For AI Vendors
Deliver Your AI Stack Where Their Data Lives
The entire AI stack is moving into customer environments. Deploy your full platform in customer VPCs or on-prem, or standardize the private deployments you’re already struggling to maintain, while retaining the ease of a managed service.
The Friction Between Cloud-Native AI and Private Infrastructure
AI runs where the data is, and your customers’ data cannot come to you. Your enterprise customers want the best AI, but their data-privacy and infrastructure requirements often force you to ship a fragmented, degraded version of your platform.
Maintaining Snowflakes
Satisfying data sovereignty and compliance demands often forces you to build unmanageable “snowflake” or “offline” versions that drain engineering resources and fragment your product roadmap.
Stale Deployments
The AI landscape moves fast. Manual update cycles leave on-prem customers stuck on outdated versions: older models, stale pipelines, and missing features. While your cloud users get continuous delivery, your enterprise customers fall behind.
Limited Operations
Debugging in enterprise environments often requires slow back-and-forth emails or requests for VPN access, stalling critical support cases and frustrating customers.
No Managed Services
Your AI stack likely relies on managed vector databases, GPU inference endpoints, queuing, and object storage. Re-architecting these elastic cloud dependencies for static, resource-constrained on-prem clusters forces you to ship a degraded version of your platform.
Deliver Your Full AI Stack with the Speed of SaaS
Give your customers private AI while keeping the centralized control, visibility, and update velocity of SaaS.
Deployment & Updates
Push updates and patches to private environments programmatically, ensuring all customers across AWS, Azure, GCP, and on-prem are on your latest release, within the maintenance windows each customer controls.
Zero-Trust Debugging
Debug secure environments without permanent VPNs. Request ephemeral, auditable remote access to customer appliances that must be explicitly approved by the customer, satisfying strict CISO requirements and data privacy mandates.
Full Stack Support
Tensor9 ingests your existing Terraform or Kubernetes manifests and translates them for any target environment, adapting managed services like RDS, SQS, and managed vector databases into local equivalents for the API surface your application actually uses, without code rewrites. We run GPU inference natively on GPU clouds like CoreWeave, Lambda Labs, and Crusoe.
Unified Observability
Treat distributed customer deployments like a single SaaS fleet. Stream logs, metrics, and traces from every customer back to your central dashboard for real-time health monitoring, while ensuring sensitive data never leaves their perimeter.
Customer Controls
Empower customers to define maintenance windows, approve operational access requests, and review full audit logs. This ensures your AI platform, and the data it processes, always operates within their strict internal compliance and governance policies.
How Serval Ships AI Into Its Customers’ Own Cloud Accounts
“Reproducing our stack in someone else’s cloud account is not trivial. The thing that sold us on Tensor9 was that they didn’t ask us to rewrite our infrastructure code. We pointed them at our existing Terraform and Helm, and three weeks later we had a live production deployment.”
How Tensor9 Works
Your AI Stack, Rebuilt for Their Environment
Tensor9 rebuilds your existing stack for any target, automatically translating cloud services to Azure, GCP, or on-prem equivalents for the API surface your application actually uses, so you can deploy anywhere without maintaining separate codebases. Stream metrics, logs, and traces back to your control plane and remotely operate customer environments for a SaaS-like operational experience. Tensor9 runs in your environment to maximize control and security.
Frequently Asked Questions
Tensor9 is a software adaptation platform. We enable AI vendors to deliver their product inside customer infrastructure, whether you're building that capability for the first time or standardizing a patchwork of bespoke deployments that isn't scaling. We rebuild your existing stack for any target environment so sensitive data stays with the customer.
- Private AI (Bring Your Own Cloud): You have a SaaS AI platform, but a major bank requires all inference to happen within their AWS account to ensure prompts and proprietary code never leave their perimeter.
- Data Gravity and Fine-Tuning: A customer wants to fine-tune your model on petabytes of internal data. Moving that data to your cloud is impossible due to cost or regulation, so you deploy the training pipeline to their data instead.
- Cloud-Agnostic AI Stack Delivery: Your stack is optimized for AWS, but a prospect mandates deployment on Azure, Google Cloud, or a GPU cloud such as CoreWeave where they have committed GPU spend.
- AI Observability and Governance in Customer Environments: Deliver AI observability, governance, and compliance tooling directly inside customer environments, so model monitoring, drift detection, audit trails, and policy enforcement run where the data lives, not in a third-party cloud.
- Standardizing Existing Private AI Deployments: You already deploy models into customer environments, but every customer has a different setup, different scripts, different GPU configs, and different support procedures. Tensor9 replaces the patchwork with a standardized delivery and operations model across all your customers.
You can deploy to virtually any environment: customer-owned VPCs (AWS, Azure, GCP), private data centers, all with or without Kubernetes. You can also deploy to GPU clouds such as CoreWeave, Lambda, and Crusoe. 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 the API surface your application actually uses, 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 own environment, 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.