Example deployments. Your use case may be different.
These are examples of workloads Hyperlake can enable, not a fixed product catalog. For each one, see what gets deployed, what your team can do, and the economic case for using a reusable, customer-controlled operating platform.
01 / WHY HYPERLAKE
When agents do the work, the economics change.
You can buy a commercial AI solution, build your own entire stack, or bring a domain solution to a reusable, customer-controlled operating platform. The choice matters most when agents keep calling models, querying data, and running jobs across users and clients.
01 / BUY A SOLUTION
Fast to start. Usage becomes the bill.
Evaluate the product license, per-seat or per-task charges, model tokens, tool calls, data movement, and the ability to deploy privately. A bundled product may be the best choice when its workflow fits and volume is modest.
02 / BUILD EVERYTHING
Own the stack. Carry the work.
Fund platform architecture, model serving, data access, security, observability, deployment automation, upgrades, and incident response before and after you build the domain application.
03 / BUILD OR DEPLOY ON HYPERLAKE
Own the solution. Reuse the foundation.
Deploy a commercial or custom domain solution on customer-controlled infrastructure with integrated data, model, policy, and lifecycle capabilities. Choose evaluated open models where they fit; retain commercial models for tasks that need them.
Platform
Private environments, model and data services, identity, policy, automation.
Build it yourself: specialist hours every year $ · Hyperlake: managed lifecycle and one control plane, with agreed customer responsibilities.
Workload
Model inference, GPUs, storage, tools, and data movement.
Buy a solution: charges may follow usage $ · Hyperlake: customer-controlled capacity plus any chosen external services $.
Domain solution
The agent, simulation workflow, industry logic, experience, and evaluation.
Buy or build it in either route. Hyperlake provides the environment; it does not replace the application.
THE AGENT SCALE EFFECT
One agent is a demo. Thousands of actions are an operating model.
An agent can generate many model calls, searches, and experiments per outcome. More users, agents, or client installations multiply that activity. Under per-use pricing, the bill can rise with each action. With well-used owned capacity, additional actions can cost relatively little until more capacity is needed.
Commercial per-use chargesShared owned capacity
Illustrative shape only. A commercial product may offer committed capacity; owned GPUs have fixed costs, capacity steps, and operations overhead. The curves can cross in either direction depending on usage, utilization, model quality, and contract terms. Marginal compute cost approaches the cost of spare capacity within a provisioned pool; total cost never becomes zero. Work out where your own curves cross with the free LLM API vs self-hosted GPU cost calculator.
Make the ROI case per workload.
Compare time to production, cost per verified outcome, model and infrastructure spend, repeat rollout effort, and the measurable benefit of the domain solution. Hyperlake is strongest when you need private control, repeated deployments, and enough sustained work to benefit from shared capacity and model choice.
From robotics demo to a repeatable development environment.
Bring simulation, orchestration, training, data, and model services together in the customer's cloud. Hyperlake helps assemble a deployment suited to the robotics workflow, with access controls and operations built in, so teams can focus on experiments and rollout instead of rebuilding the stack for each site.
The turnkey starting point
Provision the environment, storage, datasets, model serving, monitoring, and scoped access from a reusable deployment pattern.
Integrate the tools the workflow calls for: NVIDIA OSMO, Omniverse, Isaac Sim, Isaac Lab, and/or other open-source or commercial components, subject to licensing and validated integration.
Connect simulation and training outputs to approved data and inference services; keep workloads inside the customer's cloud boundary.
Why this combination works
Simulation, data, models, identity, and audit follow one deployment design instead of becoming separate integration projects.
Swap or add a component when the robotics stack changes without replacing the entire environment.
Hyperlake specialists can help select components, validate the integration, and support rollout across customer sites.
Composable Physical AI environment
Choose simulation stack
Deploy into customer cloud
Connect governed data and models
Operate and repeat
Economic advantage: Measure the time from approved environment to first simulation, integration effort avoided on subsequent deployments, support hours, and GPU utilization. A packaged experience depends on the integrations validated for the customer; inference savings require serving and underlying compute to scale appropriately.
An enterprise or solution provider needs to put agents in production inside its own cloud, with governed data and tools. Hyperlake assembles the supporting services as a deployable environment; the team can bring its preferred agent framework, application, and models.
The turnkey starting point
Combine model serving, selected data engines, agent execution, secrets, observability, and lifecycle management in a reviewed deployment plan.
Apply OAuth/OIDC sign-in, validated JWT identity, network boundaries, and OPA policy checks at integrated data and tool access points.
Use supported open-source components or approved commercial services according to workload, licensing, and the customer's architecture.
Why this combination works
Teams can launch a governed environment without separately wiring serving, data, security, and operations for every application.
Replace or extend individual services as models, agent frameworks, and requirements evolve; maintain the customer's cloud boundary.
Hyperlake experts can help scope the blueprint, review safeguards, and operate or evolve the deployment with the customer.
From approved design to private deployment
Choose models and services
Review access and policies
Deploy in customer cloud
Run, observe, and extend
Economic advantage: Compare the engineering and security work needed to assemble the stack, time to production, operating effort, and model cost per task. Eligible inference can scale down when serving mode and compute allow.
Agents need more than raw database access. Deploy a governed data foundation and a reviewed semantic layer inside the customer's cloud, with the engines and knowledge structures the application actually needs.
The turnkey starting point
Assemble Iceberg and object storage with a fitting query engine, such as Trino, plus PostgreSQL, ClickHouse, search, vector, graph, streaming, or commercial services where appropriate.
Connect catalog and lineage signals; propose entities, relationships, and metrics from schemas and documentation for human review.
Use validated JWT identity and OPA checks at integrated access points so agents and applications see only approved data.
Why this combination works
Choose the right engine for each access pattern instead of forcing every query and application through one database.
Reuse approved business definitions across agents, analytics, and applications while keeping data and policy in the customer's environment.
Hyperlake specialists can help integrate sources, choose engines, and review the semantic and governance design.
Composable, governed data foundation
Connect sources
Choose data engines
Approve semantics and access
Serve context to AI
Economic advantage: Track duplicated data integration work, time spent rebuilding definitions, cost of inconsistent answers, and engine spend per workload. Generated semantics are proposals that need domain review; connectors and policies depend on the systems integrated.
Run an AI workforce with purpose, memory, and limits.
Bring a commercial or custom agent solution into a private operating environment, or work with experts to build one for a specific domain. Give agents distinct mandates, shared approved knowledge, checkpoints, and finite budgets so they can investigate over time without losing oversight.
The turnkey starting point
Combine durable agent execution, queues, state, knowledge services, open or commercial model access, evaluation, and observability.
Assign identity-scoped tools and data; add policy checks, audit trails, spending limits, and human approval before consequential action.
Integrate the agent framework and business workflow that fit the customer, then deploy the complete solution in its own cloud.
Why this combination works
Investigators, challengers, and reviewers can work together on a task while preserving evidence and decisions between runs.
Route routine work to evaluated smaller models and reserve expensive models for steps that benefit from them; pause or resume work as demand changes.
Hyperlake specialists can help architect the agent system, integrate the domain data, test model quality, and support its ongoing operation.
One operating pattern, many domain agents
Set mandate and budget
Choose agents and models
Investigate and cross-check
Approve, learn, and repeat
Economic advantage: Measure cost per verified result, analyst review time, and model/tool spend as agent actions multiply. Shared capacity and model choice can improve unit economics when utilization and quality justify them; infrastructure and support still cost money. Inspired by the multi-agent research pattern in Primus Society; Hyperlake enables the operating environment, not that research product.