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Sovereignty isn't a setting. It's the architecture.

Gridlight is a private AI inference platform built to be the abstraction layer between an enterprise and the AI models it runs. Rather than routing sensitive data through third-party cloud APIs, Gridlight deploys on hardware the customer already controls, intelligently routes each workload to the right model, and guarantees that data never leaves the customer's environment. The company's core promise is enterprise-grade AI without sacrificing data sovereignty or compliance posture.

Gridlight's primary target market is regulated and data-sensitive enterprises of 500 to 10,000+ employees – manufacturing and industrial, financial services, healthcare and life sciences, defense and public sector, telecommunications, and energy and field operations.

Competitively, Gridlight positions itself against three groups: DIY open-source inference engines (vLLM, Ollama, LM Studio, LocalAI), which require heavy in-house DevOps investment and lack enterprise governance; large platform incumbents (NVIDIA NIM/AI Enterprise, IBM watsonx, Red Hat OpenShift AI, VMware Private AI Foundation), which tend to be heavy, hardware-locked, or tied to a specific vendor ecosystem; and cloud AI APIs (OpenAI, Anthropic, Azure AI, AWS Bedrock, Google Vertex, Mistral), which offer best-in-class models but are structurally incompatible with strict data-sovereignty requirements.

Gridlight's differentiation is that it's a managed control plane that separates the hardware management from the AI inference. Following a simple deployment existing infrastructure is repurposed, clustered, maximized and made fault tolerant. Capacity-based (by hardware TFLOPs) pricing rather than consumption (tokens) ensures predictable costs regardless of usage.

  • Data Sovereignty Even when internet connected, data never leaves the environment
  • Model Routing Policy-based inference request routing between internal and external models
  • Pricing Model Compute capacity-based (predictable); no charge for token usage (consumption)
  • Hardware Flexibility Extensible; scales from 1 to many systems operating as a single cluster
  • Model Handling Hot-swappable
  • Model Collaboration Multiple models collaborate for comprehensive inference responses
  • Model Sharding Abstraction layer spreads larger models across hardware cluster
  • Deployment Path Flexible software deployment ensures hardware clusters support one or many AI architectures.
  • Enterprise Management Centrally managed; locally configurable within air-gapped environments
  • Voice Interactive Native support for voice interactions
  • Video Creation / Interpretation Native support for video interpretation
  • Image Creation / Interpretation Native support for image interpretation
  • High Availability Orchestrator and worker redundancy and state sharing