Questions about Hyperlake, answered.
What Hyperlake is, where it runs, and how it assembles, deploys and governs the data, models, applications and tools behind Physical AI, enterprise agents and AI-native products.
The basics
What is Hyperlake?
Hyperlake is a sovereign AI operating platform. It lets teams assemble, deploy and govern the data, models, applications and tools behind Physical AI, enterprise agents and AI-native products, in their own environment or their clients', while data, models, applications and keys stay in infrastructure they control.
What does sovereign AI mean at Hyperlake?
It means the customer controls where the AI stack runs and who can reach it. Data, applications, models and keys stay within infrastructure the customer or their client controls, and Hyperlake coordinates the services that run there under the organization's identity, policy and audit controls.
Who is Hyperlake for?
Enterprise and platform teams that want to own their AI environment, and AI product companies and service providers that deploy into many client environments and need repeatable installs, client ownership and one operating approach.
The platform
Where can Hyperlake run?
In AWS, Azure, Google Cloud, OVH, private cloud or on premises. Hyperlake's foundation uses Kubernetes to keep services portable and manageable across infrastructure, with consistent governance and operations wherever the stack lives.
What does the Hyperlake platform include?
Modular capabilities for data and knowledge, models and AI services, and applications and workflows, with shared controls for security and access, observability and resilience, and lifecycle and audit. Solution packs cover data and knowledge, AI services, applications and operations, and teams can bring their own software.
Which data engines and model tools does Hyperlake use?
Engine choice follows the workload: Iceberg and Trino for lakehouse analytics, PostgreSQL for applications, ClickHouse for fast analytics, MongoDB for documents, Redis for state, Kafka for streams, Qdrant or Milvus for vectors, and graph engines for relationships. Open models are served through KServe. Lifecycle support depends on the engine and deployment.
How do people and AI agents get access to data?
Through one identity path instead of shared, long-lived database passwords: sign-in through an OAuth/OIDC proxy connected to your identity provider, a validated JWT that carries scoped user or workload identity, OPA policy decisions, and enforcement and logging at integrated access points.
Can I manage AI infrastructure in natural language?
That is the experience Hyperlake is building toward. Infrastructure is callable through an application, a CLI and MCP-compatible tools: a team states an intent, inspects the proposed plan, then approves, deploys, observes and maintains it under the organization's policies. Specific integrations and automations depend on the deployment.
Costs and use cases
How does Hyperlake reduce costs?
Through reuse, reduced integration effort and workload-aware capacity, not a fixed percentage promise. Teams deploy complete stacks from reusable patterns, eligible inference endpoints can scale down when idle, and Hyperlake adds no compute markup. Scale-to-zero depends on the serving mode and whether the underlying compute can also scale down.
When does owning AI capacity beat paying per use?
When agents keep calling models, querying data and running jobs across many users or clients. Per-use charges can rise with each action, while well-used owned capacity can make extra actions cost relatively little until more capacity is needed. The curves can cross either way, so Hyperlake recommends making the ROI case per workload.
What can teams build on Hyperlake?
Example deployments include a repeatable Physical AI development environment, a private agentic cloud, a governed data and intelligence foundation, and autonomous multi-agent systems with purpose, memory and limits. These are examples of workloads Hyperlake can enable, not a fixed product catalog. See the use cases.
Does Hyperlake support Physical AI and robotics?
Yes. Hyperlake can bring simulation, orchestration, training, data and model services together in the customer's cloud, integrating tools such as NVIDIA OSMO, Omniverse, Isaac Sim and Isaac Lab, subject to licensing and validated integration, so teams can repeat the environment across sites.
Getting started
How do I get started with Hyperlake?
Tell the Hyperlake team what you want to deploy, whose environment it must run in, and which data and policies it needs; the platform is shaped around the real workload. Existing users sign in at app.hyperlake.cloud.
Start with a workload. Build the environment around it.
Tell us what you need to deploy, whose environment it must run in, and what it needs to connect to.