Bring Your AI Models to
Their Data
Eliminate the risk of data exfiltration for high-stakes enterprise customers. Deploy your agents and LLMs in customer VPCs or on-prem, meeting data security mandates while retaining the ease of a managed service.
The Friction Between Cloud-Native AI and Private Infrastructure
Your enterprise customers demand the latest models and capabilities, but their strict compliance requirements often force you to deliver a stagnant, 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 Defense
The AI landscape moves fast. Manual update cycles leave on-prem customers stuck with outdated models and old reasoning capabilities. While your cloud users benefit from the latest optimizations, your enterprise customers suffer from lower accuracy and higher hallucination rates.
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 modern AI stack likely relies on managed vector databases or serverless GPU inference. Re-architecting these elastic cloud dependencies for static, resource-constrained on-prem clusters forces you to ship a degraded version of your platform.
Deliver Private AI with the Speed of SaaS
Give your customers total data sovereignty while retaining 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 have access to your latest reasoning capabilities and features instantly.
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 compiles them for any target environment, automatically translating managed services (like RDS) into local equivalents without code rewrites.
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 customer prompts and training data never leave 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 Lucenia Wins Against Legacy Search Giants
“Competing with OpenSearch and Elastic in the enterprise means handling strict on-prem requirements. Before Tensor9, self-hosted deployments were a black box that drained our support team. Tensor9 gave us the visibility to manage private deployments as if they were in our own fleet, helping us win contracts with multiple enterprise customers.”
How Tensor9 Works
Your AI Platform, Compiled for Their Environment
Tensor9 compiles your existing stack for any target, automatically translating cloud services to Azure, GCP, or on-prem equivalents, 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 an enterprise any-prem platform. We enable AI 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.
- 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 & 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 Model Serving: 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.
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 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.