Deploy a complete stack
Identity, data services, models, applications, policies and monitoring, packaged together instead of rebuilt for every project or client.
Customer-controlled data & AI
The sovereign AI operating platform forPhysical AI.
Assemble, deploy and govern AI in your infrastructure or your clients’.
Not sure where to start? Tell us your use case and get free expert help, by chat or through the contact form.
Intent to infrastructureIllustrative workflow
YouSet up a private model endpoint with governed access to our asset data.
- 01Select deployment environment and data services
- 02Configure model serving and retrieval components
- 03Apply identity, access, network, and audit policies
- 04Review plan, resource needs, and changes
ApproveApprovedDeployObserveMaintain
What you pay for
Expert support is included in everything you pay.
Software is getting cheaper, so what you’re really paying for is the expertise. It’s included in every subscription and every bill.
How it works
Assemble. Deploy. Govern. Operate.
01 / ASSEMBLEChoose the stackData, models and applications, from curated packs or your own technologies.
02 / DEPLOYRun it where you need itInside your infrastructure or a client’s, each with its own data, credentials and policies.
03 / GOVERNSet the boundariesIdentity, network isolation, access policy, scoped secrets, audit and lineage.
04 / OPERATEManage its lifecycleInstall, inspect, update and maintain the stack over time.
Why Hyperlake
Launch faster. Keep more control. Pay for what runs.
Hyperlake replaces repeated platform assembly with reusable deployment patterns. 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
AI-assisted planning, with the same UI, CLI and MCP tools. Human approvals remain where policy requires them.
Match compute to demand
Scale eligible inference endpoints down when idle, with direct visibility into 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.
03 / The platform
The platform beneath your AI ambitions.
Run governed data, AI, and applications together on one consistent foundation. 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.
Data and AI together
Data services, knowledge, model infrastructure, and applications, on governed enterprise context.
Operated over time
Deploy, inspect, scale, back up, and update through one control surface, with policy and audit on 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.
06 / Who it serves
Build for yourself. Deliver for your clients.
Two audiences are served: enterprises that own their AI environment and providers that deploy into their clients’.
Own your AI environment.
A common foundation for governed data, models, agents, and applications, with infrastructure and policy in your control.
Deploy into every client environment.
Package your software with its supporting services and deliver one operating model in each client’s environment.
07 / Solution packs
A platform for data, AI, and applications.
Four solution packs cover data and knowledge, AI services, applications, and operations.
Make data usable
Lakehouse, relational, document, search, vector, graph, and streaming services.
Build private AI
Serve open models through KServe, run fine-tuning and evaluation, and connect to governed data and tools.
Deploy what you build
Run your own applications and APIs, or partner solutions, next to the data and AI they use.
Keep it working
Health, access, changes, scaling, backup, upgrades, and recovery.
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.
The stack has four parts: host and serve, fine-tune and evaluate, ground in context, and put it to work.
Host and serve
Deploy open models and custom endpoints with identity, routing, and usage controls. Eligible endpoints can scale down when idle.
Fine-tune and evaluate
Data preparation, fine-tuning, evaluation, and model-version workflows, then publish approved models.
Ground in context
Structured data, documents, vector search, and knowledge graphs, so applications use governed enterprise context.
Put it to work
Agents, copilots, APIs, and domain applications, with policies on 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, on Kubernetes. 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.