable software vendors, like you, to unlock hard enterprise customers that can’t share sensitive data. To do this, we help you convert your existing product for delivery inside the customer’s cloud or datacenter, so that sensitive data stays with the customer. 

• From SaaS to BYOC: You have an AWS SaaS built with IaC. Enterprise customers now demand to run the product in their own AWS accounts, requiring immediate delivery without heavy engineering.
• From Kubernetes-only to using cloud services: You augmented your Kubernetes stack with AWS services (databases, queues). Now, customers require deployment on Azure or GCP for security reasons, but the app is tied to AWS dependencies.
• Multi-Cloud Portability: Your SaaS is optimized for AWS. New prospects demand it run on Google Cloud or Azure, requiring instant portability to unlock the market without re-platforming.

You can deploy to virtually any environment: customer-owned VPCs (AWS, Azure, GCP), private data centers, all with or without Kubernetes. The deployment experience remains consistent for you, regardless of the underlying infrastructure.

No. Tensor9 automatically translates your existing cloud-native stack into local equivalents for any environment, so you can deploy anywhere without maintaining separate codebases. 

Tensor9 aggregates metrics, logs, and traces from all your distributed deployments and forwards them to your existing tools like Datadog or Prometheus. You can see the health of your entire fleet in real-time, just as if it were running in your own cloud.

Your application runs entirely within your customer’s sovereign boundary, and their sensitive data never touches our control plane. Tensor9 only receives metadata from customer environments. This can include things like:

• The versions of Tensor9 software running in your and your customers’ environments.
• The number of Tensor9 controllers in each environment.
• The memory/cpu/network capacity of each machine.

No, it complements it. Deploying to customer-managed Kubernetes clusters provides flexibility for customers who want to run appliances in their own Kubernetes infrastructure, whether on-premises, in private data centers, or on self-managed cloud Kubernetes.