Deploy a complete stack
Package identity, data services, models, applications, policies, and monitoring together instead of integrating them from scratch for every project or client.
Customer-controlled data & AI
The sovereign AI operating platform forPhysical AI.
Bring data, AI services, and applications into one governed environment—in your infrastructure or your clients’. Assemble the stack, deploy it, and manage it through its full lifecycle.
One platform / Many AI solutionsCustomer-controlled
Workloads
What you build
- Physical AI workflows
- Enterprise agents
- AI-native products
Hyperlake
Assemble. Deploy. Govern. Operate.
- Solution packs + your software
- Natural language + CLI + policy
- Observability + lifecycle
Environments
Where it runs
- Your cloud
- Your clients’ clouds
- Private / on premises
DATA + AI + APPLICATIONS + OPERATIONSIdentity · Policy · Audit · Lineage
Why Hyperlake
Launch faster. Keep more control. Pay for what runs.
Hyperlake replaces repeated platform assembly with reusable deployment patterns, then helps teams manage the environment as workloads and customer needs change. Hyperlake adds 0% markup to the compute you run. Three benefits follow: less rebuilding, less manual work and less idle spend.
Work from intent to action
Use AI-assisted planning and the same UI, CLI, and MCP tools to propose, review, deploy, inspect, and troubleshoot changes. Human approvals remain where policy requires them.
Match compute to demand
Scale eligible inference endpoints down when idle; choose right-sized CPU and GPU capacity and keep direct visibility into infrastructure usage and cost.
Savings come from reuse, reduced integration effort, and workload-aware capacity—not a fixed percentage promise. Scale-to-zero depends on serving mode and whether the underlying compute can also scale down. To test your own case, use the free LLM API vs self-hosted GPU cost calculator.
AI initiatives need an environment that can grow with them.
Teams can choose models and build applications. Making them work together across real data, security boundaries, compute, and client environments is harder. Every project should not have to assemble its own foundation—or restart the governance review from scratch.
01 / The control surface
Describe the AI environment you need.
Hyperlake makes infrastructure callable through an application, CLI, and MCP-compatible tools. State an intent in natural language, inspect the proposed actions, then apply them under your organization’s policies. The example below turns a two-sentence request into a four-step plan.
This is the experience we are building toward: the ease of working in an AI coding tool, applied to governed infrastructure and data services.
“Set up a private model endpoint with governed access to our asset data. Add a vector store and keep it in the customer’s environment.”
- 01 Select deployment environment and data services.
- 02 Configure model serving and retrieval components.
- 03 Apply identity, access, network, and audit policies.
- 04 Review plan, resource needs, and changes.
APPROVE → DEPLOY → OBSERVE → MAINTAIN
Example of the intended workflow. Specific integrations and automations depend on the deployment.
02 / What it enables
Bring the full AI stack into production.
Hyperlake connects data, models, applications, and the infrastructure services they depend on. Teams get one way to deploy, govern, and maintain the whole environment. The path has six steps, from choosing the stack to delivering it again.
Choose the stack
Combine data, execution, model, agent, and application services. Start with curated packs or bring your own technologies.
Run it where you need it
Put the stack inside your infrastructure or a client’s, with each environment’s own data, credentials, and policies.
Set the boundaries
Apply identity, network isolation, access policy, scoped secrets, audit, and lineage across workloads.
Manage its lifecycle
Install services, inspect health, update components, and maintain a consistent operating posture over time.
Build on what works
Add new capabilities and applications without rebuilding the environment beneath each one.
Deliver again
Use the same platform approach across teams, sites, products, and customer environments.
03 / The platform
The platform beneath your AI ambitions.
Run governed data, AI, and applications together. Choose the services each workload needs while preserving a consistent foundation for security, operations, and change. Three capabilities carry it: a customer-controlled foundation, data and AI together, and operation over time.
See it live. A live demonstration: real deployed models analyze recorded robot feeds, with every result tied to evidence. Open the Physical AI reliability and safety operations demonstration.
Hyperlake platform architectureComposable / Sovereign
Your solutions
- Agents & applications
- Physical AI workloads
- AI-native products
Modular capabilities
- Data & knowledge
- Models & AI services
- Applications & workflows
Shared controls
- Security & access
- Observability & resilience
- Lifecycle & audit
HYPERLAKE FOUNDATION & CONTROL SURFACE Your infrastructure / your clients’ infrastructure
Customer-controlled foundation
Your data, applications, models, and keys stay within infrastructure you control. Hyperlake coordinates the services that run there.
Data and AI together
Combine data services, knowledge, model infrastructure, and applications so teams can build useful AI on governed enterprise context.
Operated over time
Deploy, inspect, scale, back up, update, and maintain the stack through one control surface, with policy and audit applied to changes.
Identity to data
One identity path. Policies at every approved access point.
People and agents should reach data through authenticated, governed services—not by sharing long-lived database passwords. Access takes four steps: sign in, carry identity, decide, and enforce and record. Hyperlake’s cluster-scoped tokens are signed with RS256 and expire after 24 hours.
01 / SIGN INOAuth / OIDC proxyConnect your identity provider for user access to apps and platform interfaces.
02 / CARRY IDENTITYValidated JWTPass scoped user or workload identity to APIs, query services, and tools.
03 / DECIDEOPA policyEvaluate who can access which data, actions, and resources in context.
04 / ENFORCE & RECORDGoverned serviceEnforce decisions at integrated access points; log the action and relevant lineage.
The enforcement boundary matters: direct routes to an engine must be blocked or governed as well. Each engine needs an appropriate integration for data-level policies.
04 / Full lifecycle
Deployment is the start of the job.
A production AI environment needs dependable data, protected applications, observability, and change management after the first install. Hyperlake brings the stack and its ongoing care into one control surface. Five stages follow the first install: assemble, deploy, govern, observe, and maintain.
01 / DESIGN
Assemble
Choose workloads, engines, dependencies, resources, and policies.
02 / RELEASE
Deploy
Install and configure services in the required environment.
03 / CONTROL
Govern
Manage access, secrets, audit, lineage, and workload boundaries.
04 / RUN
Observe
Inspect health, performance, logs, usage, and cost signals.
05 / EVOLVE
Maintain
Scale, back up, restore, patch, upgrade, and remediate over time.
Operational procedures vary by engine and solution pack. Hyperlake gives teams a consistent place to manage and inspect them.
06 / Who it serves
Build for yourself. Deliver for your clients.
Enterprises need control of their own AI environment. Providers need a repeatable way to deliver into many distinct customer environments. Two audiences are served: enterprises that own their AI environment and providers that deploy into their clients’.
Own your AI environment.
Give internal teams a common foundation for governed data, models, agents, and applications while keeping infrastructure and policy in your control.
Deploy into every client environment.
Package your software with its supporting services and deliver a consistent operating model in each client’s cloud or private infrastructure.
07 / Solution packs
A platform for data, AI, and applications.
Install the capabilities your use case needs. Hyperlake provides the common environment and operations across them. Four solution packs cover data and knowledge, AI services, applications, and operations.
Make data usable
Bring together lakehouse, relational, document, search, vector, graph, and streaming services for analytics and AI applications.
Build private AI
Serve small and large open models through KServe, run separate fine-tuning and evaluation workloads, and connect agents to governed data and tools.
Deploy what you build
Run your own applications and APIs—or integrate partner solutions—alongside the data and AI services they use.
Keep it working
Manage health, access, changes, scaling, backup, upgrades, and recovery across deployed components.
Engine choice follows the workload
Choose the open-source engines the use case needs:
- 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.
- Graph engines for relationships.
08 / Private AI
Build your own private AI stack.
Bring models to your data, customize them for your domain, and connect them to governed knowledge—all within infrastructure you or your client controls. The stack has four parts: host and serve, fine-tune and evaluate, ground in context, and put it to work.
Host and serve
Use KServe to deploy small or large open models and custom model endpoints, with identity, routing, and usage controls suited to the environment. Eligible endpoints can scale down when idle.
Fine-tune and evaluate
Run data preparation, fine-tuning, evaluation, and model-version workflows on selected compute, then publish approved models to serving endpoints.
Ground in context
Connect structured data, documents, vector search, and knowledge graphs or ontologies so applications can use governed enterprise context.
Put it to work
Build agents, copilots, APIs, and domain applications with policies governing their data and tool access.
Your models · Your data · Your keys · Your environment · One governance and operations approach
One operating approach, wherever the stack lives.
Run in your own cloud or a client-controlled environment. Hyperlake’s foundation uses Kubernetes to make services portable and manageable across infrastructure, while preserving consistent governance and operations. It runs in six kinds of environment: AWS, Azure, Google Cloud, OVH, private cloud, and on premises.
- AWS
- Azure
- Google Cloud
- OVH
- Private cloud
- On premises
Start with your AI stack
Build what you need. Keep control of how it runs.
Tell us what you want to deploy, whose environment it must run in, and which data and policies it needs. We’ll shape the platform around the real workload.